ABSTRACT Title of Document: MAPPING QUANTITATIVE TRAIT LOCI FOR GRAIN YIELD AND YIELD RELATED TRAITS IN A HEXAPLOID WINTER WHEAT DOUBLED HAPLOID POPULATION Yaopeng Zhou, Doctor of Philosophy, 2015 Directed By: Dr. José M. Costa, Professor Emeritus Department of Plant Science and Landscape Architecture Improving wheat grain yield potential is imperative to match the increasing food demand associated with a fast growing population. Genetic and modeling approaches were employed to investigate the genetic basis and phenotype network regarding grain yield and yield related traits in a soft red winter wheat doubled haploid population. The population and two parents were evaluated in five year-location trials in the USA and genotyped by high throughput DNA markers including simple sequence repeat (SSR) and single nucleotide polymorphism (SNP). Bi-parental linkage mapping identified a number of QTLs for grain yield and yield related traits among which sixty were for grain yield components (GYLD, grain yield; SPSM, spikes per square meter; TGW, thousand grain weight; GPS, grains per spike; GWPS, grain weight per spike), seventy four were for plant architecture (PHT, plant height; FLL, flag leaf length; FLW, flag leaf width; FLA, flag leaf area; FLS, flag leaf shape or length/width ratio), and one hundred and nine were for spike morphology (SL, spike length; TSN, total spikelet number per spike; FSN, fertile spikelet number per spike; SSN, sterile spikelet number per spike; SC, spike compactness; GSP, grains per spikelet). In addition, structural equation modeling is described to construct a phenotype network. It revealed that GSP and FSN may mediate yield component compensation. Furthermore, doubled haploid lines DH96 and DH84 may have potential as new high-yielding cultivars for the Mid-Atlantic region. MAPPING QUANTITATIVE TRAIT LOCI FOR GRAIN YIELD AND YIELD RELATED TRAITS IN A HEXAPLOID WINTER WHEAT DOUBLED HAPLOID POPULATION By Yaopeng Zhou Dissertation submitted to the Faculty of the Graduate School of the University of Maryland, College Park, in partial fulfillment of the requirements for the degree of Doctor of Philosophy 2015 Advisory Committee: Professor Dr. José M. Costa, Chair Professor Dr. James N. Culver Professor Dr. Shunyuan Xiao Professor Dr. Zhongchi Liu Special Member Dr. Qijian Song © Copyright by Yaopeng Zhou 2015 ii Dedication I would like to dedicate this dissertation to my parents, Xueyi Zhou and Yuyun Hu, for their unconditional and endless love through my whole life and to my brother, Yaodi Zhou, for inspiring and supporting me along the way. iii Acknowledgements I would like to acknowledge my advisor, Dr. José M. Costa, for leading me down to the path of plant breeding and genetics and his continuing support throughout my study at the University of Maryland. Thanks also to my other committee members: Dr. Zhongchi Liu, Dr. James N. Culver, Dr. Shunyuan Xiao, and Dr. Qijian Song for their constructive suggestions, guidance, and encouragement. I would also like to thank the farm crew at the Wye Research & Education Center and the Central Maryland Research & Education Center for their aid in fungicide and fertilizer application. Particularly, I want to thank Aaron Cooper for his help from planting to harvesting. I would not have been able to complete my work without the support from my collaborators from USDA-ARS: Dr. Shiaoman Chao, Dr. Gina Brown-Guedira, and Dr. David Marshall who made great contributions in the genotyping and field evaluation and Dr. Paul Murphy from the North Carolina State University, who produced the doubled haploid mapping population. I also want to thank my lab mates, Ben Conway and Daniela Miller, for their invaluable discussions and the happy hours we spent together at the Looney’s Pub. My graduate school experience would be totally different without them. Thanks also to my friends at the Grad Office 2125: Siqi Chen, Justine Beaulieu, Sara Allard, Chioma Egekwu, and Laura Templeton who encouraged and listened to me during my hardest time. iv Participants contribution The project was initiated and guided by Dr. Jose Costa. The doubled haploid population was produced by Dr. Paul Murphy from North Carolina State University. 9K SNP array was conducted by Dr. Shiaoman Chao from USDA-ARS Small Grains Genotyping Lab at Fargo, ND. Dr. Gina Brown-Guedira from USDA-ARS Eastern Regional Small Grain Genotyping Lab at Raleigh, NC prepared the DNA library and sent it to USDA-ARS Central Small Grain Genotyping Lab at Manhattan, KS for genotyping-for-sequencing. All genotype calls were made by Dr. Gina Brown- Guedira. I performed maker quality assessment, constructed the linkage map, proposed and finished phenotype network modeling. I also performed the QTL mapping/analyses, phenotypic data analyses and wrote the manuscript. The manuscript consists of five independent prepublication chapters. v Table of Contents Dedication ..................................................................................................................... ii Acknowledgements ...................................................................................................... iii Participants contribution .............................................................................................. iv Table of Contents .......................................................................................................... v List of Tables ............................................................................................................. viii List of Figures ............................................................................................................. xii List of Abbreviations .................................................................................................. xv Chapter 1: Literature review ......................................................................................... 1 Introduction ............................................................................................................... 1 Increasing population and food supply ..................................................................... 1 Wheat evolution ........................................................................................................ 2 Brief overview of quantitative genetics .................................................................... 2 Phenotype classification........................................................................................ 2 Genetic and environment values ........................................................................... 3 Yield-related quantitative traits in wheat .................................................................. 4 Grain yield ............................................................................................................ 4 Plant architecture .................................................................................................. 6 Spike morphology ................................................................................................. 8 Cloned QTL/genes related to grain yield in cereal crops ......................................... 9 Grain related traits................................................................................................. 9 Plant architecture ................................................................................................ 12 Spike morphology ............................................................................................... 13 QTL mapping and cloning in breeding programs ................................................... 16 Basics of QTL Mapping...................................................................................... 16 Mapping population ............................................................................................ 18 Genetic markers .................................................................................................. 19 Genotyping by sequencing .................................................................................. 21 Statistical models for linkage mapping ............................................................... 22 Association mapping ........................................................................................... 23 Cloning QTLs in wheat ....................................................................................... 25 Trends in wheat breeding ........................................................................................ 27 High-throughput phenotyping ............................................................................. 27 Genomic selection ............................................................................................... 28 Synthetic wheat ................................................................................................... 30 Chapter 2: Quantitative trait loci mapping of grain yield in a doubled haploid population of soft red winter wheat ............................................................................ 31 Introduction ............................................................................................................. 32 Materials and Methods ............................................................................................ 36 Genetic resources ................................................................................................ 36 Field experiments ................................................................................................ 37 Phenotypic data collection .................................................................................. 38 Statistical analysis of traits.................................................................................. 38 Genotyping .......................................................................................................... 39 Map construction and QTL analysis ................................................................... 40 vi Results ..................................................................................................................... 41 Environment conditions ...................................................................................... 41 Phenotypic performance ..................................................................................... 42 Linkage map construction ................................................................................... 45 QTL with additive and additive × environment interaction effects .................... 48 QTL with epistatic and epistasis × environment interaction effects ................... 49 Discussion ............................................................................................................... 56 QTLs for grain yield ........................................................................................... 57 QTLs for yield components ................................................................................ 59 Pleiotropic effects of QTLs ................................................................................. 63 Q×E and QQ×E interactions ............................................................................... 64 Conclusion .............................................................................................................. 67 Chapter 3: Quantitative trait loci mapping of plant architecture traits in a doubled haploid population of soft red winter wheat ............................................................... 68 Abstract ................................................................................................................... 68 Introduction ............................................................................................................. 68 Material and Methods ............................................................................................. 72 Genetic resources and field experiments ............................................................ 72 Traits and measurements..................................................................................... 73 Data analysis ....................................................................................................... 73 QTL analysis ....................................................................................................... 74 Results ..................................................................................................................... 75 Phenotypic data analysis ..................................................................................... 75 QTLs with additive and additive × environment interaction effects .................. 79 QTLs with epistatic and epistatic × environment interaction effects ................. 79 Discussion ............................................................................................................... 87 QTLs for plant architecture traits ........................................................................ 87 Genetic complexity of plant architecture ............................................................ 92 Conclusion .............................................................................................................. 93 Chapter 4: Quantitative trait loci mapping of spike characteristics in a doubled haploid population of soft red winter wheat ............................................................... 95 Abstract ................................................................................................................... 95 Introduction ............................................................................................................. 96 Materials and Methods ............................................................................................ 98 Genetic resources and phenotypic traits evaluation ............................................ 98 Phenotypic data analysis ................................................................................... 100 QTL detection ................................................................................................... 101 Results ................................................................................................................... 101 Phenotypic analysis ........................................................................................... 101 QTL detection ................................................................................................... 105 Discussion ............................................................................................................. 117 QTLs for spike characteristics .......................................................................... 117 Genetic complexity of spike characteristics ..................................................... 122 Conclusion ............................................................................................................ 123 Chapter 5: Multivariate analysis of grain yield and yield related traits in a doubled haploid population of soft red winter wheat ............................................................. 125 vii Abstract ................................................................................................................. 125 Introduction ........................................................................................................... 125 Materials and Methods .......................................................................................... 129 Field trials and data collection .......................................................................... 129 Statistical analyses ............................................................................................ 129 Results and Discussion ......................................................................................... 129 Phenotypic and genetic correlation analyses .................................................... 129 Cluster and principal component analysis ........................................................ 130 Multiple linear regression analysis ................................................................... 133 Structure equation modeling (SEM) ................................................................. 133 Direct genetic evidence of feedback paths in SEM .......................................... 135 Conclusion ............................................................................................................ 136 Appendix A. .............................................................................................................. 155 Appendix B. .............................................................................................................. 156 Appendix C. .............................................................................................................. 158 Appendix D. .............................................................................................................. 159 Appendix E. .............................................................................................................. 161 Bibliography ............................................................................................................. 183 viii List of Tables Table 2.1 Growing season precipitation (cm) and average monthly temperature (°C) at five environments during 2013 and 2014………………………………………....42 Table 2.2 Phenotypic summary of grain yield (GYLD, g m-2), grains per spike (GPS), grain weight per spike (GWPS, g), spikes per square meter (SPSM), and thousand- grain-weight (TGW, g) evaluated in five environments during 2013 and 2014……..43 Table 2.3 Pearson correlation coefficients among grain yield (GYLD), grains per spike (GPS), grain weight per spike (GWPS), spikes per square meter (SPSM), and thousand-grain-weight (TGW) in five environments during 2013 and 2014………………………………………………………………………….……….44 Table 2.4 Pooled analyses of variance over five environments and heritability estimates for grain yield (GYLD), grains per spike (GPS), grain weight per spike (GWPS), spikes per square meter (SPSM), and thousand-grain-weight (TGW) in five environments during 2013 and 2014……………………………................................44 Table 2.5 Distribution of markers and length of linkage maps for twenty one wheat chromosomes………………………………………………………………………...45 Table 2.6 Quantitative trait loci (QTLs) for grain yield (GYLD), grains per spike (GPS), grain weight per spike (GWPS), spikes per square meter (SPSM), thousand grain weight (TGW) in five environments during 2013 and 2014………………......46 Table 2.7 QTL × Environment interactions influencing grain yield (GYLD), grains per spike (GPS), grain weight per spike (GWPS), spikes per square meter (SPSM), and thousand-grain-weight (TGW) in five environments during 2013 and 2014………………………………………………………………………………….54 Table 2.8 Chromosome locations of digenetic epistatic QTLs for grain yield (GYLD), grains per spike (GPS), grain weight per spike (GWPS), spikes per square meter (SPSM), and thousand-grain-weight (TGW) in five environments during 2013 and 2014……………………………………………………………………………...55 Table 3.1 Pooled analyses of variance and heritability estimates for plant height (PHT), flag leaf length (FLL), flag leaf width (FLW), flag leaf area (FLA), and flag leaf shape (FLS) in four field trials from 2013 to 2014…………………………...…78 Table 3.2 Pearson correlation coefficients among plant height (PHT, cm), flag leaf length (FLL, cm), flag leaf width (FLW, cm), flag leaf area (FLA, cm2), and flag leaf shape (FLS, cm) in six trials from 2012 to 2014…………..…………………..…….78 ix Table 3.3 Quantitative trait loci (QTLs) for plant height (PHT), Flag leaf length (FLL), Flag leaf width (FLW), Flag leaf area (FLA), Flag leaf shape (FLS) in four six environments from 2012 to 2013…………………….………………………………84 Table 3.4 QTL × Environment interactions influencing plant height (PHT), flag leaf length (FLL), flag leaf width (FLW), flag leaf area (FLA), and flag leaf shape (FLS) in four field environments during 2013 and 2014…………………………..….........85 Table 3.5 Digenetic epistatic QTLs for plant height (PHT), flag leaf length (FLL), flag leaf width (FLW), flag leaf area (FLA), and flag leaf shape (FLS) in four field trails during 2013 and 2014……………….…………………………………...…….86 Table 4.1 Phenotypic values for spike characteristics: spike length (SL, cm), sterile spikelet number per spike (SSN), fertile spikelet number per spike (FSN), total spikelet number per spike (TSN), spikelet compactness (SC), and grain number per spikelet (GSP) in MD01W233-06-1 × SS8641 doubled haploid population evaluated in five field trials from 2013 to 2014………………………………………….…....103 Table 4.2 Pooled analysis of variance and heritability estimates for spike characteristics: spike length (SL), sterile spikelet number per spike (SSN), fertile spikelet number per spike (FSN), total spikelet number per spike (TSN), spikelet compactness (SC), and grain number per spikelet (GSP) in the MD01W233-06-1 × SS8641 doubled haploid population evaluated in five field trials from 2013 to 2014…………………………………………………………………………….…..104 Table 4.3 Pearson correlation coefficients among spike characteristics: spike length (SL), sterile spikelet number per spike (SSN), fertile spikelet number per spike (FSN), total spikelet number per spike (TSN), spikelet compactness (SC) in the MD01W233-06-1 × SS8641 doubled haploid population evaluated in five field trials from 2013 to 2014…………………………………………………………………..104 Table 4.4 Quantitative trait loci (QTLs) for spike characteristics: spike length (SL), sterile spikelet number per spike (SSN), fertile spikelet number per spike (FSN), total spikelet number per spike (TSN), spikelet compactness (SC), and grain number per spikelet (GSP) in the MD01W233-06-1 × SS8641 doubled haploid population evaluated in five field trials from 2013 to 2014…………………………………….112 Table 4.5 QTL × Environment interactions influencing spike characteristics: spike length (SL), sterile spikelet number per spike (SSN), fertile spikelet number per spike (FSN), total spikelet number per spike (TSN), spikelet compactness (SC) and grain number per spikelet (GSP) in the MD01W233-06-1 × SS8641 doubled haploid population evaluated in five field trials from 2013 to 2014………………………..115 Table 4.6 Digenic epistatic QTLs for spike characteristics: spike length (SL), sterile spikelet number per spike (SSN), fertile spikelet number per spike (FSN), total spikelet number per spike (TSN), spikelet compactness (SC) and grain number per x spikelet (GSP) in the MD01W233-06-1 × SS8641 doubled haploid population evaluated in five field trials from 2013 to 2014…………………………………….116 Table 5.1 Genotypic (rg) and phenotypic (rp) correlation coefficients among grain yield and yield contributing traits in the MD01W233-06-1 × SS8641 doubled haploid population. rp is shown in the upper triangular and rg in the lower triangular. Traits evaluated include grain yield (GYLD), grains per spike (GPS), grain weight per spike (GWPS), spikes per square meter (SPSM), and thousand-grain-weight (TGW), plant height (PHT), flag leaf length (FLL), flag leaf width (FLW), flag leaf area (FLA), flag leaf shape (FLS), spike length (SL), sterile spikelet number per spike (SSN), fertile spikelet number per spike (FSN), total spikelet number per spike (TSN), spikelet compactness (SC), grain number per spikelet (GSP), and dates to heading (HD). rg and rp were estimated from all five trials’ data………………….137 Table 5.2 Multiple linear regression of the MD01W233-06-1 × SS8641 doubled haploid population. Grain yield (GYLD) as dependent variable and grains per spike (GPS), grain weight per spike (GWPS), spikes per square meter (SPSM), and thousand-grain-weight (TGW), plant height (PHT), flag leaf length (FLL), flag leaf width (FLW), flag leaf area (FLA), flag leaf shape (FLS), spike length (SL), sterile spikelet number per spike (SSN), fertile spikelet number per spike (FSN), spikelet compactness (SC), grain number per spikelet (GSP), and heading date (HD) as independent variables. Total spikelet number per spike (TSN) was not included in the analysis because of its multicollinearity with SSN and FSN. Estimates of regression coefficients and the associated p value are shown………. ………………………...138 Table 5.3 Stepwise multiple linear regression of the MD01W233-06-1 × SS8641 doubled haploid population. Grain yield (GYLD) as dependent variable and grains per spike (GPS), grain weight per spike (GWPS), spikes per square meter (SPSM), and thousand-grain-weight (TGW), plant height (PHT), flag leaf length (FLL), flag leaf width (FLW), flag leaf area (FLA), flag leaf shape (FLS), spike length (SL), sterile spikelet number per spike (SSN), fertile spikelet number per spike (FSN), spikelet compactness (SC), grain number per spikelet (GSP), and heading date (HD) as independent variables. Total spikelet number per spike (TSN) was not included in the analysis because of its multicollinearity with SSN and FSN. Variables kept in the final model, their regression coefficients, and the associated p values are shown……………………………………………………………………………….139 Table 5.4 Principal component analysis of the MD01W233-06-1 × SS8641 doubled haploid population based on sixteen agronomic traits including grain yield (GYLD, g m-2), grains per spike (GPS), grain weight per spike (GWPS), spikes per square meter (SPSM), and thousand-grain-weight (TGW), plant height (PHT), flag leaf length (FLL), flag leaf width (FLW), flag leaf area (FLA), flag leaf shape (FLS), spike length (SL), sterile spikelet number per spike (SSN), fertile spikelet number per spike (FSN), total spikelet number per spike (TSN), spikelet compactness (SC), and grain number per spikelet (GSP). Eigen values for each extracted principle component xi (PC), percentage (Per.) explained by each PC and cumulative percentage (Cum. Per.) are shown…………………………………………………………………………..140 Table 5.5 Principle component analysis of the MD01W233-06-1 × SS8641 doubled haploid population based on sixteen agronomic traits including grain yield (GYLD), grains per spike (GPS), grain weight per spike (GWPS), spikes per square meter (SPSM), and thousand-grain-weight (TGW), plant height (PHT), flag leaf length (FLL), flag leaf width (FLW), flag leaf area (FLA), flag leaf shape (FLS), spike length (SL), sterile spikelet number per spike (SSN), fertile spikelet number per spike (FSN), total spikelet number per spike (TSN), spikelet compactness (SC), grain number per spikelet (GSP), and heading date (HD). Eigenvectors of the first two principle components (PC) are shown……………………………………………...141 Table 5.6 Mean and standard error for five clusters based on seventeen yield related traits evaluated at Clarksville 2013 (E1), Clarksville 2014 (E2), Queenstown 2013 (E3), Queenstown 2014 (E4), Kinston 2014 (E5), and average of five environments..............................................................................................................149 Table A.1 Source of simple sequence repeats (SSRs) on the linkage map constructed in this study…………………………………………………………………………155 Table C.1 Summary of major and possible new QTLs identified in the present study. QTLs detected in multiple environments were indicated by asterisk……………....158 Table D.1 Cluster membership of 124 doubled haploid lines based on data from: Clarksville 2013 (E1), Clarksville 2014 (E2), Queenstown 2013 (E3), Queenstown 2014 (E4), Kinston 2014 (E5), and average of five environments………………....159 Table E.1 Phenotypic data for yield contributing traits evaluated at Clarksville 2013 (E1), Clarksville 2014 (E2), Queenstown 2013 (E3), Queenstown 2014 (E4), and Kinston 2014 (E5). Two replications at each environment. Missing data is indicated by dot……………………………………………………………………………….161 Table E.2 Average phenotypic data for yield contributing traits evaluated at greenhouse 2012 and 2013. Three replications in 2012 and four in 2013. Missing data is indicated by dot……………..……………..……………..……………..……….181 xii List of Figures Figure 2.1 Genetic linkage map and position of quantitative trait loci (QTLs) detected in a doubled haploid mapping population derived from MD01W233-06-1 × SS8641. Locus marker names are shown on the right side of the chromosomes and values to the left of chromosomes show the genetic distance (cM) for each marker. QTLs are labeled with trait abbreviations and the QTL number for each trait. QTLs for the same trait are in the same color………………………………………………54 Figure 2.2 Distribution of genetic and non-genetic components for grain yield and yield related traits: grain yield (GYLD), spikes m-2 (SPSM), grains per spike (GPS), grain weight per spike (GWPS), thousand-grain-weight (TGW). a) total number of QTLs detected for additive (a), additive × environment (ae), epistasis (aa), and epistasis × environment interactions (aae) effects. b) relative magnitude of a, ae, aa, and aae effects………………………………………………………………….....…55 Figure 3.1 Precipitation (unit: cm) and monthly average temperature (unit: °C) during growing season at four field environments: E1, Clarksville, 2013; E2, Clarksville, 2014; E3, Queenstown 2013; E4, Queenstown 2014………….….……76 Figure 3.2 Frequency distribution of plant height (PHT), flag leaf length (FLL), flag leaf width (FLW), flag leaf area (FLA), and flag leaf shape (FLS) of the double haploid lines in a) Clarksville 2013, b) Clarksville 2014, c) Queenstown 2013, d) Queenstown 2014, e) Greenhouse 2012, f) Greenhouse 2013……………………….77 Figure 3.3 Position of quantitative trait loci (QTLs) detected in a doubled haploid mapping population derived from MD01W233-06-1 × SS8641. Locus marker names are shown on the right side of the chromosomes and values to the left of chromosomes show the genetic distance (cM) for each marker. QTLs are labeled with trait abbreviations and the QTL number for each trait. QTLs for the same trait are in the same color………………………………………………………………………..83 Figure 3.4 Distribution of genetic and non-genetic components for yield and yield related traits: plant height (PHT), Flag leaf length (FLL), Flag leaf width (FLW), Flag leaf area (FLA), Flag leaf shape (FLS). a) total number of QTLs detected for additive (a), additive × environment (ae), epistasis (aa), and epistasis × environment interactions (aae) effects. b) relative magnitude of a, ae, aa, aae eff………………86 Figure 4.1 Position of quantitative trait loci (QTLs) detected in a doubled haploid mapping population derived from MD01W233-06-1 × SS8641. Locus marker names are shown on the right side of the chromosomes and values to the left of chromosomes show the genetic distance (cM) for each marker. QTLs are labeled with trait abbreviations and the QTL number for each trait. QTLs for the same trait are in the same color………………………………………………………………………111 xiii Figure 4.2 Distribution of genetic and non-genetic components for yield and yield related traits: spike length (SL, cm), sterile spikelet number per spike (SSN), fertile spikelet number per spike (FSN), total spikelet number per spike (TSN), spikelet compactness (SC), and grain number per spikelet (GSP). a) total number of QTLs detected for additive (a), additive × environment (ae), epistasis (aa), and epistasis × environment interactions (aae) effects. b) relative magnitude of a, ae, aa, aae effects……………………………………………………………………………….115 Figure 5.1 Principal component analysis: biplot summarizing the relationship among grain yield components, plant architecture, and spike morphology for the MDW233 × SS8641 doubled haploid population evaluated in five trials from 2013 to 2014. Traits are grain yield (GYLD), grains per spike (GPS), grain weight per spike (GWPS), spikes per square meter (SPSM), and thousand-grain-weight (TGW), plant height (PHT), flag leaf length (FLL), flag leaf width (FLW), flag leaf area (FLA), flag leaf shape (FLS), spike length (SL), sterile spikelet number per spike (SSN), fertile spikelet number per spike (FSN), total spikelet number per spike (TSN), spikelet compactness (SC), and grain number per spikelet (GSP). PHT, FLL, FLW, FLA, FLS were not evaluated at Kinston 2014…………………………………………….......142 Figure 5.21 Dendrogram of cluster analysis and scatter diagram of doubled-haploid lines for first two principle components at Clarksville 2013 (E1)………………….143 Figure 5.22 Dendrogram of cluster analysis and scatter diagram of doubled-haploid lines for first two principle components at Clarksville 2014 (E2)………………….144 Figure 5.23 Dendrogram of cluster analysis and scatter diagram of doubled-haploid lines for first two principle components at Queenstown 2013 (E3)………………..145 Figure 5.24 Dendrogram of cluster analysis and scatter diagram of doubled-haploid lines for first two principle components at Queenstown 2014 (E4)………………..146 Figure 5.25 Dendrogram of cluster analysis and scatter diagram of doubled-haploid lines for first two principle components at Kinston 2014 (E5)……………………..147 Figure 5.26 Dendrogram of cluster analysis and scatter diagram of doubled-haploid lines for first two principle components based on the average of E1, E2, E3, and E4…………………………………………………………………………………...148 Figure 5.3 Graphical representaion of the structural equation modeling for the phenotypic network based on data from a) Clarksville 2013, b) Clarksville 2014, c) Queenstown 2013, d) Queenstown 2014, f) Kinston 2014, and g) First four environments averaged. Red arrows indicate negative contribution. Green arrows indicate error covariance……………………………………………………………154 xiv Figure B.1 Heat map of the genetic linkage map based on recombination frequencies among 859 DNA markers. Markers are aligned along each chromosome from 1A to 7D………………………………………………………………………….………..156 Figure B.2 Genome-wide distribution of LOD score. Markers are aligned along each chromosome from 1A to 7D according to their order on each chromosome. Unit of X- axis: cM……………………………………………………………………………..157 xv List of Abbreviations FLA Flag leaf area, cm2 FLL Flag leaf length, cm FLS Flag leaf shape FLW Flag leaf width, cm FSN Fertile spikelet number per spike GPS Grains per spike GSP Grain number per spikelet GWPS Grain weight per spike, g GYLD Grain yield, g m-2 HD Heading date, days PHT Plant height, cm QTL Quantitative trait locus SC Spike compactness SL Spike length, cm SPSM Spikes per square meter SSN Sterile spikelet number per spike TGW Thousand-grain weight, g LOD Logarithm of the odds PVE Phenotypic variation explained, percentage SSR Simple sequence repeat SNP Single nucleotide polymorphism SEM Structural equation modeling 1 Chapter 1: Literature review Introduction Improving grain production is the key to ensuring food supply and social stability across the globe. In the last century, the great success of Green Revolution featuring high- yielding dwarf rice and wheat plants under heavy nitrogen fertilizers helped to feed an increasing population. However, it has come to a point where continuous yield increases driven by successful breeding management might be approaching a ceiling. This review aims to provide the most current developments in wheat grain yield improvement by examining the yield and yield contributing traits from a physiological and genetic perspective and by describing state-of-art technologies in current breeding practice. Increasing population and food supply According to the official estimates and projections from United Nations, the world population will reach 8.1 billion in 2025, and further increase to 9.6 billion in 2050 and 10.9 billion by 2100 (UN DESA, 2013). Meanwhile, global crop production needs to double by 2050 to meet the demands from the rising population, changing diets, and increasing biofuel consumption (FAO, 2012; Tilman et al., 2011). Yields of major crops like maize, rice, wheat, and soybean, however, are increasing at only 1.6%, 1.0% 0.9% and 1.3% per year, which is far slower than the 2.4% per year rate required to double global production by 2050 (Ray et al., 2013). Yield potential improvement is critical to meet this challenge. 2 Wheat evolution The allohexaploid bread wheat (Triticum aestivum L.; 2n = 6x = 42 chromosomes; genomic code AABBDD) is the product of two hybridization events involving three diploid (2x; 2n = 14) grass species within the tribe Triticeae: Triticum urartu (AA), an unknown close relative of Aegilops speltoides (BB), and Aegilops tauschii (DD) (Marcussen et al., 2014). The first hybridization is hypothesized to happen between the A and B genome donors 300,000–500,000 years ago, resulting in the wild tetraploid emmer wheat (Triticum turgidum; AABB) (Marcussen et al., 2014; Peng et al., 2011). About 10,000 years ago, emmer wheat cultivation began and expanded eastwards from the Fertile Crescent to the natural habitat of wild grass Aegilops tauschii during which cultivated emmer wheat hybridized with this D genome donor to form modern hexaploid bread wheat (AABBDD) (Peng et al., 2011; Shewry, 2009). Brief overview of quantitative genetics Phenotype classification Phenotype or the expression of a trait is an observable characteristic of an individual and varies between individuals. Phenotypes of organisms are classified into three different forms: qualitative, quantitative and threshold traits (Birnbaum, 1972). A qualitative trait is expressed discretely and individual phenotypes fall into discrete categories, as opposed to a quantitative trait, where phenotypes show a continuous range of values such as weight and height (Abiola et al., 2003). Threshold traits are dimorphic traits with polygenic bases but show a limited number of phenotypes such as molting in insects which is controlled by the levels of juvenile hormones (Moorad and Linksvayer, 2008; 3 Roff, 2008). In agricultural production, most agronomic traits of economic importance are quantitative traits. Genetic and environment values Quantitative traits have been extensively studied since the 1920s, after the establishment of quantitative genetics, which, in conjunction with statistics and Mendelian genetics, provided the scientific framework for modern plant breeding (Lamkey and Lee, 1993). Usually, quantitative traits show phenotypic variation among individuals and have a complicated genetic architecture, involving many genes throughout the genome with variable contributions to the overall phenotype (Holland, 2007). The genes controlling quantitative traits are affected by gene-by-gene and gene-by-environment interactions (Xu and Zhu, 2012). The environment, defined as the integrated influence of all nongenetic variables affecting phenotype, adds more complexity to quantitative traits (Xu and Zhu, 2012). One of the fundamental principles of quantitative genetics is that the phenotypic value P of an individual for a given trait can be considered as the sum of that individual’s genotypic value G plus the environmental value E, thus, in linear format: P=G+E, where G can be divided into additive (A), dominant (D) and epistatic (I) values (Walsh, 2001). To better account for quantitative traits, especially in breeding, the additive model needs to be extended to include G× E, which is known as genotype-by-environment interaction (Eeuwijk, 2008). In natural populations, the variation of a quantitative trait often approximates a statistical normal distribution as it is the sum of small effects caused by genes and the environment (Xu, 2010). Most important agronomic traits that constitute 4 the primary focus of plant breeding such as grain yield are quantitative in nature, usually referred to as complex traits, with variation believed to be attributable to dozens if not hundreds of underlying genes (Crosbie et al., 2008). A region of the genome containing one or more genes that affect variation in a quantitative trait is known as quantitative trait locus (QTL). QTL identification and diagnostic marker development for desired traits are crucial so that modern breeders can deliver superior new cultivars with efficiency and accuracy. Yield-related quantitative traits in wheat Grain yield Improving the grain yield potential of wheat has been the principal aim of wheat breeding programs worldwide and has helped to maintain the viability of agricultural systems in both developed and developing countries (Kuchel et al., 2007b). Although, genetic improvement in yield potential, resistance to diseases, and adaptation to abiotic stresses have contributed to the increases of grain production in the last three decades, it is widely accepted that the rates of progress and genetic gains from wheat breeding have slowed down and even decreased (Reynolds et al., 2012; Reynolds et al., 2009). Part of the reason is due to the lack of sufficient knowledge about the mechanisms, complex biological pathways, and their corresponding genetic basis underlying the responses of wheat in specific environments (Henry and Prasanta, 2004). In recent years, the rapid advances in biotechnology and molecular biology, as well as research on model organisms, have provided powerful tools and references for crop genetic improvement. 5 Our current understanding of grain yield and its genetic constraints can be expressed by the following three perspectives: 1) The classical view: Grain yield= Spikes/m2 × Grains/spike × Grain Weight; 2) The carbon-economy-based view: Grain yield= Light intercepted (LI) × Radiation use efficiency (RUE) × Harvest index (HI); 3) The water-use-based view: Grain yield= W × Water use efficiency (WUE) × Harvest Index (HI); where RUE is the overall photosynthetic efficiency of the crop; W is the water transpired by the crop plus direct evaporation from the soil; WUE is the ability of the crop to produce biomass per unit of water evapotranspired (Matthew et al., 2004). A new strategy to boost wheat productivity through genetic intervention has been proposed, combining these three views. It features higher photosynthetic capacity, improved partitioning of assimilates and genetic tools to improve breeding efficiency (Reynolds et al., 2012). Grain yield is the end product of the interaction of a large number of physiological and biochemical processes, genetically complex, and determined concurrently by multiple plant characteristics (Marza et al., 2006; Sharma et al., 2003). The conventional method to explore complex traits is to deconstruct them into simpler components for further exploration and characterization. In the case of wheat grain yield, these include grains per spike, spikes per unit area and grain weight (1000-grain-weight) (Mengistu et al., 2012). Breeding efforts focused on partitioning more assimilates to reproductive development 6 and less to vegetative dry matter production have resulted in modern wheat cultivars with more grains per spike and more grains per square meter (Frederick and Bauer, 1999). Wheat genetics studies have located QTLs for grain yield and yield components on all 21 chromosomes of bread wheat (Bennett et al., 2012a; Heidari et al., 2011; Wu et al., 2012). However, the quantitative nature of QTLs and their strong interaction with the environment make constant and stable QTL detection difficult and their applicability limited to a very specific environment even though a number of them are major QTLs accounting for more than 10% of the phenotypic variation, as was verified by Heidari et al. (2011). Furthermore, the unstable correlation between grain yield and yield components reported from separate studies indicates its underlying complexity (Bennett et al., 2012a; Heidari et al., 2011; Mengistu et al., 2012). Availability of large sets of phenotypic data, genomic data from SNP arrays and new QTL mapping methods will help to detect more QTLs and elucidate the relationships between grain yield and its related traits with more precision. Plant architecture Canopy architecture of higher plants is defined by the degree of branching, internodal elongation, and shoot determinacy or, simply, as the spatial configuration of the aboveground plant organs (Fageria et al., 2006; Wang and Li, 2008). Some of the detailed characteristics of plant architecture involve plant height, tillering, branching patterns, leaf size and shape, configuration of leaf relative to the sun and spatial arrangement of leaves (Fageria et al., 2006). Plant architecture has been a focus of research because of its close association with photosynthesis and grain yield (Hedden, 2003). 7 Plant height, mainly determined by stem elongation, is an important agronomic trait in cereal crops influencing plant architecture and contributing to grain yield (Wang and Li, 2008; Wang et al., 2010). In high soil fertility conditions, the stems of tall cultivars are unable to support the resultant weight of plump grains. High-yielding cultivars fall over in the field before maturity, a process known as lodging, with consequent large yield losses (Hedden, 2003). Introduction of dwarfing genes into cereal crops, such as Rht-B1b and Rht-D1b in wheat and sd1 in rice, produced semi-dwarf plants with short strong stalks as well as more assimilate partitioned into the grain leading to the great increases of wheat and rice yield, known as the Green Revolution (Hedden, 2003). Since then, the semi-dwarf phenotype has been extensively selected as the ideal trait for high-yielding cultivars in modern crop breeding programs. However, extremely short plants are disadvantageous because leaves are very closely spaced on a short stem causing increased shading within the canopy, as well as poor ventilation and light transmission in the lower canopy, which affects seed-filling and ultimately decreases yield (Yoshida, 1972; Zhang et al., 2011). Thus, breeding a cultivar with optimum plant height for a target environment is necessary. As with grain yield, QTLs for plant height have been mapped on all 21 chromosomes of wheat (Wu et al., 2010). Leaves are responsible for photosynthesis that provides photosynthetic products in plants. The flag leaf is the last leaf to emerge before the spike and plays a dominant role in determining grain yield (Yoshida, 1972). Translocation of carbohydrates from the flag leaf is almost entirely directed towards the grain while that from the second and third leaves is only partly directed towards the grain which underscores the important influence 8 of shape and size of flag leaves on yield performance (Monyo and Whittington, 1973). Both Dere and YIildirim (2006) and Monyo and Whittington (1973) found a positive correlation between the flag leaf length and width with grain yield in bread wheat. Among the few QTL studies on leaf morphology in wheat, Jia et al. (2013a) reported a major QTL explaining 28.7-35.6% of the phenotypic variation of flag leaf width and that the Wangshuibai allele reduced flag leaf width up to 3 mm. This QTL was inherited as a semi-dominant gene, designated as TaFLW1, and was fine-mapped to a 0.2 cM interval on chromosome 5A (Xue et al., 2013). Spike morphology The morphology of wheat spike is characterized by its spike length, fertile spikelet number per spike, sterile spikelet number per spike, and spikelet compactness. The wheat spike harbors spikelets where florets develop and produce grains. Spike morphology is relevant to grain yield because it determines the number of grains. Since the 1960s, genetic gains in grain yield of wheat have generally been achieved by improvements in grain number per spike and spikes per square meter, with little change in individual grain weight (Gaju et al., 2009). Thus, increasing grain number per se by modifying the spike morphology may open new opportunities for higher grain yield potential. In bread wheat, Q, C, and S1 are the three major domestication genes affect the gross morphology of the spike. The Q gene is located on chromosome arm 5AL and pleiotropically influences not only spike length and shape, but also other domestication related traits including seed threshability, glume keeledness, rachis fragility, plant height, and spike emergence time (Faris et al., 2014). The C gene is located on chromosome 2D 9 and defines a subspecies of hexaploid wheat known as T. aestivum ssp. compactum (Host) MacKey, or club wheat, which has a characteristic compact spike due to a dominant C allele (Faris et al., 2014). The S1 gene on chromosome 3D defines another subspecies known as T. aestivum ssp. sphaerococcum (Percival) MacKey, or shot wheat, which is characterized by having round seed, round glumes, and a short dense spike (Faris et al., 2014; McIntosh et al., 2013). Therefore, common wheat (ssp. aestivum) has the genotype QcS1, club wheat (ssp. compactum) is QCS1, and shot wheat (ssp. sphaerococcum) is Qcs1 (McIntosh et al., 2013). In addition to these loci, all twenty one wheat chromosomes have been associated with spike related traits (Borner et al., 2002; Cui et al., 2012; Deng et al., 2011; Kumar et al., 2007; Ma et al., 2007b; Marza et al., 2006; Wang et al., 2011). Furthermore, mapping QTL as Mendelian factors was first reported by Deng et al. (2011) who investigated wheat spike traits in a F2 population. This population showed a clear 3:1 segregation ratio for spike number per plant, spike length, and grain number per spike. The underlying QTL was mapped to the chromosome 4B and explained 30.1 to 67.6% of the phenotypic variation across environments. Further fine mapping and molecular characterization of this region has not been reported yet. Cloned QTL/genes related to grain yield in cereal crops Grain related traits Grain morphology and grain filling rate determine grain weight and thus, grain yield. The first cloned major QTL related to grain morphology was GS3 which explained 80-90% of the variation for grain weight and length in a rice BC3F2 population derived from a cross between Minghui 63 (large grain) and Chuan 7 (small grain) (Fan et al., 2006). Initial QTL analysis mapped GS3 on chromosome 3. Fine mapping narrowed down the 10 candidate region to a DNA fragment of 7.9 kb in length where a full-length cDNA was identified. GS3 encodes a transmembrane protein consisting of a putative PEBP-like domain, a putative TNFR/NGFR family cysteine-rich domain and a VWFC module. A C to A mutation in its second exon changed a cysteine codon (TGC) in Chuan 7 to a termination codon (TGA) in Minghui 63 which yields a premature termination and a 178- aa truncation in the C-terminus. Overexpression of GS3 not only produces short grains but also results in reduced plant size, including shortened height, leaves, and panicles, suggesting its role as a negative regulator with pleiotropic effects (Mao et al., 2010). GW2 was the second cloned major QTL for grain size in rice (Song et al., 2007). This QTL was identified and molecularly characterized from a F2 population derived from a japonica × indica cross (WY3 × Fengaizhan-1). GW2 encodes a RING protein with E3 ubiquitin ligase activity. Compared with the Fengaizhan-1 allele at locus GW2, the WY3 allele has a 1-bp deletion in exon 4, resulting in a premature stop codon which leads to truncation of 310 amino acid residues. This loss-of-function mutation produces substantially more and longer cells in outer parenchyma cell layer of the spikelet as well as larger endosperm cells and accelerated grain filling. Additionally, the WY3 allele increases grain size and yield with little influence on eating or cooking quality making it a useful QTL in breeding. GS5, the first cloned positive regulator, controls grain size by regulating grain width, filling and weight in rice (Li et al., 2011). Primary QTL mapping detected this QTL on the short arm of chromosome 5 in a doubled haploid (DH) population derived from the 11 cross between Zhenshan 97 (large grain size) and H94 (small grain size). Fine mapping resolved GS5 to a candidate region of 11.6-kb in length where there was only one predicted open reading frame (ORF). This ORF has ten exons and encodes a putative serine carboxypeptidase. GS5 positively regulates grain size by increasing cell number in the inner parenchyma cell layer and the cell size of palea and GS5 is further shown to be a positive modulator upstream of cell cycle genes whose expression is significantly elevated when GS5 is overexpressed. The observed grain size and yield difference between Zhengshan 97 (large grain size) and H94 (small grain size) was due to the polymorphisms in the GS5 promoter region where strong/weak promoters were associated with high/low yield. The Zhengshan 97 allele was expressed with more abundance in the palea/lemma at 2, 4 and 5 day before heading and in the endosperm at 10 days after fertilization which corresponded well with critical stages for grain width and grain filling. GRAIN INCOMPLETE FILLING 1 (GIF1) was the first cloned and functionally analyzed QTL for grain-filling (Wang et al., 2008). GIF1 is located on chromosome 4 and encodes a cell-wall invertase required for carbon partitioning during early grain-filling. Specifically, GIF1 unloads sucrose in the ovular and stylar vascular tissues for starch synthesis in the endosperm during grain-filling. The gif1 mutant has a 1-nt deletion in the coding region which results in a premature stop and reduced grain weight. During grain filling, the wild-type GIF1 allele is expressed in the ovular vascular trace, pericarp and endosperm tissues. In contrast, the cultivated GIF1 allele is mainly confined to the ovular trace which leads to a higher accumulation level of glucose, fructose and sucrose and, 12 hence, increased grain weight. This restricted expression pattern of cultivated GIF1 gene is caused by accumulated mutations in its promoter region during rice domestication both in japonica and indica. Plant architecture The generalization of dwarfing genes in wheat and rice cultivars was crucial to the success of the Green Revolution. Reduced height-B1b (Rht-B1b) and Reduced height- D1b (Rht-D1b) are known as the green revolution genes in wheat (Peng et al., 1999). Their wild-type alleles Rht-B1a and Rht-D1a encode DELLA proteins which are transcriptional regulators that repress gibberellin (GA)-responsive growth. Rht-B1b and Rht-D1b both contain single nucleotide substitutions causing premature stop codons in the N-terminal coding region leading to truncated proteins with increased repression of GA signal transduction. semidwarf-1 (sd-1) is known as the green revolution gene in rice and encodes gibberellin 20-oxidase (GA20ox) which is an enzyme catalyzing three intermediate steps of reactions converting GA precursors to GA (GA53 → GA44→ GA19→ GA20) (Monna et al., 2002; Sasaki et al., 2002). Its widely-used allele is from the Chinese cultivar, Dee-geo-woo-gen that contains a 383-bp deletion in the GA20ox gene (known as OsGA20ox2) resulting in a premature stop codon and a highly truncated inactive enzyme (Hedden, 2003). MONOCULM 1 (MOC1) is the first cloned key regulator of rice tiller number (Li et al., 2003). A screen of mutants with altered tiller numbers identified a rice plant with only one main culm, named monoculm 1 (moc1). moc1 is caused by a recessive mutation at a single locus and was mapped to the chromosome 6 of rice. MOC1 encodes a plant- 13 specific GRAS family nuclear protein and is mainly expressed in the axillary buds to initiate axillary buds and to promote their outgrowth. Rice plants with enhanced expression of MOC1 produced more tillers as expected. In contrast, moc1 is not able to initiate axillary meristem and therefore, has only one main culm. Rice Narrow leaf 1(Nal1) encodes a plant specific protein preferentially expressed in vascular tissues with rich abundance (Qi et al., 2008). A 30-bp deletion in its coding region is significantly associated with reduced polar auxin transport capacity and affects the distribution pattern of vascular bundles leading to narrower leaves with fewer longitudinal veins. NARROW AND ROLLED LEAF 1 (NRL1) was mapped to chromosome 12 in rice. It encodes the cellulose synthase-like protein D4 (OsCsID4) which plays a crucial role in leaf expansion in rice (Hu et al., 2010). Its three mutants (single base substitutions at three different loci) nrl1-1, nrl1-2, and nrl1-2 are smaller and show erect, narrow and semi-rolled leaves compared to the NRL1 genotype. Spike morphology Grain number per panicle is one of the most important yield components in cereals. The first cloned QTL for grain number per panicle was Gn1a in rice (Ashikari et al., 2005). This QTL was mapped by using 96 backcross inbred lines derived from the cross between Habtaki (higher grain number) and Koshihikari (lower grain number). A major QTL contributed by Habataki explained 44% of the grain number variation and was identified on chromosome 1. This QTL was further fine-mapped to a region of 6.3 kb, where there was only one predicted open reading frame. Molecular characterization of Gn1a showed that it encodes a cytokinin oxidase/dehydrogenase (OsCKX2) whose 14 reduced expression causes cytokinin accumulation in the inflorescence meristem and increases the number of reproductive organs which leads to higher grain number per panicle enhancing grain yield. Based on comparative genomics, TaCKX6-D1 a wheat ortholog of the rice OsCKX, was cloned and located on the wheat chromosome 3D (Zhang et al., 2012). This gene was mapped by using a set of 199 RILs derived from a cross between two Chinese Spring cultivars Yanzhan1 and Neixiang188. The Yanzhan1 allele, named TaCKX6-D1a, has an 18-bp indel in its second intron where the Neixiang188 allele, named TaCKX6-D1b, has an insertion in this region. TaCKX6-D1a is associated with higher 1000-grain weight and its additive effect is 1.3~1.4 g per 1000 grains. Four more alleles of TaCKX6-D1 were found and named TaCKX6- D1c─TaCKX6-D1e. Evolutionary analysis showed that alleles c, e and d are ancient haplotypes occurring only in the wild species, whereas alleles a and b are newly derived, present most commonly in both modern cultivars and landraces. Quantitative trait loci WFP (WEALTHY FARMER’S PANICLE) and IPA1 (IDEAL PLANT ARCHITECTURE) were cloned in the same year by two research groups independently and were found to share the same underlying gene OsSPL14 (Jiao et al., 2010; Miura et al., 2010). OsSPL14 is located on the chromosome 8 of rice and encodes a plant-specific transcription factor SQUAMOSA PROMOTER BINDING PROTEIN- LIKE (OsSPL14) which is conserved in sorghum, wheat, maize and Arabidopsis thalinana (Miura et al., 2010). Higher expression of OsSPL14 promotes panicle branching in the reproductive stage. Sequence analysis showed that OsSPL14 contains a microRNA-targeted sequence in the third exon (targeted by OsmiR156). OsmiR156 is 15 highly expressed in the vegetative stage and cleaves the OsSPL14 mRNA suppressing the expression of OsSPL14. In the reproductive stage, OsmiR156 disappears leading to a higher expression level of OsSPL14 and the subsequent enhanced primary branching on the panicle. DEP1 is the first cloned QTL that acts through the determination of panicle architecture (Huang et al., 2009). DEP1 is located on chromosome 9 of rice and encodes a phosphatidylethanolamine-binding protein-like domain protein. Its dominant loss-of- function allele dep1 from cultivars such as Nanjing 11 and Nipponbare produces erect panicles with a shorter inflorescence internode, increased number of both primary and secondary panicle branches and increased number of grains. Although the 1000-grain weight of NIL-dep1 was slightly lower than that of NIL-DEP1 plants, the overall grain yield of NIL-dep1 was 40.9% higher. Moreover, the downregulation of TaDEP1, a homolog of DEP1 in wheat, showed a longer panicle with fewer spikelets suggesting that this locus and its homologs in other small grain cereals may provide an option for increasing grain yield. Ghd7 is the first cloned quantitative trait locus that controls grain numbers per panicle, plant height and heading date simultaneously (Xue et al., 2008b). Ghd7 was mapped on chromosome 7 of rice using both F2:3 and RIL populations derived from a cross between Zhenshan 97 (lower grain number and days to heading) and Minghui 63 (higher grain number and days to heading). Ghd7 encodes a CCT (CO, CO-LIKE and TOC1) domain protein. However, comparison with other CCT domain-containing proteins showed that t 16 GHD7 is distinct from all other members of the CCT domain protein family and is considered to be an evolutionary new gene in the lineage. Ghd7 is a key upstream transcription factor in the photoperiod flowering pathway and its Minghui 63 allele allow rice plants to fully utilize light and temperature by delaying flowering under long-day conditions. As a result, larger panicles with more grain numbers occur. Furthermore, the Minghui 63 allele is mainly expressed in young tissues and also has a positive effect on stem growth by producing more nodes, a longer upper-most internode and thicker stems with improved lodging resistance. All these pleiotropic effects contribute to a high grain yield. Ghd8 is a major QTL on chromosome 8 with similar pleiotropic effects as Ghd7 (Yan et al., 2011). Ghd8 encodes the OsHAP3 subunit of the HAP complex and acts upstream of rice florigen genes Fhd1, Hd3a, and RFT1. In addition, Ghd8 has a positive effect on rice tiller number, primary and secondary branches, by up-regulating MOC1 which is a key gene controlling tillering and branching. QTL mapping and cloning in breeding programs Basics of QTL Mapping Historically, genetics relied entirely on phenotypic information to determine the relative importance of genetic versus environmental factors through techniques such as analysis of variance and heredity analysis (Walsh, 2001). However, merely based on phenotypic evaluation, it is generally not possible to identify relevant loci influencing a trait. The development and combination of genetic marker technologies, molecular biology and biometric methods has made QTL mapping possible in complex traits studies. 17 QTL mapping is a set of statistical methods attempting to explore the relationship between DNA sequence variation and natural phenotypic variation for quantitative or complex traits and is widely utilized in modern genetics (Haley, 2002; Kearsey and Farquhar, 1998 ; Majumder and Ghosh, 2005; Myles et al., 2009). By combining phenotypic data (trait measurements) and genotypic data (usually molecular markers), QTL mapping allows researchers to link certain complex phenotypes to specific regions of chromosomes (Miles and Wayne, 2008). Although the principles of QTL mapping have been known since the early twentieth century, genetic dissection of complex traits was limited to a few model organisms due to the lack of polymorphic markers (Mackay et al., 2009). Since the discovery of abundant molecular markers in late 1980s, advances in rapid and cost-effective genotyping methods and the employment of statistical methods have revolutionized the field of QTL mapping (Mackay et al., 2009). Statistical methods developed for QTL mapping are based on homologous recombination at meiosis, during which the genetic material is exchanged by crossing over (Myles et al., 2009; Nordborg and Weigel, 2008). To perform QTL mapping for a measurable quantitative trait, a mapping population and linkage map are needed. Coupling this map with phenotypic data for the trait (e.g. yield) allows the region of the genome associated with the phenotype to be identified. Therefore, the three requirements for QTL mapping are: 1) a mapping population where individuals differ genetically with regard to traits of interest; 2) genetic markers that distinguish these lines; and 3) quantitative data for the traits to be explored; (Miles and Wayne 2008). 18 Mapping population In plant breeding, the most common mapping populations include F2, recombinant inbred lines (RILs), and doubled haploids (DH). The simplest form of a mapping population is a collection of F2 plants derived directly by selfing a F1 plant. In this case, the expected segregation ratio for each codominant marker is 1:2:1 (homozygous like P1:heterozygous:homozygous like P2) (Schneider, 2005). However, an F2 populations can only be used once since they are not immortal and generally cannot be clonally propagated (Schneider, 2005). This makes phenotypic evaluation in multi-location/year difficult to perform. Recombinant inbred lines (RILs) are generated by repeated selfing of F2 individuals for at least six generations using the single seed descent method (Snape and Riggs, 1975). Once established, RILs can be propagated eternally and shared by other groups in the research community (Broman, 2005). A second advantage of RILs is that after several rounds of meiosis before homozygosity is reached, the degree of recombination and the resolution of the linkage map are both higher compared to that of F2 populations and the map positions of even tightly linked markers can be determined (Schneider, 2005). Despite the fact that RILs are among the most effective population designs, it is time consuming to construct homozygous RIL populations, typically requiring at least six generations of self-fertilization starting from a heterozygous F1 (Seymour et al., 2012). Another option for mapping is to develop a doubled haploid (DH) population. Haploid gametes produced from F1 meiosis contain all recombination information but only half 19 the number of chromosomes. To make a DH population in plants, F1 flowers are pollinated with incompatible pollen, leaving a haploid embryo. After embryo rescue and tissue culture, haploid seedlings are treated with colchicine, preventing cytokinesis after mitosis and leading to doubled haploids (Schneider, 2005). Each DH contains two identical sets of chromosomes in each cell and is completely homozygous at every locus. This time-efficient process can be finished in only two steps and has been widely used in QTL mapping in a variety of species, especially in grasses (Seymour et al., 2012). Genetic markers Genetic markers are heritable biological features that are determined by allelic forms of genes or genetic loci and can be measured in one or more populations (Davey et al., 2011; Xu, 2010). Thus, they can be used as experimental probes or tags to keep track of an individual, a tissue, a cell, a nucleus, a chromosome or a gene and are the cornerstone of modern genetics (Davey et al., 2011; Xu, 2010). As Xu (2010) summarized, genetic markers fall into two categories: 1) classical markers and 2) DNA markers. Classical markers include morphological markers, cytological markers and biochemical markers. DNA markers include randomly amplified polymorphic DNA (RAPD), simple sequence repeats (SSR) or microsatellites, amplified fragment length polymorphisms (AFLP), single nucleotide polymorphisms (SNP), and diversity arrays technology markers (DarT). After the first identification and use of DNA-based molecular markers in 1980s, such as restriction fragment length polymorphism (RFLP), the development and use of molecular markers has increased explosively in human genetics, plant breeding and genetics, animal breeding and genetics, and germplasm characterization and management (Botstein et al., 20 1980; Jiang, 2013). This technological revolution began with low-throughput RFLP and culminated with SNPs in recent years (Gupta et al., 2008). First identified in the human genome, SNPs make up about 90% of all human genetic variation, happen every 100–300 bases, and have been proven to be universal in plant and animal systems as well (Wang, 1998; Xu, 2010). SNP identification is usually achieved by aligning genomic or expressed sequence tag (EST) sequences available in databases, or via next-generation sequencing (NGS)-based sequencing or resequencing of candidate genes/ PCR products and even whole genomes in more than one genotype (Gupta et al., 2008). Once discovered, many platforms are available to carry out SNP genotyping, such as Genechip, Infinium II and Goldengate (Kumar et al., 2012). In crop plants, abundant and high-density SNPs can accelerate high-density genetic mapping and identification of genes/QTLs for traits of economic and agronomic importance as well as the application of marker-assisted breeding and genomic selection (Trebbi et al., 2011). Recently, SNP discovery has been reported in many crop plants such as rice, maize, barley, wheat, and sunflower (Bachlava et al., 2012; Cavanagh et al., 2013; Close et al., 2009; Ganal et al., 2011; Hu et al., 2013; McCouch et al., 2010). For example, Trebbi et al. (2011) discovered and validated a set of 275 SNPs in durum wheat using 12 durum cultivars through complexity reduction of polymorphic sequences (CroPS) technology and Illumina Golden Gate technology. Ganal et al. (2011) developed a large maize SNP array containing 57,838 markers across the genome, out of which 49,585 markers, representing 17,520 genes were storable and of good quality for further 21 genotyping. Additionally, using the RICE6K SNP array, Hu et al. (2013) mapped 5 novel QTLs for rice grain shape for marker-assisted selection in rice. Genotyping by sequencing The decreasing cost of next-generation sequencing (NGS) makes high-throughput genome-wide genetic marker discovery applicable not only to model organisms with reference genome sequences but also to non-model species without genome data (Davey et al., 2011). Recently, genotyping by sequencing (GBS), a low coverage genotyping method suitable for high diversity and large genome species, was proposed. It is reported to be “simple, quick, extremely specific, highly reproducible, and may reach important regions of the genome that are inaccessible to sequence capture approaches” (Elshire et al., 2011). Compared with restriction-site-associated DNA sequencing (RAD-seq), GBS has simpler library preparation protocols but produces equivalent results at a very low cost per sample (Davey et al., 2011). After the digestion of genomic DNA with restriction enzymes, barcode and common adapters are ligated to sticky ends of digested DNA fragments after which samples can directly go to PCR amplification followed by DNA sequencing (Elshire et al., 2011). Since no fragment size selection and few enzymatic and purification steps are involved, this protocol is time and cost efficient (Elshire et al., 2011). In maize, for example, 200,000 markers were identified and mapped in a very short time at a cost of $8,000 (Elshire et al., 2011). During this study, GBS was coupled with multiplex technology and simultaneously processed up to 2,688 samples per sequencing run (384 samples per channel ×7 channels) (Elshire et al., 2011). 22 Statistical models for linkage mapping Traditionally, QTL detection is achieved by linkage mapping, where two homozygous inbred parental lines are crossed to create a mapping population/family and attempts are made to identify cosegregation of genetic markers and phenotypes within this family (Myles et al., 2009). In the late 1980s, markers and advances in genotyping technology led to the development of statistical methods for use in QTL mapping of complex quantitative traits. A landmark method for QTL mapping is interval mapping (IM) (Lander and Botstein, 1989).This method established a statistical framework for most methods that are currently used to analyze QTLs of complex traits (Xu and Zhu, 2012). In IM, phenotypic data is used to compute a log likelihood (LOD) value at a DNA marker interval. As the marker interval slides along the chromosome (genome scanning), LOD values change accordingly. A QTL associated with a quantitative trait is assumed to be located on the genome under the peak where the LOD is higher than a specified threshold. The precision of IM was improved by including associated markers as covariant variables (Zeng, 1994). This method is known as composite interval mapping (CIM). Under the assumption of no QTL× environment interaction, CIM can produce unbiased estimations of QTL positions and effects. The IM and CIM methods have been widely applied in experimental populations, such as F2, recombinant inbred lines (RIL), and doubled haploids (DH) (Xu and Zhu, 2012). Other well-recognized mapping models include multiple interval mapping (MIM) (Kao et al., 1999), inclusive composite interval mapping (ICIM) (Li et 23 al., 2007a), conditional QTL mapping (Wen and Zhu, 2005; Zhu, 1995), and QTL mapping based on mixed linear model (Wang et al., 1999; Yang et al., 2007). Linkage mapping, however, has its own drawbacks. It is based on a highly controlled population structure that goes through relatively few meiosis events. Therefore, recombination has not had sufficient time to shuffle and rearrange the genome and QTLs may end up in large chromosomal regions making it difficult to capture the precise location of promising QTLs and to distinguish pleiotropic effects of a single QTL from multiple independent linked QTLs (Xu, 2010). The resulting low precision can be partially improved by using a larger mapping population with more recombination events and a high-density marker coverage across the genome. Lastly, due to this rigid population structure, QTLs identified in linkage mapping populations are usually limited to specific crosses and may not be generalized to other populations. Association mapping In association mapping, genotype data and phenotype data are collected from a natural population (assuming random mating) where the experimenter has no control over the structure of the mapping population (Myles et al., 2009). This advantage leads to its enormous success in human disease research, for which obtaining a controlled population is almost impossible (Collins, 2007). Association mapping employs historical recombination events that have happened between QTLs and marker alleles providing higher mapping resolution and thus requires a smaller number of individuals compared with linkage mapping (Mackay et al., 2009). The application of association mapping expanded enormously with the advent of next generation sequencing technology which 24 has the capacity of discovering, sequencing and genotyping large numbers of molecular markers, mostly SNP, across almost any genome of interest in a short time and in a cost- effective manner (Davey et al., 2011). Having the whole genome covered with molecular markers enables researchers to conduct genome-wide association studies with revolutionary resolution. A randomly mating population, however, almost does not exist in practice and this nonrandom mating population structure can generate complex patterns of population structure and relatedness in plants which is a strong confounding factor, especially for the traits that are to be introgressed into local cultivars (Myles et al., 2009; Nordborg and Weigel, 2008). Despite the fact that statistical methods have been developed to correct for various types of relatedness, one should recognize that these methods are still subject to further improvement (Myles et al., 2009). In addition, association mapping cannot detect alleles with low frequency in the population, even if they have a large effect on the phenotype (Davey et al., 2011). However, population genetics suggests that, in the majority of species, most alleles are rare, which makes it difficult to explain phenotypic variation via association mapping. (Myles et al., 2009). Thus, biparental mapping is still an important tool. The power of association mapping highly depends on the strength of the association between molecular markers and the corresponding functional variants/QTLs, which is known as linkage disequilibrium (LD). LD happens, considering two separate loci located on the same chromosome, when the presence of the genotype at one locus is not 25 independent of the other. In other words, they are linked and tend to occur together. Since it is described through DNA recombination, the strength of LD is a function of the distance between two loci: the closer they are, the stronger the LD (Mackay and Powell, 2007). In association analysis, the final mapping resolution relies on the decay of LD over distance, which differs both between and within species (Collins, 2007). Therefore, association mapping may have less power when performed on a bi-parental mapping population where LD is higher. Cloning QTLs in wheat To breeders, QTL cloning is not a routine option and is economical only for those loci with clear added value (Salvi and Tuberosa, 2007). Only a very few QTLs in bread wheat have been cloned through map-based cloning and the underlying gene characterized (Liu et al., 2013; Uauy et al., 2006). QTL cloning in wheat is challenging partly because of its large and complex genome and the lack of a high quality reference sequence. This issue could be addressed by synteny. Cereal genomes show substantial conservation in gene order, known as synteny or colinearity (Akhunov et al., 2013; Dubcovsky et al., 2001; Qi et al., 2013; Sorrells et al., 2003). This has great important applications. For example, Akhunov et al. (2013) used the syntenic relationships between wheat and Brachypodium distachyon, rice, and sorghum to order contigs and scaffolds of wheat chromosome 3A. Salse et al. (2008) studied the evolution of grasses through comprehensive analysis of intragenomic duplications and comprehensive synteny. However, macro-collinearity does not always predict micro-colinearity (Sorrells et al., 2003). An abundance of rearrangements, 26 insertions, deletions, and duplications exist when grass genomes are compared (La Rota and Sorrells, 2004). Therefore, for QTL cloning, synteny is mostly reliable when a relatively small genomic region is examined. In general, four steps are generally involved in cloning a QTL in wheat. First, a bi- parental population is used to locate a QTL and its flanking markers on a certain chromosome. Second, a fine-mapping population derived from a cross between two parents differing only in the flanking marker-defined region is used to construct a precise genetic map indicating the position of the QTL of interest. In this step, the QTL is physically mapped to one of wheat’s deletion bins based on the physical position of its flanking markers (Abeysekara et al., 2010; Hua et al., 2009). Wheat geneticists have developed a collection of deletion stocks that physically dissect wheat chromosomes into bins (Endo and Gill, 1996). A number of simple sequence repeats (SSRs) and expressed sequence tag (ETSs) are also physically mapped to these deletion bins through Southern hybridization experiments (Qi et al., 2004; Sourdille et al., 2004). Thirdly, the sequences of the ESTs that mapped to the same deletion bin with the QTL of interest are used as query sequences to search the rice and Brachypodium distachyon genome sequences to identify a collinear region. Namely, saturation mapping via synteny (Zhang et al., 2013). Rice and Brachypodium distachyon genes residing within a colinear region are used to search the wheat ESTs database to identify previously unmapped ESTs to saturate the flanking marker-defined region. The fourth step involves sequencing BAC clones (Liu et al., 2013). After saturation mapping, the QTL region is narrowed down. The two closest flanking markers are used to screen BAC libraries and chromosome walking as well as 27 sequencing of the target interval leads to the identification of candidate genes (Krattinger et al., 2009). Trends in wheat breeding High-throughput phenotyping Linking genotypic variation to observed traits/phenotypes is essential for marker assisted selection and breeding by design in breeding practice (Peleman and van der Voort, 2003; Tester and Langridge, 2010). The rapid development of genomics-based genotyping technologies in the past decade, especially sequencing capability, has offered breeders powerful tools and resources to access a wealth of genomic information on a breeding population at a relatively low cost (Davey et al., 2011). In contrast, phenotyping a large breeding population for multiple traits at multiple environments is still technically challenging and laborious (Furbank and Tester, 2011). The lack of access to high- throughput and high-dimensional phenotypic data on organism-wide scale has become a new bottleneck that limits our ability to dissect the genetics of quantitative traits in both crop improvement and basic research (Houle et al., 2010). Interest in developing high-throughput phenotyping platforms (HTPPs) has arisen from both private and public sectors to address the issue (Araus and Cairns, 2014). Collaborative networks have formed to build HTPPs. Some of the most advanced and fully automated public facilities for indoor experimentation include the Australian Plant Phenomics Facility and the European Plant Phenotyping Network. These platforms are equipped with robotics, conveyor systems, imaging stations, watering stations, and computing infrastructure and are able to operate automatically to collect data for 3D plant 28 canopy architecture, canopy temperature, leaf color and morphology, and photosynthesis at different developmental stages (http://www.plantphenomics.org.au; http://www.plant- phenotyping-network.eu). Under field conditions, HTPPs employs, mostly, remote sensing and imaging and near-infrared reflectance spectroscopy analysis to finish rapid assessment of traits such as vegetation indices at more or less frequent intervals during the crop cycle (Araus and Cairns, 2014). Field HTPPs carry multiple sets of sensors and often use high-clearance tractors, cable robots, helicopters, aerostats, and drones as sensor carriers (White et al., 2012). Accurate and rapid phenotypic data produced on HTTPs (indoor and outdoor) helps breeders and crop scientists to exploit genomic information and gain new insights that are hard or unable to access before. For example, rice researchers built a high-throughput rice phenotyping facility and demonstrated that, when combined with genome wide association studies, high-throughput phenotyping better dissected the genetic architecture of rice complex traits such as shoot weight and green leaf area than traditional manual measurements (Yang et al., 2014). In addition, an image-based high-throughput field phenotyping system for crop roots was developed and identified 13 new plant root traits that differentiated nine maize genotypes 8 weeks after planting (Bucksch et al., 2014). High-throughput genotyping is emerging as a new crop breeding frontier and is revolutionizing many areas of plant science (Araus and Cairns, 2014; Kloth et al., 2015; Klukas et al., 2014). Genomic selection Prediction of crop performance as a function of genetic architecture is a major challenge for crop research (White et al., 2012). Marker-assisted selection (MAS) has been successfully and efficiently used to select elite cultivars with desired qualitative 29 characters such as enhanced disease resistance. However, since MAS has traditionally relied on markers linked to large-effect quantitative trait loci (QTL), it has been less effective for quantitative traits that are complex and controlled by many genes with small effects (Jannink et al., 2010). Genomic selection has been proposed and implemented as a new breeding approach to address the deficiency of MAS and to accelerate genetic gains in plant and animal breeding (Crossa et al., 2014; Meuwissen et al., 2001). In contrast to MAS, genomic selection simultaneously estimates the allelic effects of all available markers spread across the genome to predict phenotypic performance and does not test the significance of a link between a marker and a QTL (Massman et al., 2013). Genomic selection first uses a ‘training population’ of individuals that have been both genotyped and phenotyped to produce genomic estimated breeding values (GEBVs) for each marker which are further used by a prediction model to predict the performance of a ‘candidate population’ from which individuals are only genotyped and, then, selected based on their GEBVs for advancement in the breeding cycle (Jannink et al., 2010). Genomic selection has been evaluated with simulation data and real data in dairy cattle (Hayes et al., 2009), mice (Legarra et al., 2008), rye (Wang et al., 2014), sugar beet (Wurschum et al., 2013), rice (Xu et al., 2014), wheat (Poland et al., 2012), and maize (Crossa et al., 2013). The correlation between true breeding value and the estimated breeding value has reached levels of 0.85 even for polygenic low heritability traits (Heffner et al., 2009). With its continuously improved prediction accuracy, genomic selection could dramatically change the role of phenotyping from selecting lines to updating prediction models and substantially accelerate the breeding cycle (Heffner et al., 30 2009; Morrell et al., 2012). It is expected that genomic selection will revolutionize plant and animal breeding in the next decade (Henryon et al., 2014; Morrell et al., 2012). Synthetic wheat Hexaploid wheat evolved from the hybridization between T. turgidum (AABB) and Ae. tauschii (DD). It is believed that only a limited number of these two donor species were involved in the speciation process and, thus, the genetic diversity of hexaploid wheat was largely reduced (Yang et al., 2009). To address this evolutionary bottleneck and introduce favorable alleles into hexaploid wheat from its wild relatives, synthetic wheats have been made via artificial synthesis of hexaploid wheat (T.turgidum × Ae. tauschii) in a manner analogous to the natural evolution of hexaploid wheat (Trethowan and van Ginkel, 2009). Many of these wild species, especially Ae. tauschii, possess novel and elite genes for biotic and abiotic stresses which can provide synthetic wheat with exceptional disease resistance and stress tolerance (Dreisigacker et al., 2008; Jia et al., 2013b). In addition, synthetic wheat is also a valuable source of alleles to improve grain yield and yield components (del Blanco et al., 2001). Since the early 1990s, the International Maize and Wheat Improvement Center (CIMMYT) has started making synthetic wheat and transferring favorable traits to CIMMYT elite breeding lines (Dreisigacker et al., 2008). To date, more than 1000 syntheitc wheats have been produced by CIMMYT and are being used by breeding programs worldwide (Dreisigacker et al., 2008; Yang et al., 2009). Synthetic wheat and synthetic wheat-derived cultivars have great potential for enhancing grain yield and adaptation of modern hexaploid wheat (Li et al., 2014; Trethowan and van Ginkel, 2009). Thus, a new generation of wheat varieties produced from synthetic wheats is on the horizon (van Ginkel and Ogbonnaya, 2007). 31 Chapter 2: Quantitative trait loci mapping of grain yield in a doubled haploid population of soft red winter wheat Abstract Understanding the genetic basis of grain yield and yield components is the key to improving grain yield potential in common wheat (Triticum aestivum L.). My objective was to identify quantitative trait loci (QTL) associated with grain yield (GYLD), spikes m-2 (SPSM), grain weight per spike (GWPS), grains per spike (GPS) and thousand-grain- weight (TGW) using a doubled haploid (DH) population. The DH population was evaluated in five environments and was genotyped with single nucleotide polymorphism (SNPs), simple sequence repeats (SSRs), and a morphological marker. The linkage map spanned 1977.6 cM with an average interval length of 2.3 cM. Sixty four putative QTLs for GYLD, SPSM, GWPS, and GPS were detected on eighteen wheat chromosomes. The phenotypic variance explained by these QTLs ranged from 3.7% for GWPS to 71.2% for TGW. The major GYLD QTL (QYld.cz-3B.2) and TGW QTL (QTgw.cz-7A.5) identified in the present study explained 21.2% and 71.2% of the phenotypic variation, respectively. GYLD QTLs closely linked to Fhb1 and Ppd-D1 genes were identified. Eleven QTLs exhibited pleiotropic effects. A genomic region with significant pleiotropic effects for GYLD, SPSM, GWPS, and GPS was located on 1A. In addition, QTL × environment interaction, epistasis and epistasis × environment interactions were detected. Major QTLs identified in this study could be used in marker-assisted breeding to increase grain yield or QTL fine mapping. 32 Introduction Wheat (Triticum aestivum L.) is the staple food for more than 40% of the world’s population. Increasing wheat production is essential to meet the demand of wheat consumption from an increasing population worldwide. As one of the key economic drivers behind the wheat cropping enterprise, improving grain yield potential is a major goal in both public and private breeding programs (Kuchel et al., 2007b). Grain yield is a resultant complex trait influenced by many processes that involve vegetative and reproductive growth and developmental stages (Yoshida, 1972). Grain yield is determined by yield component traits, such as grains per spike (GPS), spikes m-2 (SPSM), grain weigh per spike (GWPS), thousand-grain-weight (TGW) and affected by other yield related traits, e.g. plant architecture. Yield and yield component traits are genetically controlled by multiple quantitative trait loci (QTL) with major and minor effects that are highly influenced by environmental conditions (Deng et al., 2011; Kumar et al., 2007). Identification of QTLs on specific chromosomes for yield and yield components can facilitate incorporating these traits into regionally adapted cultivars in an effective manner through marker assisted selection (MAS) (Carter et al., 2011). This allows breeders to test for the presence and to track down the proven QTL by targeting its closely linked markers for a more efficient and accurate selection of superior cultivars (Kuchel et al., 2007b). A large number of QTL studies have been reported in wheat (Heidari et al., 2011; Kuchel et al., 2007b; Kumar et al., 2007; Wu et al., 2012) and QTLs for grain yield and yield components have been identified in all wheat chromosomes 33 mostly with minor genetic effects (Wu et al., 2012; Zhang et al., 2010). For example, using two wheat mapping populations, Kumar et al. (2007) detected eighty-six QTLs out of which six were pleiotropic/coincident involving more than one yield related trait. Kuchel et al. (2007b) found in a DH population that although the higher yielding parent contributed most of the favorable alleles, the lower yielding parent also possessed higher yielding QTLs based on the data from eighteen environments. Li et al. (2007b) identified five environment-specific QTLs for GYLD on chromosome 1D, 2D, and 3B explaining 10.4-23.0% of the phenotypic variation. Groos et al. (2003) reported a stable QTL for TGW on chromosome 2B which explained up to 20% of the phenotypic variation in seven trials. Interestingly, the favorable allele was from Récital, the parental line with lower TGW. Heidari et al. (2011) identified a genomic region on chromosome 1A for GPS explaining up to 22.4% of the phenotypic variation in two environments and three QTLs for SPSM on chromosome 1A, 7A, and 2D explaining up to 21.4% of the phenotypic variation. Several large-effect loci affecting grain yield per se such as Rht1 and Ppd-D1 have been cloned and molecularly characterized (Boden et al., 2015; Pearce et al., 2011). One locus, TaCKX6-D1, significantly associated with TGW in wheat was isolated and shown to be orthologus to rice gene OsCK2. Moreover, yield component traits are less environmentally sensitive and generally exhibit higher heritability than grain yield, as a result of which, indirect selection on yield component traits tends to result in higher stable genetic gain than direct selection for grain yield (Kumar et al., 2007; Wu et al., 2012). Therefore, examining yield components when evaluating grain yield per se is necessary for sustained yield potential improvement (Wu et al., 2012). 34 Additionally, additive main effects, digenic epistasis, QTL × environment interactions (additive × environment interaction and epistasis × environment interactions) also are crucial factors determining the expression of quantitative traits (Mackay, 2001). In classical Mendelian genetics, the masking of genotypic effects at one locus by genotypes of another is called epistasis which is also broadly used to indicate any statistical interaction between genotypes at two (or more) loci in quantitative genetics (Mackay et al., 2009). Epistasis can be synergistic or antagonistic depending on whether the effect of one locus is enhanced or suppressed by the second locus (Mackay, 2001). As a result, the phenotype of a certain genotype would not be a simple sum of the additive effects of all loci involved. When plants are challenged by fluctuations in environmental conditions, both additive and epistatic effects of the same loci are modified to some extent so that plants can adapt to new situations by changing its phenotypic expression, known as phenotypic plasticity (El-Soda et al., 2014). A thorough understanding of the interactions mentioned above in breeding populations would help breeders predict the performance of genotypes across years and locations with more confidence. However, due to the lack of appropriate methodology and easy-to-use statistical software, QTL detection was typically conducted under the assumption of additive main effects only until the mixed- model based composite interval mapping (MCIM) was developed (Wang et al., 1999). MCIM showed high accuracy and power in mapping QTLs with epistatic effects and QTL × environment interactions by using the-best-linear-unbiased prediction (BLUP) method (Wang et al., 1999) and has been well accepted ever since (Li et al., 2007b; Xing et al., 2002; Zhang et al., 2009). Another big constraint in accurate QTL mapping and subsequent application of MAS was the lack of fast and large-scale genotyping platform 35 as the cost of initial genotyping approaches were high. A recent development in DNA marker technology is single nucleotide polymorphisms (SNPs). In contrast to traditional simple sequence repeats (SSRs) and amplified fragment length polymorphisms (AFLPs), SNPs are more abundant across genomes of many species and constitute ~90% of the genetic variation in virtually all organisms (Gupta et al., 2008). Recently, SNP discovery and QTL mapping using SNPs have been reported in many crop plants such as rice, maize, barley, wheat, and sunflower (Bachlava et al., 2012; Cavanagh et al., 2013; Close et al., 2009; Ganal et al., 2011; Hu et al., 2013; McCouch et al., 2010). Trebbi et al. (2011) discovered and validated a set of 275 SNPs in durum wheat using 12 durum cultivars through complexity reduction of polymorphic sequences (CroPS) technology and Illumina Golden Gate technology. Ganal et al. (2011) developed a large maize SNP array containing 57,838 markers across the genome, out of which 49,585 markers, representing 17,520 genes were storable and of good quality for further genotyping. This SNP array was then used to genotype two recombinant inbred line populations and two high density linkage maps were also established with 20,913 and 14,524 markers respectively. Moreover, using the RICE6K SNP array, Hu et al. (2013) mapped five novel QTLs for rice grain shape. Furthermore, genotyping by sequencing (GBS) is a new SNP genotyping method suitable for high diversity and large genomes and has shown to be “simple, quick, extremely specific, highly reproducible, and may reach important regions of the genome that are inaccessible to sequence capture approaches” (Elshire et al., 2011). Compared with other sequencing-based genotyping method such as restriction- site-associated DNA sequencing (RAD-seq), GBS has simpler library preparation protocols but produces equivalent results at very low cost per sample (Davey et al., 36 2011). Coupling GBS with multiplex technology, up to 2,688 samples/breeding lines can be processed simultaneously per sequencing run (Elshire et al., 2011). In maize, for example, 200,000 markers were identified and mapped in a very short time at a cost of $8,000 (Elshire et al., 2011). High grain yield of any crop can be achieved only when a proper combination of cultivar, environment, and agronomic practices is obtained (Yoshida, 1972). Understanding the genetic effects of QTLs, how QTLs interact with each other, and how these QTLs and their interactions are affected in different environments is important for breeders. In the present study, quantitative trait loci mapping in a DH population of soft red winter wheat was attempted (1) to identify QTLs affecting grain yield and yield components mostly with SNP makers, (2) to determine the additive genetic effects, digenic epistasis effects and their interactions with environments. Materials and Methods Genetic resources A doubled haploid (DH) population was established from the cross of the soft red winter wheat germplasm line MD01W233-06-1 (MDW233) (Costa et al., 2010) and soft red winter wheat cultivar Southern States 8641 (SS8641) (Johnson et al., 2007b). The population consists of 124 DH lines and shows a wide range of phenotypic variation for yield and yield components. MDW233 was produced by crossing the soft red winter wheat cultivar ‘McCormick’ (VA92–51–39 (IN71761A4–31–5-48//VA71–54– 147/‘McNair 1813’)/AL870365 (‘Coker 747*2/‘Amigo’)) (PI632691) (Griffey et al., 2005) with ‘Choptank’ (‘Coker 9803’/‘Freedom’) (PI 639724) (Costa et al., 2006) and 37 was released by the Maryland Agricultural Experiment Station in 2009 with enhanced Fusarium Head Blight (FHB) resistance. MDW233 carries the Rht-D1b dwarfing gene and the Ppd-D1b photoperiod sensitive allele. SS8641 was photoperiod insensitive and was released by the University of Georgia Experiment Station in 2007, with high yield and multiple disease resistance (Johnson et al., 2007b). It is a medium-maturing, white- chaffed, medium-tall line derived from the cross 'GA 881130 / 2* GA 881582'. The pedigree of GA 881130 is 'KSH8998 / FR81-10 // Gore'. KSH8998 was developed from the cross of a hard wheat with Ae. tauschii to transfer Hessian fly resistance gene H13. FR81-10 was selected because of its resistance to leaf rust (Lr37 and Yr17) from the cross 'Novisad 138 /4/Ae.ventricosa/T.persicum/2/ Marve*3/3/Moisson'. Field experiments The DH mapping population and parents were grown in five environments: Clarksville, MD and Queenstown, MD in 2013 and 2014 and at Kinston, NC in 2014. The entries were evaluated in field trials with two replications in a randomized complete block design. Yield plots at Maryland consisted of seven rows 15.2 cm apart. Seed density was 22 seeds per 0.305 m in each row. The length of rows harvested was 4.17 m, making the harvest area 3.8 m2. Yield plots at North Carolina had seven rows 19.1 cm apart with a seed density of 24 seeds per 0.305 m in each row. The length of rows harvested was 3.35m, making the harvest area 3.8 m2. Growing season rainfall and temperature data were obtained from respective research farms for Clarksville, MD and Queenstown, MD and the National Oceanic and Atmospheric Administration (NOAA) measurements for Kinston, NC (National Climatic Data Center 2014) (Table 2.1). Soil fertility management followed recommended management practices for each location. All trials were sprayed 38 with the metconazole fungicide (Caramba®, BASF) at anthesis to reduce potential infection by Fusarium graminearum. Phenotypic data collection At maturity, plots were mechanically harvested using a small plot combine (Wintersteiger Nurserymaster Elite, Ried, Austria). Plot weight and moisture-content data of the wheat trials were obtained with a HarvestMaster HM1000b (Juniper Systems, Logan, UT) attached to the plot combine. Gain yield was measured from seed collected from the combine as pounds per plot and reported as grams per square meter. Grains per spike was recorded as the mean of the number of grains of ten random spikes from each plot. Grain weight per spike was measured using ten random spikes harvested from each plot. Spikes per square meter was calculated by dividing grain yield by grain weight per spike. Thousand-grain-weight was computed from the weight of 200 random grains from a sample harvested from each plot. Statistical analysis of traits Analysis of variance (ANOVA) for GYLD, GPS, GWPS, SPSM, and TGW was performed separately for each environment and for the five environments combined using the PROC GLM procedure of SAS version 9.3 (SAS Institute, Raleigh, NC 2013). The ANOVA model for single environment analysis was Y= replication + genotype + error, where replication and genotype were fixed and error was random. Combined ANOVA was performed to examine the effects of environments and the model was Y = environment + replication within environment + genotype + genotype × environment + error, where error was considered random and all others were fixed. Pearson’s correlation 39 coefficients were calculated using the PROC CORR procedure of SAS. Broad-sense heritability (h2) (defined as h2= ???/(???+(???? /?)+ (???/??)), where ??? is the variance of genotypic effect, ???? is the genotype × environment variance, and ? and ? are the number of environments and replicates, respectively) was calculated on a family mean basis using the PROC MIXED procedure of SAS, as described by Holland et al. (2003). The descriptive statistics of all traits were calculated using the PROC MEANS procedure of SAS (Table 2.2). Genotyping SSR genotyping was performed at the USDA-ARS Eastern Regional Small Grain Genotyping Lab at Raleigh, NC, USA. Approximately 25 mg of leaf tissue of the parents and 124 doubled haploid lines were collected from 2-3 week-old seedlings for genomic DNA extraction which was performed according to the protocol of Pallotta et al. (2003). For all SSR markers, the polymerase chain reaction (PCR) master mix consisted of 2 μL of 20 ng μL–1 genomic DNA template, 0.40 μL of a 10 μM mixture of forward and reverse primers, 0.18 μL (0.9 U) of Taq polymerase, 1.20 μL of 10x buffer (10 mM Tris- HCL, 50 mM KCl, and 1.5 mM MgCl2, pH 8.3), 0.96 μL of a 100 μM mixture of deoxyribonucleotide triphosphates (dNTPs), and 7.26 μL of water, bringing the total reaction volume to 12 μL. A touchdown profile was used that consisted of an initial denaturation at 95°C followed by 15 cycles of 95°C for 45s, 65°C for 45s decreasing by 1°C each cycle, and 72°C for 60s, followed by 25 cycles of 50°C annealing temperature. The forward primers were 5'-modified to include one of the following fluorescent dyes: 6-FAM, VIC, NED, or PET. Amplifications were performed using an Eppendorf Mastercycler (Eppendorf AG, Hamburg, Germany). Sizing of PCR products was 40 performed by capillary electrophoresis using an ABI3130xl Genetic Analyzer (Applied BioSystems, Foster City, CA). Analysis of PCR fragments was performed using GeneMarker 1.60 software (SoftGenetics, LLC, State College, PA) SNP genotyping was performed on the 9K iSelect SNP genotyping array containing 9,000 wheat SNP markers developed by Illumina Inc. (San Diego, CA, USA). This assay was designed under the protocols of the International Wheat SNP Consortium (Cavanagh et al., 2013). Additionally, genotyping-by-sequencing (GBS) was also employed for SNP genotyping as was described by Elshire et al. (2011). The SNP array was conducted at the USDA-ARS Small Grains Genotyping Lab at Fargo, ND, USA and GBS assay at the USDA-ARS Central Small Grain Genotyping Lab at Manhattan, KS, USA. Map construction and QTL analysis Markers with more than 20% missing rate and those that were monomorphic and distorted (differing significantly from the expected 1:1 segregation ratio) were eliminated from the analyses. The remaining polymorphic markers were used to construct linkage groups using the MAP function in software IciMapping version 4.0 with a LOD value of 10 (Li et al., 2008). Recombination frequencies were converted to centimorgans (cM) using the Kosambi mapping function. Assignment of linkage groups to chromosomes was based on the SNP consensus map (Cavanagh et al., 2013) and on the SSR consensus map (Somers et al., 2004), and as well as with wheat POPSEQ data (http://wheat- urgi.versailles.inra.fr/), after which, genetic distance of markers on the same chromosome was recalculated with RECORD and COUNT algorithm in IciMapping version 4.0. 41 Detecting QTL with additive effects was performed by IciMapping version 4.0 using the additive module (ICIM-ADD). The walking speed for all traits was 1 cM. Reference LOD values were determined by 1,000 permutations (Doerge, 2002). Type I error to determine the LOD from the permutation test was 0.05. The LOD threshold to declare the presence of a significant QTL was 3.0. The position at which the LOD score curve reaches its maximum was used as the estimate of the QTL location. Further QTL analysis for digenetic QTL epistasis (A×A or Q×Q), additive × environment (A×E or Q×E) and epistasis × environment (QQ×E) interactions was performed with QTLNetwork version 2.1 using mixed-model based composite interval mapping (MCIM) (Wang et al., 1999; Yang et al., 2007). All effects mentioned above were estimated by Monte Carlo Markov Chain method with a scanning speed of 1 cM step with a 0.05 experiment-wise type I error. Results Environment conditions Phenotypic data for QTL analysis was collected from five environments (Table 2.1, Appendix C). The conditions at five environments varied for rainfall and average monthly temperature during each growing season. In 2013 and 2014, Queenstown had more precipitation and higher average temperature than that of Clarksville. However, both of these two locations had less precipitation and lower average monthly temperature than that at Kinston 2014. In 2013, the precipitation at Clarksville was lower than that of 2014 but the average temperature was higher implying that 2013 was a relatively warmer and drier growing season. At Queenstown, the 2013 season had more precipitation and higher average monthly temperature than that of 2014. 42 Table 2.1 Growing season precipitation (cm) and average monthly temperature (°C) at five environments during 2013 and 2014. Phenotypic performance Analysis of variance (ANOVA) performed separately for each environment indicated significant differences (P<0.001) among all traits (data not shown). Combined ANOVA showed that genotype × environment interaction was significant (P<0.001) for GYLD, GWPS, SPSM, TGW, and GPS (Table 2.2). MDW233 had more SPSM while SS8641 had higher GPS, GWPS, and TGW across all five environments except for Clarksville 2014 where MDW233 produced slightly higher TGW than SS8641 (Table 2.2). For grain yield, MDW233 performed better in all four Maryland environments but not as well as SS8641 in Kinston 2014. Furthermore, SPSM had the most variation (measured by coefficient of variation) among all traits across five environments (Table 2.2). The DH lines showed transgressive segregation for all traits (Figure 2.1, Table 2.2). The heritability estimates were highest for thousand-grain-weight (0.92) and grain weight spike-1 (0.90) followed by spikes m-2 (0.84) and grains spike-1 (0.81), but was lowest for grain yield (0.74) (Table 2.4). Correlation analysis (Table 2.3) showed consistently that grain yield was positively correlated with SPSM and TGW (P<0.001). The correlation between GYLD and GPS was positive at Queenstown 2014 but was negative at Clarksville 2013 and Clarksville Environments Precipitation (cm) Temperature (°C) Feb. Mar. Apr. May Jun. Total Feb. Mar. Apr. May Jun. Average Queenstown 2013 6.1 9.3 11.8 4.9 24.9 57.1 2.0 5.1 12.7 17.7 23.0 12.1 Queenstown 2014 11.3 11.9 13.2 9.3 7.0 52.7 0.9 3.8 11.7 18.2 22.2 11.4 Clarksville 2013 5.0 6.5 4.7 9.0 12.7 37.9 0.6 3.8 11.8 16.5 21.9 10.9 Clarksville 2014 6.1 9.9 17.1 10.4 8.4 51.9 -1.2 2.4 10.7 17.2 22.2 10.3 Kinston 2014 6.5 14.2 11.0 8.9 26.3 67.0 8.4 9.8 17.9 22.9 25.4 16.9 43 2014. In general, GYLD showed the strongest positive correlation with SPSM followed by TGW. SPSM was negatively correlated with GPS and GWPS in all five environments. TGW was positively correlated with GWPS and was negatively correlated with GPS in all five environments. Table 2.2 Phenotypic summary of grain yield (GYLD, g m-2), grains per spike (GPS), grain weight per spike (GWPS, g), spikes per square meter (SPSM), and thousand-grain-weight (TGW, g) evaluated in five environments during 2013 and 2014. Environments Traits Parents DHs MDW233 SS8641 Mean SD† Minimum Maximum CV‡ Clarksville 2013 GYLD 671.4 598.6 566.7 110.9 268.7 1091.6 19.6% GPS 38.4 45.8 39.9 5.2 27.2 54.8 12.9% GWPS 1.2 1.5 1.2 0.2 0.9 1.7 15.0% SPSM 565.8 393.9 473.6 110.9 182.9 863.0 23.4% TGW 33.4 34.8 31.5 2.3 25.7 37.4 7.3% Queenstown 2013 GYLD 712.0 664.8 736.6 144.4 363.4 1071.2 19.6% GPS 45.7 52.4 43.5 5.5 30.9 58.2 12.7% GWPS 1.5 1.8 1.4 0.2 1.0 2.0 12.9% SPSM 474.2 372.5 529.3 124.2 222.9 951.3 23.5% TGW 33.1 33.9 32.2 2.1 25.5 38.6 6.6% Clarksville 2014 GYLD 830.2 740.0 787.3 106.1 473.2 1098.1 13.5% GPS 34.7 41.4 35.7 4.0 25.0 48.0 11.1% GWPS 1.0 1.3 1.0 0.1 0.6 1.4 14.3% SPSM 794.3 572.9 806.0 160.4 490.3 1257.7 19.9% TGW 30.7 30.3 29.4 2.5 15.8 36.6 8.4% Queenstown 2014 GYLD 614.9 594.5 614.2 73.9 379.3 769.0 12.0% GPS 34.8 39.2 39.6 4.8 26.8 55.8 12.0% GWPS 1.0 1.2 1.1 0.1 0.8 1.5 13.0% SPSM 622.8 487.6 553.1 83.5 351.9 774.5 15.1% TGW 29.7 31.8 29.2 2.1 23.5 35.2 7.1% Kinston 2014 GYLD 615.0 679.4 555.1 92.8 228.7 837.8 16.7% GPS 35.2 46.0 42.4 5.2 32.0 59.3 12.2% GWPS 1.1 1.5 1.2 0.1 0.7 1.6 12.5% SPSM 534.8 449.3 474.6 82.7 274.9 737.9 17.4% TGW 30.3 31.5 27.5 2.9 18.3 35.5 10.7% † Standard deviation ‡ Coefficient of variation 44 Table 2.3 Pearson correlation coefficients among grain yield (GYLD), grains per spike (GPS), grain weight per spike (GWPS), spikes per square meter (SPSM), and thousand-grain-weight (TGW) in five environments during 2013 and 2014. Environments Traits GPS GWPS SPSM TGW Clarksville 2013 GYLD -0.20*** 0.05 0.79*** 0.33*** GPS 0.75*** -0.62*** -0.15* GWPS -0.54*** 0.36*** SPSM 0.06 Queenstown 2013 GYLD -0.06 0.06 0.83*** 0.21*** GPS 0.82*** -0.50*** -0.22*** GWPS -0.48*** 0.22*** SPSM 0.09 Clarksville 2014 GYLD -0.16*** -0.03 0.69*** 0.29*** GPS 0.64*** -0.57*** -0.24*** GWPS -0.70*** 0.34*** SPSM -0.07 Queenstown 2014 GYLD 0.13* 0.22*** 0.60*** 0.21*** GPS 0.77*** -0.52*** -0.30*** GWPS -0.62*** 0.22*** SPSM -0.03 Kinston 2014 GYLD -0.07 0.33*** 0.74*** 0.44*** GPS 0.61*** -0.49*** -0.39*** GWPS -0.36*** 0.36*** SPSM 0.17** * Significant at the 0.05 probability level. ** Significant at the 0.01 probability level. *** Significant at the 0.001 probability level. Table 2.4 Pooled analyses of variance over five environments and heritability estimates for grain yield (GYLD), grains per spike (GPS), grain weight per spike (GWPS), spikes per square meter (SPSM), and thousand-grain-weight (TGW) in five environments during 2013 and 2014. Mean squares Source of Variation df GYLD GPS GWPS SPSM TGW Environment 4 2697855.85* 2260.17* 5.64* 4679695.46* 841.21* Rep (environment) 5 100996.06* 179.67* 0.45* 429149.39* 8.00* Genotype 123 40596.54* 136.50* 0.11* 47696.44* 38.96* Genotype × environment 492 10475.85* 13.18* 0.02* 8954.60* 3.29* R2 0.85 0.85 0.86 0.88 0.95 Heritability (h2) † 0.74 (0.04) 0.81 (0.03) 0.90 (0.01) 0.84 (0.02) 0.92 (0.01) * Significant at the 0.001 probability level. † Values in parenthesis are standard errors for h2 45 Linkage map construction The DH population was analyzed with 4981 markers that were polymorphic between the two parents (4956 SNPs, 24 SSRs and 1 morphological marker). A total of 4972 markers (99.8%) were assigned to 39 linkage groups representing all 21 wheat chromosomes (Table 2.5). After excluding co-segregating markers, the final genetic linkage map was constructed with 859 unique makers that spanned 1977.62 cM in length (Appendix A). The average interval length was 2.3 cM. Since the recommended map distance for QTL analysis is ten re-combinations per 100 meiotic events, or an interval length less than 10 cM (Doerge, 2002), the map is suitable for QTL analysis in this study. Table 2.5 Distribution of markers and length of linkage maps for twenty one wheat chromosomes. Chromosome Number of markers Length (cM) 1A 521 67.71 2A 298 112.3 3A 333 216.84 4A 272 158.28 5A 218 190.04 6A 242 95.05 7A 365 171.5 1B 257 144.13 2B 516 139.83 3B 488 134 4B 121 134.51 5B 430 123.21 6B 286 112.39 7B 245 177.83 1D 55 85.55 2D 116 125.93 3D 29 72.74 4D 8 76.77 5D 36 179.45 6D 81 144.64 7D 55 259.8 46 Table 2.6 Quantitative trait loci (QTLs), LOD score, percentage of variation explained (PVE), and additive effects of each QTL for grain yield (GYLD, g m-2), grains per spike (GPS), grain weight per spike (GWPS, g), spikes per square meter (SPSM), and thousand-grain-weight (TGW, g) in five environments during 2013 and 2014. QTL Trait Environment Position (cM) Marker interval LOD score PVE (%) Additive effect Qyld.cz-1A GYLD Clarksville 2013 0 Xwmc496-Xsnp1970 3.4128 7.4686 -25.6006 Qyld.cz-2A GYLD Kinston 2014 0 Xsnp2477-Xsnp2432 4.2968 9.9008 25.211 Qyld.cz-3A GYLD Clarksville 2014 0 Xsnp3027-Xsnp3744 3.0403 8.6136 -24.2575 Qyld.cz-6A GYLD Queenstown2014 75 Xsnp4211-Xsnp4186 3.455 9.7424 19.1153 Qyld.cz-1B GYLD Clarksville 2013 74 Xsnp4928-Xsnp2107 5.6376 13.2401 -35.0708 Qyld.cz-3B.1 GYLD Kinston 2014 7 Xbarc147-Xsnp3328 4.5878 10.9125 -26.2832 Qyld.cz-3B.2 GYLD Clarksville 2014 60 Xsnp3382-Xsnp3372 6.7965 21.2499 -38.2413 Qyld.cz-5B.1 GYLD Clarksville 2013 66 Xsnp4059-Xsnp4061 5.5119 12.7278 -33.4221 Qyld.cz-5B.2 GYLD Kinston 2014 115 Xsnp4011-Xsnp4073 4.4601 10.2934 25.6696 Qyld.cz-6B GYLD Kinston 2014 6 Xsnp4444-Xsnp4453 4.6423 10.9935 26.3474 Qyld.cz-2D GYLD Queenstown2013 54 Xsnp2862-XPpdD1 4.5148 17.7026 52.6542 Qyld.cz-6D GYLD Clarksville 2013 137 Xsnp4465-Xsnp4487 6.677 15.5547 -37.1149 QGps.cz-1A.1 GPS Kinston 2014 1 Xsnp1970-Xbarc28 14.2219 26.511 2.439 QGps.cz-1A.2 GPS Clarksville 2013 2 Xbarc28-Xsnp2005 21.1713 44.118 2.9461 QGps.cz-1A.2 GPS Clarksville 2014 2 Xbarc28-Xsnp2005 11.0378 22.5923 1.6195 QGps.cz-1A.2 GPS Queenstown2013 2 Xbarc28-Xsnp2005 12.5949 29.3932 2.681 QGps.cz-1A.2 GPS Queenstown2014 2 Xbarc28-Xsnp2005 8.5734 18.1522 1.7105 QGps.cz-2A GPS Queenstown2014 40 Xsnp2448-Xsnp2475 4.6006 8.99 -1.2111 QGps.cz-3A.1 GPS Kinston 2014 2 Xsnp3048-Xsnp1466 4.4227 6.9464 1.2484 QGps.cz-3A.2 GPS Clarksville 2013 5 Xsnp3049-Xsnp3021 24.1227 51.9916 -3.1986 QGps.cz-3A.3 GPS Clarksville 2013 124 Xsnp3037-Xsnp3023 5.1633 8.0188 1.264 QGps.cz-3A.4 GPS Clarksville 2014 126 Xsnp3023-Xsnp3383 3.4903 6.0965 0.8454 QGps.cz-3A.4 GPS Kinston 2014 126 Xsnp3023-Xsnp3383 5.269 8.2402 1.3662 QGps.cz-4A GPS Queenstown2014 138 Xsnp3464-Xsnp3547 3.2552 6.2726 1.0236 QGps.cz-2B GPS Queenstown2013 62 Xsnp2752-Xsnp2786 3.9483 7.8456 -1.3878 QGps.cz-3B.1 GPS Kinston 2014 34 Xsnp3344-Xsnp3253 6.3159 10.277 1.5188 QGps.cz-3B.2 GPS Queenstown2013 36 Xsnp3253-Xsnp3349 4.5431 9.3033 1.5085 QGps.cz-3B.3 GPS Clarksville 2014 47 Xsnp3119-Xsnp3395 7.0831 13.3475 1.2462 QGps.cz-5B.1 GPS Clarksville 2013 45 Xsnp3973-Xsnp4062 4.5948 6.7766 -1.1547 QGps.cz-5B.2 GPS Queenstown2014 48 Xsnp4083-Xsnp3988 7.6516 15.9459 -1.6031 QGps.cz-5B.3 GPS Clarksville 2014 58 Xsnp3988-Xsnp1006 5.6091 10.3121 -1.0956 QGps.cz-5B.4 GPS Kinston 2014 68 Xsnp4061-Xsnp4027 6.9909 11.3158 -1.5936 QGps.cz-3D GPS Queenstown2014 72 Xsnp3422-Xsnp3187 3.8018 7.4961 -1.1211 QGws.cz-1A.1 GWPS Clarksville 2013 0 Xwmc496-Xsnp1970 12.1722 28.5671 0.079 QGws.cz-1A.2 GWPS Clarksville 2014 1 Xsnp1970-Xbarc28 11.5142 33.1833 0.0641 QGws.cz-1A.2 GWPS Kinston 2014 1 Xsnp1970-Xbarc28 7.284 20.32 0.0599 QGws.cz-1A.2 GWPS Queenstown2013 1 Xsnp1970-Xbarc28 11.28 32.2485 0.0937 QGws.cz-1A.3 GWPS Queenstown2014 2 Xbarc28-Xsnp2005 5.0104 15.4379 0.0446 QGws.cz-3A GWPS Clarksville 2014 188 Xsnp2984-Xsnp2934 3.1047 7.4815 -0.0306 QGws.cz-5A GWPS Clarksville 2013 87 Xsnp3843-Xsnp3820 4.6284 9.4469 0.046 QGws.cz-5B GWPS Clarksville 2013 20 Xsnp4130-Xsnp3884 3.602 7.2761 -0.0398 QGws.cz-6B GWPS Kinston 2014 63 Xsnp4421-Xsnp4451 4.8634 13.2328 0.0483 QGws.cz-7B GWPS Clarksville 2014 58 Xsnp4927-Xsnp489 3.1066 7.472 0.0305 47 Table 2.6 Continued QTL Trait Environment Position (cM) Marker interval LOD score PVE (%) Additive effect QSsm.cz-1A.1 SPSM Clarksville 2013 0 Xwmc496-Xsnp1970 13.8851 30.1443 -50.8488 QSsm.cz-1A.1 SPSM Clarksville 2014 0 Xwmc496-Xsnp1970 4.434 10.2247 -35.3148 QSsm.cz-1A.2 SPSM Kinston 2014 1 Xsnp1970-Xbarc28 15.5894 22.0811 -34.0394 QSsm.cz-1A.3 SPSM Queenstown2013 3 Xbarc28-Xsnp2005 8.2625 22.9784 -51.5593 QSsm.cz-1A.3 SPSM Queenstown2014 2 Xbarc28-Xsnp2005 6.9139 15.2758 -25.0824 QSsm.cz-2A.1 SPSM Kinston 2014 0 Xsnp2477-Xsnp2432 3.3285 3.7029 14.0568 QSsm.cz-2A.2 SPSM Queenstown2014 75 Xsnp2382-Xsnp2401 4.8376 10.2491 20.7007 QSsm.cz-3A SPSM Kinston 2014 1 Xsnp3744-Xsnp3048 3.5396 3.9658 -14.4323 QSsm.cz-6A SPSM Clarksville 2014 81 Xsnp4197-Xsnp473 3.6434 8.2722 31.9406 QSsm.cz-1B.1 SPSM Kinston 2014 8 Xsnp2205-Xsnp4503 3.241 3.6256 -13.836 QSsm.cz-1B.2 SPSM Clarksville 2013 72 Xsnp4928-Xsnp2107 3.362 6.142 -23.355 QSsm.cz-3B.1 SPSM Queenstown2014 24 Xsnp3405-Xsnp3389 5.7956 12.7812 -23.0253 QSsm.cz-3B.2 SPSM Kinston 2014 31 Xsnp3389-Xsnp3344 5.9097 6.9082 -19.0795 QSsm.cz-3B.3 SPSM Clarksville 2014 46 Xsnp3335-Xsnp3119 3.8828 8.9779 -33.1306 QSsm.cz-3B.4 SPSM Kinston 2014 129 Xsnp3401-Xsnp3358 3.3933 3.8222 -14.2074 QSsm.cz-5B.1 SPSM Clarksville 2013 75 Xsnp4072-Xsnp4085 3.1264 5.6471 -22.0261 QSsm.cz-5B.2 SPSM Kinston 2014 116 Xsnp4011-Xsnp4073 5.1611 6.03 17.9303 QSsm.cz-6B SPSM Kinston 2014 3 Xsnp4456-Xsnp107 10.2874 13.4102 26.5311 QSsm.cz-2D SPSM Queenstown2013 57 Xsnp2862-XPpdD1 4.7473 12.6431 38.5899 QSsm.cz-3D SPSM Queenstown2014 57 Xsnp3422-Xsnp3187 4.858 10.5722 21.1958 QSsm.cz-6D SPSM Clarksville 2013 137 Xsnp4465-Xsnp4487 3.2886 5.8034 -22.4133 QTgw.cz-3A.1 TGW Clarksville 2013 126 Xsnp3023-Xsnp3383 5.3262 8.2592 -0.6336 QTgw.cz-3A.2 TGW Queenstown2013 136 Xsnp1758-Xsnp1485 6.4554 11.2965 -0.6975 QTgw.cz-3A.3 TGW Queenstown2014 137 Xsnp1485-Xsnp2964 4.5797 9.3414 -0.6078 QTgw.cz-3A.4 TGW Clarksville 2014 143 Xsnp2885-Xsnp2987 3.0018 4.914 -0.514 QTgw.cz-3A.5 TGW Kinston 2014 147 Xsnp2937-Xsnp4728 4.9325 12.6304 -1.0067 QTgw.cz-3A.6 TGW Clarksville 2013 208 Xsnp2951-Xsnp2971 4.2346 6.7403 -0.5712 QTgw.cz-5A.1 TGW Kinston 2014 53 Xsnp218-Xsnp49 3.5745 8.8337 0.8469 QTgw.cz-5A.2 TGW Clarksville 2013 58 Xsnp3838-Xbarc100 7.3695 11.6843 0.7601 QTgw.cz-5A.3 TGW Clarksville 2014 60 Xbarc100-Xsnp4843 5.4004 9.237 0.7158 QTgw.cz-5A.3 TGW Queenstown2013 61 Xbarc100-Xsnp4843 5.7471 10.1805 0.6715 QTgw.cz-7A.1 TGW Queenstown2013 18 Xsnp4718-Xsnp4759 4.8786 8.2581 -0.5963 QTgw.cz-7A.2 TGW Clarksville 2013 105 Xsnp4637-Xsnp4567 5.1786 8.0243 0.6267 QTgw.cz-7A.3 TGW Queenstown2014 107 Xsnp4946-Xsnp4546 6.7631 14.7144 0.7671 QTgw.cz-7A.4 TGW Clarksville 2014 115 Xsnp4935-Xsnp4622 5.7396 9.8599 0.7303 QTgw.cz-7A.5 TGW Queenstown2013 123 Xsnp4588-Xsnp4620 26.7529 71.1913 -1.7692 QTgw.cz-1B.1 TGW Clarksville 2013 85 Xsnp2084-Xsnp2113 6.3088 9.8591 -0.6978 QTgw.cz-1B.1 TGW Clarksville 2014 86 Xsnp2084-Xsnp2113 3.6645 6.0751 -0.5787 QTgw.cz-1B.2 TGW Queenstown2014 87 Xsnp2113-Xsnp2091 3.6309 7.2769 -0.5424 QTgw.cz-2B TGW Clarksville 2014 58 Xbarc10-Xsnp2744 6.1855 10.8214 0.7626 QTgw.cz-2B TGW Kinston 2014 58 Xbarc10-Xsnp2744 4.7936 12.1618 0.9873 QTgw.cz-4B TGW Queenstown2013 75 Xsnp3721-Xsnp1656 4.2407 7.1261 0.5646 QTgw.cz-7B.1 TGW Clarksville 2014 58 Xsnp4927-Xsnp489 5.7036 10.3833 0.747 QTgw.cz-7B.1 TGW Queenstown2013 58 Xsnp4927-Xsnp489 3.0727 5.3189 0.4786 QTgw.cz-7B.2 TGW Clarksville 2013 63 Xsnp838-Xsnp4852 7.3537 11.8238 0.7572 QTgw.cz-7B.3 TGW Queenstown2014 65 Xsnp4852-Xsnp4943 4.2808 9.5333 0.6163 QTgw.cz-5D TGW Clarksville 2013 95 Xsnp4170-Xsnp4157 3.6356 5.4776 0.5138 48 QTL with additive and additive × environment interaction effects ICIM-ADD mapping detected a total of 64 putative QTLs for grain yield and yield components at five environments (Table 2.6). Significant QTLs were detected on all chromosomes except 1D, 4D, and 7D. QTLs were unevenly distributed across the three homoeologous groups and twenty one chromosomes of wheat. Thirty QTLs (46.9%) were in the A genome, also 30 QTLs (46.9%) were in the B genome, and only 4 (6.3%) were in the D genome. Distribution of QTLs was also unbalanced on chromosomes among homologous chromosome groups as follows: 7 on chromosomes 1 (11.1%), 6 on chromosomes 2 (9.4%), 22 on chromosomes 3 (34.4%), 2 on chromosomes 4 (3.1%), 13 on chromosomes 5 (20.3%), 6 on chromosomes 6 (9.4%), and 8 on chromosomes 7 (12.5%). The number of QTL for individual traits ranged from 8 to 22. Specifically, 12 QTL were identified for grain yield and each of them explained 7.5% to 21.3% of the phenotypic variation; 17 QTL were identified for grains spike-1 explaining 6.1 % to 44.1% of the phenotypic variation; 19 QTL were detected for spikes m-2 and 8 QTL were for grain weight spike-1 accounting for 3.7% to 15.6% and 7.3% to 33.2% of the phenotypic variation respectively; 22 significant QTL were found to explain 5.5% to 71.2% of the phenotypic variation of thousand grain weight. In addition, 11 marker intervals where QTL co-location existed were estimated to have pleiotropic effects. Among all QTL identified, 6 QTL were repeatedly detected in more than one environment. 49 Additive × environment interaction effects were detected for all traits evaluated except for TGW. Of the five significant QTLs, three were detected with additive main effects in previous single environment mapping and the other two were insignificant (LOD<3) for additive main effects, hence, were environment-specific QTLss (Table 2.7). The heritability of additive × environment interactions ranged from 0.2% to 27.4%. Clarksville 2013 had the most additive × environment interactions, followed by Queenstown 2014. One additive × environment interaction was detected for Clarksville 2014 and Kinston 2014 and none were detected for Clarksville 2013. QTL with epistatic and epistasis × environment interaction effects A total of 7 pairs of significant epistatic interactions (P<0.001) were identified across five environments for yield and yield components except for SPSM (Table 2.8). The epistatic interactions were observed within and across chromosomes (mostly in the A and B genome) with heritability ranging from 0.2% to 6% and 0.1% to 2.7% for epistatic and epistatic × environment interaction effects, respectively. The only significant epistatic × environment interaction identified in this study was in Queenstown 2013. Furthermore, two marker intervals Xbarc28-Xsnp2005 and Xsnp3253-Xsnp3349, had already been detected for significant additive effects (Table 2.6) while the rest were detected only for epistatic interactions. 50 Xwmc4960.0 Xsnp19700.9 Xbarc281.7 Xsnp200513.6 Xsnp199914.4 Xsnp212915.3 Xsnp199718.2 Xsnp185519.1 Xsnp93920.8 Xsnp145221.6 Xsnp189522.4 Xsnp198323.3 Xsnp194328.1 Xsnp183430.0 Xsnp200131.0 Xsnp195336.9 Xsnp194037.7 Xsnp199338.7 Xsnp131641.2 Xsnp197743.6 Xsnp184048.7 Xsnp188950.4 Xsnp186251.3 Xsnp182353.8 Xsnp193154.8 Xsnp198655.6 Xsnp199658.7 Xsnp195059.6 Xsnp186862.3 Xsnp199563.2 Xsnp222767.7 Q G ps.cz-1A .1 Q G ps.cz-1A .2 Q G w s.cz-1A.1 Q G w s.cz-1A.2 Q G w s.cz-1A.3 Q Ssm .cz-1A .1 Q Ssm .cz-1A .2 Q Ssm .cz-1A .3 Q Yld.cz-1A 1A Xsnp24770.0 Xsnp24322.7 Xsnp44906.8 Xsnp24457.9 Xsnp248014.0 Xsnp247114.9 Xsnp246117.6 Xsnp246622.8 Xsnp242723.6 Xsnp247925.3 Xsnp242326.3 Xsnp132729.1 Xsnp248131.6 Xsnp243634.4 Xsnp244839.8 Xsnp247547.3 Xsnp243148.1 Xsnp8749.0 Xsnp232349.8 Xsnp76850.7 Xsnp240051.5 Xsnp226852.4 Xsnp236653.3 Xsnp232054.2 Xsnp240455.0 Xsnp237755.9 Xsnp8159.1 Xsnp240960.0 Xsnp239061.7 Xsnp235162.6 Xsnp227764.2 Xsnp237265.1 Xsnp239466.0 Xsnp237568.0 Xsnp236569.0 Xsnp238273.7 Xsnp240175.6 Xsnp233978.7 Xsnp239779.6 Xsnp238181.7 Xsnp232182.7 Xsnp231387.0 Xsnp240587.8 Xsnp240688.8 Xsnp236392.9 Xsnp231593.8 Xsnp235396.3 Xsnp235097.3 Xsnp238398.2 Xsnp233799.1 Xsnp2355100.1 Xsnp2360102.9 Xsnp2362104.7 Xsnp494105.5 Xsnp2291108.0 Xsnp2398109.8 Xsnp533112.3 Q G ps.cz-2A Q Ssm .cz-2A.1 Q Ssm .cz-2A.2 Q Yld.cz-2A 2A Xsnp30270.0 Xsnp37440.9 Xsnp30481.8 Xsnp14663.6 Xsnp30494.4 Xsnp30218.8 Xsnp305912.5 Xbarc1213.4 Xsnp300814.3 Xsnp299323.2 Xsnp152225.1 Xsnp303426.7 Xsnp305127.7 Xsnp304628.6 Xsnp306431.4 Xsnp304032.2 Xsnp309433.1 Xsnp305634.0 Xsnp300534.9 Xsnp304145.1 Xsnp298879.1 Xsnp3065115.2 Xbarc45121.0 Xsnp3037123.6 Xsnp3023124.4 Xsnp3383126.1 Xsnp3009127.0 Xsnp3052127.8 Xsnp758128.7 Xsnp310129.5 Xsnp4288132.0 Xsnp1758134.7 Xsnp1485136.3 Xsnp2964137.1 Xsnp2885142.1 Xsnp2987143.0 Xsnp2937144.0 Xsnp4728147.9 Xsnp2968148.9 Xsnp443152.4 Xsnp2927153.3 Xsnp2924154.1 Xsnp2956155.9 Xsnp2950159.3 Xsnp3313160.2 Xsnp2977161.0 Xsnp2900161.9 Xsnp2973162.9 Xsnp2989168.0 Xsnp2983169.8 Xsnp2990173.9 Xsnp2905179.7 Xsnp2979180.7 Xsnp445181.5 Xsnp2984187.8 Xsnp2934188.7 Xsnp2970189.5 Xsnp2920197.5 Xsnp2960200.5 Xsnp2948201.4 Xsnp2951206.5 Xsnp2971215.9 Xsnp2909216.8 Q G ps.cz-3A.1 Q G ps.cz-3A.2 Q G ps.cz-3A.3 Q G ps.cz-3A .4 Q G w s.cz-3A Q Ssm .cz-3A Q Tgw .cz-3A .1 Q Tgw .cz-3A .2 Q Tgw .cz-3A .3 Q Tgw .cz-3A.4 Q Tgw .cz-3A .5 Q Tgw .cz-3A .6 Q Yld.cz-3A 3A 51 Xsnp38740.0 Xsnp38722.0 Xsnp38695.0 Xsnp38796.8 Xsnp387715.8 Xsnp62120.8 Xsnp174921.6 Xsnp136822.4 Xsnp27924.0 Xsnp387848.7 Xsnp61750.4 Xsnp84252.0 Xsnp21852.8 Xsnp4953.6 Xgwm30454.4 Xsnp99655.3 Xsnp383756.2 Xsnp383857.1 Xbarc10060.0 Xsnp484367.3 Xsnp381968.3 Xsnp378971.2 Xsnp384477.1 Xsnp385177.9 Xsnp416778.9 Xsnp376079.9 Xsnp383980.9 Xsnp381282.0 Xsnp385683.9 Xsnp377684.9 Xsnp385285.8 Xsnp384386.7 Xsnp382087.5 Xsnp200894.1 Xsnp447297.9 Xsnp385598.8 Xsnp85699.6 Xsnp3867101.2 Xsnp3836102.1 Xsnp3833102.9 Xsnp3863107.4 Xsnp3845108.2 Xsnp3865125.4 Xsnp3802127.1 Xsnp3783129.0 Xsnp3859134.4 Xsnp3853136.1 Xsnp3835143.1 Xsnp3761165.6 Xsnp3862170.5 Xsnp3787171.3 Xsnp663172.2 Xsnp3747174.0 Xsnp3849176.6 Xsnp3841186.2 Xsnp3803187.0 Xsnp3775188.0 Xsnp3860190.0 Q G w s.cz-5A Q Tgw .cz-5A.1 Q Tgw .cz-5A.2 Q Tgw .cz-5A.3 5A Xsnp42960.0 Xsnp427613.7 Xsnp424320.0 Xsnp429820.8 Xsnp427123.6 Xsnp443525.6 Xsnp428027.8 Xsnp424728.6 Xsnp425937.0 Xsnp428637.9 Xsnp429038.9 Xsnp429940.9 Xsnp424541.7 Xsnp36742.5 Xsnp427843.3 Xsnp448944.3 Xsnp426545.1 Xsnp418346.0 Xsnp421647.6 Xsnp421749.3 Xsnp422650.2 Xsnp422551.1 Xsnp421151.9 Xsnp418675.0 Xsnp422278.3 Xsnp49279.9 Xsnp419780.8 Xsnp47381.6 Xsnp422883.2 Xsnp421992.6 Xsnp7093.4 Xsnp436494.2 Xsnp420795.1 Q Ssm .cz-6A Q Yld.cz-6A 6A Xsnp47250.0 Xsnp47662.1 Xsnp13983.0 Xsnp47323.9 Xsnp47795.6 Xsnp47646.5 Xsnp47527.5 Xsnp47178.4 Xsnp47369.3 Xsnp472310.3 Xsnp472712.8 Xsnp466513.6 Xsnp468214.6 Xsnp471815.5 Xsnp475918.1 Xsnp477125.7 Xsnp477326.6 Xsnp477731.1 Xsnp474132.9 Xsnp473935.4 Xsnp469736.5 Xsnp476837.6 Xsnp130238.5 Xsnp476239.4 Xsnp475446.6 Xsnp473447.5 Xsnp466748.5 Xsnp471549.3 Xsnp472250.2 Xsnp469051.0 Xsnp466851.9 Xsnp474953.6 Xsnp32454.5 Xbarc12755.3 Xsnp137657.1 Xsnp474558.8 Xsnp477459.7 Xsnp476760.8 Xsnp476361.6 Xsnp474671.8 Xsnp467573.6 Xsnp477677.7 Xsnp84978.5 Xsnp477581.0 Xsnp155384.5 Xsnp167485.3 Xsnp477086.9 Xsnp473388.5 Xsnp475890.2 Xsnp475791.2 Xsnp476192.2 Xsnp477294.8 Xsnp454795.7 Xsnp453596.6 Xsnp4660101.0 Xsnp4637103.0 Xsnp4567105.4 Xsnp4946106.3 Xsnp4546107.1 Xsnp4624107.9 Xsnp4612109.6 Xsnp1385110.5 Xsnp4523112.1 Xsnp4584113.0 Xsnp4935114.7 Xsnp4622115.7 Xsnp4639117.4 Xsnp1181118.3 Xsnp321120.0 Xsnp4593120.8 Xsnp4588121.6 Xsnp4620123.3 Xgwm282124.1 Xsnp4563127.6 Xsnp4936129.3 Xsnp4602137.2 Xsnp4659138.1 Xsnp4658139.0 Xsnp4606149.1 Xsnp4570150.8 Xsnp4623152.6 Xsnp4536154.2 Xsnp4556155.1 Xsnp4557156.2 Xsnp4947159.1 Xsnp1727159.9 Xsnp4608160.9 Xwmc273167.4 Xsnp4655170.7 Xsnp4643171.5 Q Tgw .cz-7A .1 Q Tgw .cz-7A .2 Q Tgw .cz-7A.3 Q Tgw .cz-7A .4 Q Tgw .cz-7A .5 7A Xsnp36340.0 Xsnp36321.9 Xsnp362310.6 Xsnp105713.1 Xsnp361419.0 Xsnp363649.0 Xsnp357651.5 Xsnp350852.4 Xsnp363753.4 Xsnp359054.3 Xsnp344458.3 Xsnp31880.7 Xsnp30981.5 Xsnp360484.1 Xbarc17085.0 Xsnp83688.2 Xsnp356693.8 Xsnp359794.7 Xsnp91097.5 Xsnp354599.4 Xsnp3573100.3 Xsnp3574102.9 Xsnp753103.8 Xsnp3551104.6 Xsnp3490105.5 Xsnp3595111.6 Xsnp3462114.2 Xsnp3572115.1 Xsnp3568116.1 Xsnp3608116.9 Xsnp3535121.5 Xsnp3447122.5 Xsnp3464135.3 Xsnp3547138.4 Xsnp3606139.3 Xsnp3575140.2 Xsnp3542142.0 Xsnp3488146.5 Xsnp3607154.7 Xsnp3486155.7 Xsnp3602156.6 Xsnp3512157.4 Xsnp4975158.3 Q G ps.cz-4A 4A 52 Xsnp22000.0 Xsnp22210.9 Xsnp22173.8 Xsnp22057.8 Xsnp450312.4 Xsnp218118.5 Xsnp221819.4 Xsnp218021.3 Xsnp220922.1 Xsnp218623.1 Xsnp220624.2 Xsnp221325.1 Xsnp170730.9 Xgwm1131.9 Xsnp207132.8 Xsnp76733.8 Xsnp213034.6 Xsnp207339.6 Xsnp211049.2 Xsnp85450.0 Xsnp212150.8 Xsnp205352.7 Xsnp213254.5 Xsnp209258.9 Xsnp206459.7 Xsnp208760.6 Xsnp211561.4 Xsnp211763.1 Xsnp208064.0 Xsnp211464.9 Xsnp322766.8 Xsnp212867.7 Xsnp212668.6 Xsnp492871.2 Xsnp210774.1 Xsnp210675.1 Xsnp208484.5 Xsnp211386.3 Xsnp209187.1 Xsnp205287.9 Xsnp1777117.4 Xsnp2059124.8 Xsnp2131125.6 Xsnp2116128.9 Xsnp2067133.2 Xsnp2127141.7 Xbarc80144.1 Q Ssm .cz-1B.1 Q Ssm .cz-1B.2 Q Tgw .cz-1B.1 Q Tgw .cz-1B.2 Q Yld.cz-1B 1B Xsnp27790.0 Xsnp27500.9 Xsnp277110.2 Xsnp278017.3 Xsnp373419.9 Xsnp274324.7 Xsnp45625.5 Xsnp74633.7 Xsnp278534.5 Xsnp277836.2 Xsnp276946.4 Xbarc1047.3 Xsnp274458.3 Xsnp77459.1 Xsnp270560.0 Xsnp275261.1 Xsnp278662.2 Xsnp277763.2 Xsnp277364.1 Xgwm31966.1 Xsnp276767.0 Xsnp259169.0 Xsnp256970.9 Xsnp268871.8 Xsnp269772.6 Xsnp259874.2 Xsnp268675.1 Xsnp263576.9 Xsnp264677.8 Xsnp260778.6 Xsnp341379.5 Xsnp265980.4 Xsnp269882.0 Xsnp257186.4 Xsnp266787.4 Xsnp266888.3 Xsnp269689.1 Xsnp251590.0 Xsnp117690.8 Xsnp269492.5 Xsnp264893.5 Xsnp261594.5 Xsnp268295.5 Xsnp261997.4 Xsnp267098.3 Xsnp2523100.2 Xsnp2620102.1 Xsnp2665103.8 Xsnp2633105.8 Xsnp2614107.7 Xsnp2649109.9 Xsnp2585115.9 Xsnp681116.8 Xsnp2666119.4 Xsnp2723122.1 Xsnp2574123.7 Xsnp448125.4 Xsnp2603130.3 Xsnp2586132.3 Xsnp2600133.9 Xsnp2679134.8 Xsnp2599137.1 Xsnp2802138.1 Xsnp2496139.0 Xsnp2663139.8 Q G ps.cz-2B Q Tgw .cz-2B 2B Xsnp30740.0 Xsnp5991.7 Xsnp34074.3 Xbarc1475.1 Xsnp33289.2 Xsnp342113.5 Xsnp331815.5 Xsnp315617.4 Xsnp340418.2 Xsnp341119.3 Xsnp339920.3 Xsnp307021.3 Xsnp328822.4 Xsnp340523.3 Xsnp338927.2 Xsnp334431.0 Xsnp325335.3 Xsnp334941.8 Xsnp336742.7 Xsnp332043.5 Xsnp333544.3 Xsnp311946.3 Xsnp339547.3 Xsnp338748.1 Xsnp341549.9 Xsnp153951.6 Xsnp340852.4 Xbarc16453.2 Xsnp320555.1 Xsnp338555.9 Xsnp314656.7 Xsnp338257.6 Xsnp337260.9 Xsnp61161.9 Xsnp319262.7 Xsnp338163.6 Xsnp339364.5 Xsnp336866.2 Xsnp315367.1 Xsnp88768.8 Xsnp341072.5 Xsnp341673.4 Xsnp338674.3 Xsnp324775.4 Xsnp331276.2 Xsnp169779.6 Xsnp18780.4 Xsnp334281.3 Xsnp311285.6 Xsnp386487.3 Xsnp329988.2 Xsnp334593.1 Xsnp328993.9 Xsnp316097.3 Xsnp317498.1 Xsnp3397101.5 Xsnp3417102.4 Xsnp3418107.2 Xsnp3175109.0 Xsnp3401128.2 Xsnp3358129.1 Xsnp3400130.0 Xsnp326130.9 Xsnp3406131.8 Xsnp3403132.8 Xsnp3181134.0 Q G ps.cz-3B.1 Q G ps.cz-3B.2 Q G ps.cz-3B.3 Q Ssm .cz-3B .1 Q Ssm .cz-3B.2 Q Ssm .cz-3B .3 Q Ssm .cz-3B .4 Q Yld.cz-3B.1 Q Yld.cz-3B .2 3B 53 Xsnp37300.0 Xsnp373917.1 Xsnp373225.9 Xsnp374136.9 Xsnp374251.6 Xsnp373657.6 Xsnp374058.7 Xsnp365661.1 Xsnp373764.2 Xsnp375166.8 Xsnp366068.5 Xsnp372770.2 Xsnp368871.1 Xsnp365272.1 Xsnp369973.0 Xsnp370573.9 Xsnp372174.8 Xsnp165677.3 Xsnp143078.2 Xsnp372979.0 Xsnp493079.9 Xbarc16380.9 Xsnp363882.0 Xsnp136684.6 Xsnp372586.4 Xsnp364588.4 Xsnp3716133.6 Xsnp3711134.5 Q Tgw .cz-4B 4B Xsnp44340.0 Xsnp43810.9 Xsnp44562.7 Xsnp1073.6 Xsnp44454.5 Xsnp44445.6 Xsnp44539.7 Xsnp443714.1 Xsnp443315.0 Xsnp443616.7 Xsnp441917.5 Xsnp445820.3 Xsnp439821.2 Xsnp440223.0 Xsnp442028.1 Xsnp442436.4 Xsnp441341.4 Xsnp438048.8 Xsnp441857.8 Xbarc10160.7 Xsnp479361.6 Xsnp442162.5 Xsnp445164.2 Xsnp168365.0 Xsnp440565.8 Xsnp435866.8 Xsnp431287.9 Xsnp143893.8 Xsnp41696.2 Xsnp170398.6 Xsnp4326101.4 Xsnp4360106.4 Xsnp4359108.0 Xsnp4352108.9 Xsnp1582110.7 Xsnp4357112.4 Q G w s.cz-6B Q Ssm .cz-6B Q Yld.cz-6B 6B Xsnp48750.0 Xsnp49254.1 Xsnp48865.1 Xsnp492113.9 Xsnp169318.2 Xsnp490619.0 Xsnp487421.1 Xsnp491327.4 Xsnp492433.4 Xsnp489535.1 Xsnp40045.3 Xsnp491447.0 Xsnp492356.6 Xsnp492757.6 Xsnp48958.4 Xsnp492959.4 Xsnp488360.3 Xsnp480862.1 Xsnp83862.9 Xsnp485263.8 Xsnp494384.2 Xsnp483087.1 Xsnp487291.2 Xsnp486992.1 Xsnp485592.9 Xsnp484094.0 Xsnp478095.1 Xsnp4865101.1 Xsnp4867102.8 Xsnp881103.6 Xsnp4833105.4 Xsnp4839106.3 Xsnp4810108.3 Xsnp4864109.1 Xsnp2525130.3 Xsnp4860137.3 Xsnp4831151.6 Xsnp1066152.4 Xsnp4801153.2 Xsnp4785154.1 Xsnp4838155.1 Xsnp1065155.9 Xsnp4804157.6 Xsnp4940177.8 Q G w s.cz-7B Q Tgw .cz-7B.1 Q Tgw .cz-7B.2 Q Tgw .cz-7B.3 7B Xsnp41400.0 Xsnp41140.9 Xsnp414211.4 Xsnp4812.2 Xsnp415213.8 Xsnp62515.6 Xsnp415016.6 Xsnp415517.7 Xsnp412018.7 Xsnp413019.5 Xsnp388420.3 Xsnp407921.2 Xsnp401622.1 Xsnp396423.0 Xsnp33025.0 Xsnp388225.8 Xsnp416827.4 Xsnp402828.3 Xsnp390129.5 Xsnp399730.5 Xsnp409231.3 Xsnp72532.2 Xsnp400533.0 Xsnp401834.1 Xsnp401435.0 Xsnp397536.7 Xsnp396337.7 Xsnp389138.8 Xsnp397344.9 Xsnp406245.9 Xsnp44646.7 Xsnp408347.6 Xsnp398857.5 Xsnp100658.3 Xsnp82359.1 Xsnp405964.8 Xsnp406166.4 Xsnp402768.1 Xsnp401769.8 Xsnp404971.5 Xsnp409072.3 Xsnp406073.2 Xsnp407274.2 Xsnp408578.2 Xsnp405879.2 Xsnp405080.9 Xsnp402082.6 Xsnp406883.6 Xsnp401294.7 Xsnp135696.5 Xsnp54998.9 Xsnp3983100.6 Xsnp797101.5 Xsnp794102.3 Xsnp4086103.2 Xsnp4089104.1 Xsnp29106.7 Xbarc59108.4 Xsnp4077113.0 Xsnp4011114.7 Xsnp4073117.9 Xsnp3970118.8 Xsnp4753122.4 Xsnp435123.2 Q G ps.cz-5B.1 Q G ps.cz-5B.2 Q G ps.cz-5B.3 Q G ps.cz-5B.4 Q G w s.cz-5B Q Ssm .cz-5B .1 Q Ssm .cz-5B .2 Q Yld.cz-5B.1 Q Yld.cz-5B.2 5B 54 Figure 2.1 Genetic linkage map and position of quantitative trait loci (QTLs) detected in a doubled haploid mapping population derived from MD01W233-06-1 × SS8641. Locus marker names are shown on the right side of the chromosomes and values to the left of chromosomes show the genetic distance (cM) for each marker. QTLs are labeled with trait abbreviations and the QTL number for each trait. QTLs for the same trait are in the same color. Table 2.7 QTL × Environment interactions influencing grain yield (GYLD), grains per spike (GPS), grain weight per spike (GWPS), spikes per square meter (SPSM), and thousand-grain-weight (TGW) in five environments during 2013 and 2014. Trait Chr. Position Interval AE1† AE2 † AE3 † AE4† AE5† h 2(ae) ‡ GYLD 2A 23.6 Xsnp2427-Xsnp2479 -15.27* 12.78* 0.002 GYLD 2A 88.8 Xsnp2406-Xsnp2363 19.00** 0.030 GPS 1A 3.7 Xbarc28-Xsnp2005§ 0.52* -0.66** 0.274 SPSM 1A 1.7 Xbarc28-Xsnp2005§ 12.87* 0.217 SPSM 3B 46.2 Xsnp3119-Xsnp3395§ -14.30* 0.099 † AE is the additive × environment interaction effect at each environment. E1: Clarksville 2013; E2: Clarksville 2014; E3: Queenstown 2013; E4: Queenstown 2014; E5: Kinston 2014. ‡ h2 (ae) is heritability estimate of the additive × environment interaction effect across five environments. § Interval with significant additive effect. * Significantly different from zero at the 0.05 probability level. ** Significant difference from zero at the 0.01 probability level. Xsnp41770.0 Xsnp41793.5 Xsnp17516.7 Xsnp417522.1 Xsnp19823.8 Xsnp415624.6 Xsnp417327.1 Xsnp417828.1 Xgdm13647.9 Xsnp417256.3 Xsnp417178.9 Xsnp87689.5 Xsnp417094.6 Xsnp4157117.6 Xsnp1271124.4 Xsnp4163125.3 Xsnp4162139.4 Xsnp4169165.6 Xsnp4166178.6 Xsnp553179.5 Q Tgw .cz-5D 5D Xsnp45210.0 Xsnp45011.0 Xsnp45135.1 Xsnp450814.2 Xsnp449629.2 Xsnp450430.0 Xsnp451530.9 Xsnp451231.9 Xsnp451851.4 Xsnp448260.5 Xsnp81466.4 Xsnp447067.3 Xsnp449171.3 Xsnp4484130.0 Xsnp4488131.8 Xsnp4485136.1 Xsnp4465137.0 Xsnp4487138.8 Xsnp4468139.7 Xsnp4483141.4 Xsnp4486142.2 Xsnp230144.6 Q Ssm .cz-6D Q Yld.cz-6D 6D Xsnp49770.0 Xsnp49760.9 Xsnp49677.3 Xsnp497862.2 Xsnp495470.6 Xsnp497386.9 Xsnp342093.4 XRc106.7 Xsnp4942118.0 Xsnp4945118.9 Xsnp4932119.7 Xsnp4966121.5 Xgwm111122.5 Xsnp4937125.8 Xsnp1759145.7 Xsnp4949233.6 Xsnp4941235.5 Xsnp4934241.4 Xsnp4944244.9 Xsnp4948256.4 Xsnp420259.8 7D Xsnp13040.0 Xsnp22524.3 Xsnp6065.2 Xsnp223814.6 Xsnp223717.2 Xsnp164218.0 Xsnp224022.4 Xsnp223926.5 Xsnp225127.3 Xsnp225550.0 Xsnp225650.8 Xsnp224452.6 Xsnp222953.4 Xsnp223555.3 Xsnp223160.0 Xsnp222468.3 Xsnp223473.3 Xsnp223285.6 1D Xsnp28700.0 Xsnp28545.8 Xsnp28199.4 Xsnp286812.5 Xsnp288113.4 Xsnp282319.6 Xsnp288220.5 Xgwm26125.7 Xsnp281027.5 Xsnp287528.5 Xsnp285029.4 Xsnp286247.0 PpdD161.7 Xsnp286976.4 Xsnp284477.4 Xsnp287778.5 Xsnp284879.5 Xsnp63480.4 Xsnp286381.3 Xsnp287682.1 Xsnp52285.6 Xsnp280888.5 Xsnp280489.4 Xsnp176691.2 Xsnp280995.5 Xsnp280696.4 Xsnp17999.7 Xsnp2790105.0 Xsnp2807107.8 Xsnp2788108.7 Xsnp2805109.5 Xsnp2795112.5 Xsnp708119.4 Xsnp1745125.9 Q Ssm .cz-2D Q Yld.cz-2D 2D Xsnp49800.0 Xsnp343116.6 Xsnp343418.6 Xsnp342528.1 Xsnp342735.0 Xsnp343035.8 Xsnp343236.7 Xsnp94238.3 Xsnp78639.1 Xsnp341947.6 Xsnp342256.0 Xsnp318772.7 Q G ps.cz-3D Q Ssm .cz-3D 3D Xsnp37530.0 Xsnp375210.2 Xsnp375414.0 Xsnp498155.8 Xsnp375062.6 Xsnp374863.4 Xsnp374376.8 4D 55 Table 2.8 Chromosome locations of digenetic epistatic QTLs for grain yield (GYLD), grains per spike (GPS), grain weight per spike (GWPS), spikes per square meter (SPSM), and thousand-grain-weight (TGW) in five environments in 2013 and 2014. Trait Interval† Chr† Position† Interval‡ Chr‡ Position‡ AA§ E1¶ E2¶ E3 ¶ E4 ¶ E5¶ h2(aa) # h2(aae) †† GYLD Xsnp4749-Xsnp324 7A 53.6 Xsnp3312-Xsnp1697 3B 76.2 23.79*** 0.2% 2.7% GYLD Xsnp4171-Xsnp876 5D 83.9 Xsnp4518-Xsnp4482 6D 56.4 17.11*** 2.5% 0.9% GPS Xbarc28-Xsnp2005‡‡ 1A 3.7 Xsnp1006-Xsnp823 5B 58.3 -0.50*** 1.5% 0.5% GPS Xsnp4715-Xsnp4722 7A 49.3 Xsnp2780-Xsnp3734 2B 17.3 -0.46*** 1.5% 0.6% GWPS Xsnp2956-Xsnp2950 3A 155.9 Xsnp2571-Xsnp2667 2B 86.4 0.03*** 3.5% 0.9% TGW Xsnp4050-Xsnp4020 5B 80.9 Xsnp4451-Xsnp1683 6B 64.2 0.29*** 3.1% 0.1% TGW Xsnp3253-Xsnp3349‡‡ 3B 35.3 Xsnp3175-Xsnp3401 3B 120 0.65*** 6.0% 0.1% TGW Xsnp3645-Xsnp3716 4B 114.4 Xsnp4948-Xsnp420 7D 256.4 -0.58*** 4.3% 0.1% † The flanking markers, chromosome and position of the first interval involved in the epistasis. ‡ The flanking markers, chromosome and position of the second interval involved in the epistasis. § The additive × additive effect. ¶ The epistasis × environment effect at each environment. E1: Clarksville 2013; E2: Clarksville 2014; E3: Queenstown 2013; E4: Queenstown 2014; E5: Kinston 2015. # The heritability estimates for additive × additive interaction effects across five environment. †† The heritability estimates for epistasis × environment interaction effects across five environments. ‡‡ Interval with significant additive effect. *** Significantly different from zero at the 0.001 probability level. ► Figure 2.2 Distribution of genetic and non-genetic components for grain yield and yield related traits: grain yield (GYLD), spikes m-2 (SPSM), grains per spike (GPS), grain weight per spike (GWPS), thousand-grain-weight (TGW). a) total number of QTLs detected for additive (a), additive × environment (ae), epistasis (aa), and epistasis × environment interactions (aae) effects. b) relative magnitude of a, ae, aa, and aae effects. 56 Discussion Grain yield and yield components are complex quantitative traits determined by genetic components, environmental factors, and the interaction between them (Cooper et al., 2009; Eeuwijk, 2008; Holland, 2007; Mackay, 2001). In this study, I used a mixed linear model to investigate the genetic basis of grain yield and yield components in a DH population of 124 lines by dividing genetic effects into additive main effect (A), additive × additive epistatic main effects (A×A or Q×Q), and their environmental interaction effects (A×E, AA×E or Q×E, QQ×E) (Wang et al., 1999; Yang et al., 2007). As single environment experiment could underestimate the number of QTL controlling a certain trait, whereas repeated experiments are useful in detecting stable QTL over environments (Paterson et al., 1991), I evaluated the DH population and parents in five environments in the US East Coast. For genotyping, I relied mostly on SNPs by using the 9K SNP array and GBS in addition to SSRs to get more coverage of the wheat genome. The map contained 4972 polymorphic markers and is highly consistent with the previously published 9K SNP consensus map (Cavanagh et al., 2013). The rank correlation coefficient between them was as high as 0.99 for most chromosomes in terms of SNP order (data not shown). Furthermore, the average interval length of the map (2.3 cM) was much smaller than that observed in previous studies (Carter et al., 2011; Heidari et al., 2011; Li et al., 2013), indicating a better resolution. 57 QTLs for grain yield In this study, grain yield was defined as yield m-2 as reported by previous researchers (Heidari et al., 2011; Lopes et al., 2013). Twelve grain yield QTLs were detected (Table 2.6). Both parents carried favorable QTL alleles. Seven loci of MDW233 alleles increased grain yield on 1A, 3A, 3B, 5B, 6D, accounting for 7.5 to 21.3% of the phenotypic variation. The SS8641 alleles were associated greater grain yield at the other five loci on 2A, 6A, 5B, 6B, 2D, accounting for 9.7 to 17.7% of the phenotypic variation. Grain yield was the only trait that had no stable QTL detected over five environments in this study. This was expected as similar results were obtained by Kumar et al. (2007) and Li et al. (2007b), verifying that grain yield is strongly influenced by environment. Furthermore, QTL is a genomic region that may contain several functional genes or sub-QTLs that are closely linked and may have opposite genetic effects and as well as being subject to environmental influences (Mackay et al., 2009). The detection of a QTL indicates that the net effects of all sub- QTLs within it are significant whereas a non-significant QTL may still contain significant sub-QTLs (Mackay et al., 2009). Therefore, increasing marker density and population size would allow for the discovery of more QTLs as well as develop more detailed insights into the genetic basis of quantitative traits in this DH population. Eight QTL (QYld.cz-1B, QYld.cz-3B.1, QYld.cz-3B.2, QYld.cz-5B.1, QYld.cz-5B.2, QYld.cz-6B, QYld.cz-2D, QYld.cz-6D) explained more than 10 % of the phenotypic variation of grain yield (Table 2.6). The QTL QYld.cz-3B.2 was detected at 58 Clarksville 2014 with LOD=6.8 and the effect of MDW233 allele was very large accounting for the highest genetic variation for gain yield (PVE=21.3%). In this region, Bennett et al. (2012a) and Bennett et al. (2012b) also reported QTLs for grain yield, spike length, thousand grain weight repeatedly in heat, drought and high yield potential environments and Zhang et al. (2010) identified two meta-QTLs for grain yield and yield associated traits in a meta-QTL analysis based on 59 independent studies. Other studies also identified QTLs for plant height, harvest index, isotope discrimination, and canopy temperature in this region (Bennett et al., 2012b; Cuthbert et al., 2008; Kumar et al., 2007; Rebetzke et al., 2008). To date, the region where QYld.cz-3B.2 resides appears to have pleiotropic effect on grain yield and should be given high priority for fine mapping and candidate gene identification, so that diagnostic gene-specific markers can be developed and utilized within breeding programs. QYld.cz-2D (LOD=4.5148, PVE=17.7%) was flanked by Xsnp2862 and Ppd-D1. Ppd-D1 is a photoperiod-sensitivity gene that largely confers wheat dominant insensitivity to short day length. It enhances grain yield by allowing earlier heading under the short days of spring so that grain-filling can occur before heat and drought stress often associated with late summer (Nelson et al., 2006). Moreover, a recent study showed that Ppd-D1 controlled photoperiod dependent floral induction in wheat and had a major inhibitory effect on paired spikelet formation by regulating the expression of FLOWERING LOCUS T (FT) (Boden et al., 2015). The yield- increasing effect of QYld.cz-2D may be due to the pleiotropic effect of Ppd-D1. 59 QYld.cz-3B.1 (LOD=4.6, PVE=10.9%) was mapped on the short arm of chromosome 3B and may be related to the one detected by Li et al. (2007b). Another well-known QTL, Qfhs.ndsu-3BS (also known as resistance gene Fhb1), is located in the same region (Schweiger et al., 2013). This suggests a possible new way to improve wheat disease resistance and grain yield by deploying this genomic region in breeding lines. Additionally, QTL QYld.cz-1A was in a region similar to that identified by Heidari et al. (2011) which controlled the expression of both grains per spike and grain weight per spike. Previous studies also detected QTL for grain yield in similar regions for QYld.cz-3A (Campbell et al., 2003; Mengistu et al., 2012), QYld.cz-1B (Huang et al., 2003), QYld.cz-5B.1 (Bennett et al., 2012b), QYld.cz-6A (Kuchel et al., 2007b; Simmonds et al., 2014), QYld.cz-6B (Marza et al., 2006), and QYld.cz-6D (Kumar et al., 2007). Yield QTLs reported by Kumar et al. (2007) and Groos et al. (2003) were located in a region more than 10 cM away from QYld.cz-2A and QYld.cz-5B.2, respectively. This suggests that QYld.cz-2A and QYld.cz-5B.2 may be new QTLs or this could be due to the difference in linkage map resolution. QTLs for yield components In this study, TGW had the highest heritability and number of QTLs among all traits evaluated (Table 2.4 and 2.6). Of the twenty-two QTLs identified, four were detected in more than one environment. They were located on chromosomes 5A, 1B, 2B, and 7B. However, the strongest QTL was on chromosome 7A, designated as QTgw.cz- 7A.5, and explained up to 71.2% of the variation of TGW in Queenstown 2013. The positive allele for this QTL was from MDW233, the parental line with the lower 60 TGW. Similarly, Groos et al. (2003) also reported a stable QTL in this region for TGW explaining 5.2 to 10.3% of the phenotypic variation across seven trials. Thus, QTgw-7A.5 may be the underlying QTL in both studies. Four QTL clusters were found on 3A, 1B, 5A and 7B as QTLs on those chromosomes were detected in proximity to each other and exhibited the same direction of genetic effects (Cai and Morishima, 2002). Specifically, favorable allelic clusters on 3A and 1B came from MDW233 while those alleles from SS8641 were associated with higher TGW for the allelic clusters on 5A and 7B. Extensive studies have focused on 3A, which is known to contain QTL/genes controlling grain yield and associated traits, and several loci for TGW were identified (Dilbirligi et al., 2006; Mengistu et al., 2012; Rustgi et al., 2013). However, after a close comparison of previous results, I found that QTgw.cz- 3A.6 appeared to be a new QTL for TGW since no TGW QTLs were reported in this region before. Clusters/QTL have also been reported in similar regions for the ones on 3A (Huang et al., 2004), 5A (Cuthbert et al., 2008), 7B (Hai et al., 2008; Huang et al., 2003),1B (Huang et al., 2004), QTgw.cz-2B (Hai et al., 2008), QTgw.cz-5D (Li et al., 2007b). QTLs for GPS have been identified on all wheat chromosomes (Tang et al., 2011; Wu et al., 2012; Zhang et al., 2010). In the present study, several major GPS QTLs on 1A, 3A and 5B were detected and formed QTL clusters. The QTL cluster on 1A was detected in all five environments and explained 18.2 to 44% of the phenotypic variation. Heidari et al. (2011) found the same region significantly associated with GPS but with less PVE. The QTL cluster (QGps.cz-3A.1 and QGps.cz-3A.2) at the 61 distal end short arm of 3A is comparable with the region identified for GPS, GYLD, and TGW by Campbell et al. (2003). QGps.cz-3A.2 had the most influence on GPS (PVE=52%) and its SS8641 allele decreased GPS, which was opposite to the effect of QGps.cz-3A.1. This may be due to environmental difference and Q × E interaction where QGps.cz-3A.1 was detected in a warmer location with more precipitation whereas QGps.cz-3A.2 was detected in a cooler location with less precipitation. The other cluster on 3A was located next to Xbarc45, a marker 8 cM away from Xwmc664 according to the high-density microsatellite consensus map (Somers et al., 2004). Mengistu et al. (2012) found Xwmc664 to be the most significant marker for GPS QTL QKps.neb-3A.1 in a recombinant inbred line population derived from cultivar Cheyenne and its 3A substitution line and that QKps.neb-3A.1 was in a cluster with nearby QTL. Therefore, it is supposed that the cluster on 3A may represent the same cluster identified by Mengistu et al. (2012). Moreover, Li et al. (2007b) detected two QTLs at the distal end of 3AS. Its estimated position on the microsatellite consensus map was approximately 40 cM based on the information of Xgwm77 (Somers et al., 2004). The cluster identified in this study (QGps.cz-3B.1, QGps.cz-3B.2, and QGps.cz-3B.3) was positioned in the same region, suggesting those two clusters may be the same. Another cluster of QTLs with similar influence on GPS was on 5B and the genetic effects of those QTLs were in the same direction. Marza et al. (2006) detected the same region in six environments explaining 18.5% of the phenotypic variation of GYLD. These findings suggested that this cluster improved grain yield by modifying GPS. Additionally, QTLs for GPS or other yield traits have been reported in or close to QGps.cz-2A (Li et al., 2007b), QGps.cz-2B (Marza et al., 62 2006), QGps.cz-4A (Kirigwi et al., 2007), QGps.cz-4A(Huang et al., 2004), and QGps.cz-3D (Quarrie et al., 2005). Multiple environment experiments allowed the identification of eight QTLs for GWPS in five environments (Table 2.6). Three closely linked QTLs in which SS8641 alleles increased GWPS were located on 1A and were the strongest QTL associated with GWPS explaining 15.4 to 33.2% of the phenotypic variation. Additional evidence of QTL for GWPS was reported in 3A, 5A and 6B (Zhang et al., 2010). A summary of preceding studies showed that chromosomes 5B and 7B had the fewest number of QTL for yield and yield associated traits (Zhang et al., 2010). Thus, it was not surprising to find no QTLs previously reported in the region of QGws.cz-5B and QGws.cz-7B. Under current agricultural production systems, improving spikes m-2 or grains m-2 rather than other yield components has been generally agreed to be the key to raising grain yield potential worldwide (Gaju et al., 2009). Therefore, QTL analysis for SPSM has been the target of many studies. Heidari et al. (2011) reported QTLs for SPSM on 1A, 7A, and 2D in a DH population. The QTLs they reported on 1A are comparable to the ones identified at the distal end of 1AS in this study. Marza et al. (2006) found 1B, 4A, 7B and 7D to be associated with SPSM in a wheat population derived from Ning7840 × Clark, where the QTL on 1B was located in the similar region of Qsm.cz-1B.1 I detected in this study. Similarly, additional SPSM QTLs were located in the region previously described by Huang et al. (2004) on 63 chromosome 1B, Groos et al. (2003) on chromosome 3B, Bennett et al. (2012b) on chromosomes 3B and 5B, Campbell et al. (2003) on chromosome 3A, Huang et al. (2003) on chromosomes 2A, 2D, and 6D. Pleiotropic effects of QTLs Correlated traits are often affected by pleiotropic effects of the same QTL/gene(s) or closely linked QTL/gene(s), which would enable the selection of a complex trait via a closely correlated single trait (Hai et al., 2008). In the present study, a significant positive correlation was observed between GYLD and SPSM in all five environments (Table 2.3) and five loci with genetic effect of same direction were detected for GYLD and SPSM (Table 2.6). Favorable alleles came from both parents. The MDW233 allele increased GYLD and SPSM at QYld.cz-1A/QSsm.cz.-1A.1, QYld.cz- 1B/QSsm.cz.-1B.2, and QYld.cz-6D/QSsm.cz.-6D while the SS8641 allele improved GYLD and SPSM jointly at QYld.cz-2A/QSsm.cz.-2A.1 and QYld.cz-2D/QSsm.cz.-2D. The negative correlation between SPSM and both GWPS and GPS may be due to the pleiotropic effects of loci flanked by Xbar28-Xsnp2005 which increased SPSM but decreased GWPS and GPS or vice versa. These findings supported the existence of a QTL with pleiotropic effect and provided a genetic explanation of observed phenotypic correlation. Although a significant positive correlation was also observed between GYLD and TGW, no pleiotropic QTL was detected. This may indicate that the expression of GYLD was through TGW and conditional mapping was needed to investigate the underlying mechanism (Zhu, 1995). 64 Q×E and QQ×E interactions Generally, a QTL with low or no Q × E interaction can be utilized in a broad range of environments, whereas a QTL with significant Q × E interaction can only be used in the specific environment in which it is detected (Zhao and Xu, 2012). In this study, the DH population was evaluated in five environments spanning two crop years. Two loci for SPSM, Two for GY and 1 for GPS showed significant additive × environment interaction. The majority of the significant Q × E effects were found in Queenstown 2013 and Queenstown 2014. Those two environments had relative higher precipitation and average monthly temperature during the growing season indicating that high rainfall and temperature may contribute to Q × E expression in this study. The intervals Xsnp2427-Xsnp2479 and Xbar28-Xsnp2005 were detected for Q × E interactions in two environments with opposite effects confirming that the QTL effects were subject to change due to environments and that the environment suitable for the expression of one QTL may not be suitable for another QTL. The SS8641 allele of the locus located in Xbar28-Xsnp2005 was found to be pleiotropic in Queenstown 2014. It increased GPS but decreased SPSM. However, its Q × E interaction effects were opposite which decreased GPS and increased SPSM, suggesting that the additive effect alone was not enough to characterize the genetic effect of this QTL. It was also apparent that only a small portion of QTL with additive main effect was involved in Q × E interaction. This suggests that a QTL with no main effects can exercise its effect through interaction with the environment. Therefore, to develop genotypes for target environments or genotypes with broad 65 adaptation, the Q × E interaction should be investigated and assessed in plant breeding programs (Basford and Cooper, 1998; El-Soda et al., 2014). Epistasis has long been recognized to describe a situation where the effect of a particular genotype depends on the genetic background or generally as an interaction between a pair of loci, in which the phenotypic effect of one locus depends on the genotype at the second locus (Bocianowski, 2013; Carlborg and Haley, 2004). Understanding epistasis has been regarded as a necessity to characterize the genetic basis of complex traits (Carlborg and Haley, 2004; Phillips, 2008). Although epistasis was not well investigated in most previous QTL-mapping studies in wheat and its effect may not be as significant (Bennett et al., 2012a; Carter et al., 2011; Heidari et al., 2011; Marza et al., 2006; Mengistu et al., 2012), ignoring epistasis could affect the efficiency and accuracy of MAS as a result of overestimating or underestimating QTL effects (Bocianowski, 2013; Carlborg and Haley, 2004). Also, a simulation study showed that the genetic advance of selection on additive effects became fixed after several cycles of selection when epistasis was present (Wang et al., 2004). In this study, 7 pairs of significant epistatic interactions influencing grain yield and yield components were detected (Table 2.8). However, only two intervals/loci were detected by ICIM-ADD. This suggested many intervals in two locus analysis may escape detection by ICIM-ADD. Kumar et al. (2007) reported similar results and pointed out that this phenomena was more conspicuous in some populations and was perhaps also due to density of map used for QTL analysis. The fact that most epistasis involved only QTL with no main effects indicated that 66 epistasis between non-significant loci may be an important genetic basis of grain yield and yield components in wheat. This has also been found in maize and rice (Li et al., 1997; Ma et al., 2007a; Xing et al., 2014). Besides, it should be noted that the effects of some significant locus (e.g. the one located in Xbarc28-Xsnp2005) was completely changed to the opposite direction through interaction with another locus (e.g. the one located in Xsnp1006-Xsnp823), implying the need to account for epistasis to avoid an inflated estimate of the net QTL effect. In contrast, the genetic effect of QTL located in Xsnp3253-Xsnp3349 was enhanced by interacting with the one in Xsnp3175-Xsnp3401 suggesting that pyramiding QTL/genes could further improve the trait of interest when the direction of epistatic effect among QTL/genes is in the same direction with the additive effects of each QTL/gene involved. Although both additive and epistatic effects contributed to the phenotypic performance of grain yield and yield components, the contribution from significant epistasis was much smaller compared to that from additive loci for all traits investigated in this study (Figure 2.2), suggesting the essential role of additive main effects in determining yield and yield components in the current DH population and potential targets for MAS. This agreed with recent studies on rice, barley and wheat, where significant epistatic effects for yield and yield components were small in magnitude relative to the additive effects (Wu et al., 2012; Xing et al., 2002; Xu and Jia, 2007; Zhuang et al., 2002). And the low percentage of phenotypic variance explained by epistasis is largely due to a large number of QTLs with small effects (Wu et al., 2012). This might also explain why Q×E and QQ×E interactions were not 67 examined by researchers in some recent studies in wheat (Carter et al., 2011; Heidari et al., 2011; Kato et al., 2000; Marza et al., 2006). Conclusion In the current study the genetic basis of grain yield and yield components in a DH population was investigated by QTL mapping. Significant QTLs for GYLD, GWPS, GPS, SPSM, and TGW were detected almost on every wheat chromosome confirming the general involvement of loci (major QTL clusters and scattered minor QTLs) across the whole genome in the expression of yield and yield components. Although additive main effects, additive × additive epistatic main effects, and their interactions with environments all served as genetic determinants of grain yield and yield components, the additive main effects were the major contributors in this DH population and the magnitude and directions of QTL effects may change due to epistasis and QTL× environment interactions. Additionally, the observed phenotypic correlations between yield and yield components in this study were possibly caused by pleiotropy from QTLs located on 1A, 2A, 2D and 6D. Moreover, a major gene such as Ppd-D1 was involved in the expression of grain yield per se. Finally, major QTLs identified in this study such as QYld.cz-3B.2 for GYLD and QTgw.cz-7A.5 for TGW could be utilized by breeders for MAS and QTL fine mapping. 68 Chapter 3: Quantitative trait loci mapping of plant architecture traits in a doubled haploid population of soft red winter wheat Abstract Higher wheat grain yields are required to feed an increasing population. An optimized plant architecture may play a crucial role in increasing grain yield. Quantitative trait loci (QTLs) analysis was conducted in a doubled haploid (DH) population to study the genetic basis of plant architecture traits (plant height, PHT; flag leaf length, FLL; flag leaf width, FLW; flag leaf area, FLA; Flag leaf shape (length/width ratio), FLS) across six year-location trials. The DHs showed normal distribution with transgressive segregation, suggesting that plant architecture traits are controlled by polygenes. Seventy four QTLs were detected on all wheat chromosomes. Twenty were for PHT, thirteen were for FLL, sixteen were for FLW, twelve were for FLA, and eleven were for FLS. Major QTLs such as QPht.cz-2D.2 and QTL clusters on chromosome 2D, 3B, 6A etc. are first reports for plant architecture traits. These QTLs provide useful information for understanding the genetic mechanisms regulating plant architecture in wheat and for marker-assisted selection in designing desirable plant height and flag leaf morphology to increase yield. Introduction Plant architecture involves several traits, such as plant height, tillering, branching patterns, leaf size and shape, configuration of leaf relative to the sun and spatial arrangement of leaves (Fageria et al., 2006) and is closely associated with photosynthetic ability and grain yield in wheat (Triticum aestivum L.) (Hedden, 2003). Under high soil fertility conditions, the stems of tall plants are generally 69 unable to support the resultant weight of plump grains and fall over in the field before maturity, a process known as lodging, with consequent large yield losses (Hedden, 2003). This situation was greatly improved after the introduction of dwarfing genes into cereal crops, such as Rht-B1b and Rht-D1b in wheat and sd1 in rice which produce semi-dwarf plants with short strong stalks as well as more assimilate partitioned into the grain, leading to large yield increases in wheat and rice known as Green Revolution (Hedden, 2003). However, extremely short plants are disadvantageous because leaves are very closely spaced on a short stem causing increased shading within the canopy, as well as poor ventilation and light transmission in the lower canopy, which affects grain filling and decreases grain yield (Yoshida, 1972; Zhang et al., 2011). Thus, appropriate plant height is a requirement for achieving the desired yield level in wheat breeding programs. The closest leaf from the spike, the flag leaf, is the primary source of assimilates for grain filling and thus grain yield and it also stays green longer than other leaves (Ali et al., 2010). Translocation of carbohydrates from the flag leaf is almost entirely directed towards the grain while that from the lower leaves is only partly directed towards the grain and the detachment of flag leaf considerably decreases grain yield (Ali et al., 2010; Monyo and Whittington, 1973) Plant height and leaf morphology (flag leaf length, width, and area) are generally considered quantitative traits and influenced by the environment. Understanding the genetic bases of these traits is useful in wheat improvement. To date, more than twenty reduced height genes have been named and some are molecularly 70 characterized (McIntosh et al., 2013). Height-reducing genes fall into two groups depending on their reaction to endogenous gibberellic acid (GA). Firstly, GA- insensitive genes such as Rht-B1b and Rht-D1b encode mutant proteins that belong to the DELLA subfamily of GRAS regulatory proteins which repress GA responsive growth by decreasing the sensitivity of vegetative and reproductive tissues to endogenous GA, leading to reduced stem internode length and overall plant height (Tan et al., 2013). Secondly, plants carrying GA-responsive genes, such as Rht4 and Rht8, retain GA responsiveness but show decreased levels of endogenous bioactive GA not due to defective gibberellin biosynthesis or signaling, but possibly to a reduced sensitivity to brassinosteroids (Chen et al., 2015; Gasperini et al., 2012). It should be noted that unfavorable effects such as reduced seedling vigor associated with GA-insensitive genes and delayed anthesis date associated with GA-responsive genes do occur (Chen et al., 2013). Therefore, breeders may need new alternative dwarfing genes to achieve the appropriate height reduction without introducing too much of a negative effect. In wheat, studies on flag leaf characteristics have focused on their relationship with grain yield and plant adaptation (Blake et al., 2007; Dere and YIildirim, 2006; Monneveux et al., 2004) and few on QTL analysis. A previous report from Jia et al. (2013a) detected six QTLs for flag leaf length and width among which a major QTL named QFlw.nau-5A.1 explained 28.7 to 35.6% of the phenotypic variation. QFlw.nau-5A.1 was inherited like a semidominant gene, designated as TaFLW1, and fine mapped in a 0.2 cM interval on chromosome 5A (Xue et al., 2013). The 71 Wangshuibai TaFLW1 allele reduced flag leaf width up to 3 mm and was closely linked to the type I Fusarium head blight resistance gene Fhb5 (Wu et al., 2014; Xue et al., 2013). QTLs controlling leaf morphology have been cloned in rice. A 30-bp deletion in the coding region of rice Narrow leaf 1(Nal1) was significantly associated with reduced polar auxin transport capacity which affected the distribution pattern of vascular bundles leading to narrower leaves with fewer longitudinal veins. NARROW AND ROLLED LEAF 1 (NRL1), on rice chromosome 12, encodes the cellulose synthase-like protein D4 (OsCsID4) which plays a crucial role in leaf expansion in rice (Hu et al., 2010). Its three mutants (single base substitutions at three different loci) nrl1-1, nrl1-2, and nrl1-2 are shorter and show erect, narrow and semi-rolled leaves compared with the NRL1 carrying plant (Hu et al., 2010). QTL mapping studies of plant architecture, especially of flag leaf morphology at the whole genome level, have rarely been reported in wheat. To further explore QTLs for plant architecture and provide information for QTL pyramiding, I conducted experiments to map QTLs for plant architecture in a doubled haploid (DH) population of soft red winter wheat. The objective of this study was to identify QTLs with additive effects, epistatic effects, and Q×E interactions for wheat plant height, flag leaf width, length, area, and shape to help design strategies for attaining the desired plant architecture in wheat breeding programs. 72 Material and Methods Genetic resources and field experiments A doubled-haploid population of 124 lines derived from a cross between a soft red winter wheat germplasm line MD01W233-06-1 (MDW233) (Costa et al., 2010) and a soft red winter wheat cultivar SS8641 (Johnson et al., 2007b) was used for this study. MDW233 carries the Rht-D1b dwarfing gene the Ppd-D1b photoperiod sensitive allele as well as the 1RS/1AL translocation. A genetic linkage map with single nucleotide polymorphism (SNPs), simple sequence repeats (SSRs), and a morphological marker (coleoptile color) was constructed with an average interval length of 2.3 cM (Chapter 2 of this dissertation). The 124 DH lines, together with the parents MDW233 and SS8641, were planted in the greenhouse and research fields at the University of Maryland. The greenhouse evaluation was carried out in the 2011-2012 and 2012-2013 crop seasons. The population was germinated at room temperature and placed in a growth chamber (4°C, 16 hour light and 8 hour darkness) for eight weeks for vernalization and then transferred to to greenhouse (20°C, 16 hour light and 8 hour darkness) with each line planted in a one-gallon pot. Regular irrigation was used to keep soil moist. Fertilizers were applied directly to each pot in seedling stage. Pots were randomized with three replications in the 2011-2012 season and four replications in the 2012-2013 season. Field tests were conducted in research fields at Clarksville, MD and Queenstown, MD for the 2012-2013 and 2013-2014 crop seasons. The DH lines and two parents evaluated in field plots which were arranged in a randomized complete block design 73 with two replications. Each field plot consisted of seven rows separated by 15.2 cm. Seed density was 22 seeds per 0.305 m in each row. Growing season rainfall and temperature data were obtained from respective research farms for Clarksville, MD and Queenstown, MD (Figure 3.1). Soil fertility management followed recommended management practices for each location. All trials were sprayed with the metconazole fungicide (Caramba®, BASF Corporation) at anthesis to reduce potential infection by Fusarium graminearum. Traits and measurements At maturity, five plants were randomly chosen from each plot of the field study for plant architecture traits evaluation. Plants likely affected by the border effect were avoided. A total of five traits were measured including plant height (PHT, cm), flag leaf length (FLL, cm), flag leaf width (FLW, cm), and flag leaf area (FLA, cm2). FLW was taken at the widest part of the flag leaf. Flag leaf length was measured from the auricle to the apex. Flag leaf area (FLA) was derived (FLA=FLL×FLW×0.79) as previously described (Simpson, 1968; Spagnoletti Zeuli and Qualset, 1990). In the greenhouse study, PHT, FLL, FLW, FLA values were collected from each replication from three individual plants for 2011-2012 and four individual plants for 2012-2013 and averaged for further analyses. Data analysis An analysis of variance (ANOVA) for PHT, FLL, FLW, FLA, and FLS was performed separately for each environment and for six environments combined using the PROC GLM procedure of SAS version 9.3 (SAS Institute, Raleigh, NC 2013). 74 The ANOVA model for single environment analysis was Y= replication + genotype + error, where replication and genotype were fixed and error was random. The ANOVA model for combined analysis was Y = environment + replication within environment + genotype + genotype × environment + error, where error was considered random and all others were fixed. Pearson’s correlation coefficients were calculated using the PROC CORR procedure of SAS to detect the association among plant architecture traits. Broad-sense heritability (h2) (defined as h2= ???/(???+(???? /?)+ (???/??)), where ??? is the variance of genotypic effect, ???? is the genotype × environment variance, and ? and ? are the number of environments and replicates, respectively) for each trait was calculated on a family mean basis using the PROC MIXED procedure of SAS, as described by Holland et al. (2003). The distributions of all evaluated traits were produced using the JMP® Pro, Version 11 (SAS Institue, Cary, NC 2014) (Figure 3.2). QTL analysis In this study, QTL analysis was performed using IciMapping version 4.0 (Li et al., 2008) for additive effects and QTLNetwork version 2.1 for digenetic QTL epistasis (A×A or Q×Q), additive × environment (A×E or Q×E) and epistasis × environment (QQ×E) interactions (Wang et al., 1999; Yang et al., 2007). For IciMapping version 4.0, inclusive composite interval mapping of additive module (ICIM-ADD) was used and the walking speed for all traits was 1 cM. Reference LOD values were determined by 1, 000 permutations (Doerge, 2002). Type I error to determine the LOD from the permutation test was 0.05. The LOD threshold to declare the presence of a significant QTL was 3.0. The position at which the LOD score curve reached its 75 maximum was used as the estimate of the QTL location. For QTLNetwork version 2.1, mixed-model based composite interval mapping (MCIM) was used and Q×E, Q×Q, and QQ×E effects were estimated by the Monte Carlo Markov Chain method with a scanning speed of 1 cM step and the experiment-wise type I error for putative QTL detection of 0.05. Results Phenotypic data analysis The performance of the two parents and the DH lines is shown in Figure 3.2. In all six environments, plant architecture traits segregated continuously as typical quantitative traits. Transgressive segregation, progenies with higher or lower phenotype values than the respective parents, was observed for all traits investigated. The ANOVA revealed that the difference between DH lines for all plant architecture traits was highly significant (Table 3.1). Pairwise correlation between plant architecture traits are shown in Table 3.2. Four traits related to flag leaf morphology (FLL, FLW, FLA, and FLS) were significantly intercorrelated across all six environments. Positive correlations were found between FLL and FLW, FLA, and FLS whereas FLW was negatively correlated with FLS. An additional significant negative correlation was identified between PHT and FLW in two of the six environments. The direction of the correlation between FLL and PHT varied among environments. 76 Figure 3.1 Precipitation (unit: cm) and monthly average temperature (unit: °C) during growing season at four field environments: E1, Clarksville, 2013; E2, Clarksville, 2014; E3, Queenstown 2013; E4, Queenstown 2014. -5.0 0.0 5.0 10.0 15.0 20.0 25.0 Feburay March April May June M on th y av er ag e te m pe ra tu r ° C E1 E2 E3 E4 0.0 5.0 10.0 15.0 20.0 25.0 30.0 Feburay March April May June Pr ec ip it at io n (c m ) E1 E2 E3 E4 77 Figure 3.2 Frequency distribution of plant height (PHT, cm), flag leaf length (FLL, cm), flag leaf width (FLW, cm), flag leaf area (FLA, cm2), and flag leaf shape (FLS) of the double haploid lines in a) Clarksville 2013, b) Clarksville 2014, c) Queenstown 2013, d) Queenstown 2014, e) Greenhouse 2012, f) Greenhouse 2013. 78 Table 3.1 Pooled analyses of variance and heritability estimates for plant height (PHT, cm), flag leaf length (FLL, cm), flag leaf width (FLW, cm), flag leaf area (FLA, cm2), and flag leaf shape (FLS) in four field trials from 2013 to 2014 * Significant at the 0.001 probability level. † Values in parenthesis are standard errors for h2 Table 3.2 Pearson correlation coefficients among plant height (PHT, cm), flag leaf length (FLL, cm), flag leaf width (FLW, cm), flag leaf area (FLA, cm2), and flag leaf shape (FLS, cm) in six trials from 2012 to 2014. * Significant at the 0.05 probability level. ** Significant at the 0.01 probability level. *** Significant at the 0.001 probability level. Mean Squares Source of Variation df PH FLL FLW FLA FLS Environment 3 20508.86* 578.47* 3.92* 2648.79* 107.89* Rep(environment) 4 312.84* 23.69* 0.06* 65.26* 3.73* Genotype 123 212.04* 11.22* 0.08* 38.38* 6.41* genotype × environment 369 14.70* 1.65* 0.01* 5.42* 0.72* R2 0.95 0.88 0.89 0.90 0.86 Heritability (h2) † 0.93(0.01) 0.85(0.02) 0.90(0.2) 0.86 (0.02) 0.89(0.02) Environments Traits FLL FLW FLA FLS Clarksville 2013 PHT 0.24*** -0.07 0.14 0.28*** FLL 0.26*** 0.87*** 0.76*** FLW 0.70*** -0.41*** FLA 0.35 Clarksville 2014 PHT 0.03 -0.27*** -0.13 0.22* FLL 0.29*** 0.82*** 0.67*** FLW 0.78*** -0.51*** FLA 0.12 Queenstown 2013 PHT 0.14 -0.19* -0.01 0.29*** FLL 0.38*** 0.85*** 0.65*** FLW 0.81*** -0.44*** FLA 0.16 Queenstown 2014 PHT 0.30*** -0.06 0.16 0.31*** FLL 0.37*** 0.84*** 0.60*** FLW 0.82*** -0.51*** FLA 0.06 Greenhouse 2012 PHT -0.19* -0.13 -0.19* -0.13 FLL 0.35*** 0.90*** 0.81*** FLW 0.71*** -0.25*** FLA 0.48*** Greenhouse 2013 PHT 0.21* -0.06 0.07 0.25*** FLL 0.50*** 0.81*** 0.36*** FLW 0.90*** -0.60*** FLA -0.19* 79 QTLs with additive and additive × environment interaction effects Significant QTLs were detected for all traits evaluated, as summarized in Table 3.3. A total of seventy-four QTLs with additive effects were identified including twenty QTLs for PHT, thirteen for FLL, eighteen for FLW, twelve for FLA, and eleven for FLS. These QTLs were unevenly distributed in the wheat genome. Among them, 35 (47.3%) were in the A genome, 21 (28.4%) were in the B genome, and 18 (24.3%) were in the D genome. The phenotypic variance explained by each QTL ranged from 5.7 to 22% for PHT, 6.4 to 20.7% for FLL, 5.4 to 31.2% for FLW, 6.8 to 24.1% for FLA, and 7.3 to 19.6% for FLS. Both parents contributed favorable alleles (35 from MDW233 and 39 from SS8641). In general, these QTLs had low to moderate genetic effects common for quantitative traits. Additionally, QTL co-localization was found in nine marker intervals suggesting the possible presence of pleiotropy. Mapping QTLs with additive × environment interaction effects was conducted based on the data from the four field trials only. A total of four intervals were detected with significant Q×E interaction for PHT, FLL, and FLA (Table 3.4). Among them, the loci flanked by XPpdD1-Xsnp2869 and Xsnp1970-Xbarc28 were detected with significant additive effects and other two marker intervals were insignificant for additive effects. The heritability of Q×E interaction ranged from 1% to 2%. Queenstown 2013 had three Q×E interactions and the other three environments each had one. QTLs with epistatic and epistatic × environment interaction effects A total of 12 pairs of significant epistatic interactions (p<0.001) were detected for all five plant architecture traits (Table 3.5). These epistatic interactions involved loci 80 from within and across chromosomes with heritability values ranging from 0.6% to 4.3%. Among the twenty four epistatic intervals/loci, five were significant for additive effects and the rest were significant only in digenic epistatic interactions. Additionally, an epistatic × environment interaction was detected between chromosome regions flanked by Xsnp4061-Xsnp4027 on 5B and Xsnp4860-Xsnp4831 on 7B at Queenstown 2013 for FLW. However, none of these two intervals were significant for additive main effects. 81 Xwmc4960.0 Xsnp19700.9 Xbarc281.7 Xsnp200513.6 Xsnp199914.4 Xsnp212915.3 Xsnp199718.2 Xsnp185519.1 Xsnp93920.8 Xsnp145221.6 Xsnp189522.4 Xsnp198323.3 Xsnp194328.1 Xsnp183430.0 Xsnp200131.0 Xsnp195336.9 Xsnp194037.7 Xsnp199338.7 Xsnp131641.2 Xsnp197743.6 Xsnp184048.7 Xsnp188950.4 Xsnp186251.3 Xsnp182353.8 Xsnp193154.8 Xsnp198655.6 Xsnp199658.7 Xsnp195059.6 Xsnp186862.3 Xsnp199563.2 Xsnp222767.7 Q Fla.cz-1A .1 Q Fll.cz-1A.1 Q Fla.cz-1A .2 Q Fll.cz-1A.2 Q Fll.cz-1A .3 1A Xsnp24770.0 Xsnp24322.7 Xsnp44906.8 Xsnp24457.9 Xsnp248014.0 Xsnp247114.9 Xsnp246117.6 Xsnp246622.8 Xsnp242723.6 Xsnp247925.3 Xsnp242326.3 Xsnp132729.1 Xsnp248131.6 Xsnp243634.4 Xsnp244839.8 Xsnp247547.3 Xsnp243148.1 Xsnp8749.0 Xsnp232349.8 Xsnp76850.7 Xsnp240051.5 Xsnp226852.4 Xsnp236653.3 Xsnp232054.2 Xsnp240455.0 Xsnp237755.9 Xsnp8159.1 Xsnp240960.0 Xsnp239061.7 Xsnp235162.6 Xsnp227764.2 Xsnp237265.1 Xsnp239466.0 Xsnp237568.0 Xsnp236569.0 Xsnp238273.7 Xsnp240175.6 Xsnp233978.7 Xsnp239779.6 Xsnp238181.7 Xsnp232182.7 Xsnp231387.0 Xsnp240587.8 Xsnp240688.8 Xsnp236392.9 Xsnp231593.8 Xsnp235396.3 Xsnp235097.3 Xsnp238398.2 Xsnp233799.1 Xsnp2355100.1 Xsnp2360102.9 Xsnp2362104.7 Xsnp494105.5 Xsnp2291108.0 Xsnp2398109.8 Xsnp533112.3 Q Flw .cz-2A.1 Q Pht.cz-2A.1 QFll.cz-2A Q Flw .cz-2A.2 Q Fla.cz-2A.1 Q Fla.cz-2A.2 Q Flw .cz-2A.3 Q Pht.cz-2A.2 2A Xsnp30270.0 Xsnp37440.9 Xsnp30481.8 Xsnp14663.6 Xsnp30494.4 Xsnp30218.8 Xsnp305912.5 Xbarc1213.4 Xsnp300814.3 Xsnp299323.2 Xsnp152225.1 Xsnp303426.7 Xsnp305127.7 Xsnp304628.6 Xsnp306431.4 Xsnp304032.2 Xsnp309433.1 Xsnp305634.0 Xsnp300534.9 Xsnp304145.1 Xsnp298879.1 Xsnp3065115.2 Xbarc45121.0 Xsnp3037123.6 Xsnp3023124.4 Xsnp3383126.1 Xsnp3009127.0 Xsnp3052127.8 Xsnp758128.7 Xsnp310129.5 Xsnp4288132.0 Xsnp1758134.7 Xsnp1485136.3 Xsnp2964137.1 Xsnp2885142.1 Xsnp2987143.0 Xsnp2937144.0 Xsnp4728147.9 Xsnp2968148.9 Xsnp443152.4 Xsnp2927153.3 Xsnp2924154.1 Xsnp2956155.9 Xsnp2950159.3 Xsnp3313160.2 Xsnp2977161.0 Xsnp2900161.9 Xsnp2973162.9 Xsnp2989168.0 Xsnp2983169.8 Xsnp2990173.9 Xsnp2905179.7 Xsnp2979180.7 Xsnp445181.5 Xsnp2984187.8 Xsnp2934188.7 Xsnp2970189.5 Xsnp2920197.5 Xsnp2960200.5 Xsnp2948201.4 Xsnp2951206.5 Xsnp2971215.9 Xsnp2909216.8 Q Flw .cz-3A 3A Xsnp36340.0 Xsnp36321.9 Xsnp362310.6 Xsnp105713.1 Xsnp361419.0 Xsnp363649.0 Xsnp357651.5 Xsnp350852.4 Xsnp363753.4 Xsnp359054.3 Xsnp344458.3 Xsnp31880.7 Xsnp30981.5 Xsnp360484.1 Xbarc17085.0 Xsnp83688.2 Xsnp356693.8 Xsnp359794.7 Xsnp91097.5 Xsnp354599.4 Xsnp3573100.3 Xsnp3574102.9 Xsnp753103.8 Xsnp3551104.6 Xsnp3490105.5 Xsnp3595111.6 Xsnp3462114.2 Xsnp3572115.1 Xsnp3568116.1 Xsnp3608116.9 Xsnp3535121.5 Xsnp3447122.5 Xsnp3464135.3 Xsnp3547138.4 Xsnp3606139.3 Xsnp3575140.2 Xsnp3542142.0 Xsnp3488146.5 Xsnp3607154.7 Xsnp3486155.7 Xsnp3602156.6 Xsnp3512157.4 Xsnp4975158.3 Q Fll.cz-4A Q Pht.cz-4A 4A Xsnp38740.0 Xsnp38722.0 Xsnp38695.0 Xsnp38796.8 Xsnp387715.8 Xsnp62120.8 Xsnp174921.6 Xsnp136822.4 Xsnp27924.0 Xsnp387848.7 Xsnp61750.4 Xsnp84252.0 Xsnp21852.8 Xsnp4953.6 Xgwm30454.4 Xsnp99655.3 Xsnp383756.2 Xsnp383857.1 Xbarc10060.0 Xsnp484367.3 Xsnp381968.3 Xsnp378971.2 Xsnp384477.1 Xsnp385177.9 Xsnp416778.9 Xsnp376079.9 Xsnp383980.9 Xsnp381282.0 Xsnp385683.9 Xsnp377684.9 Xsnp385285.8 Xsnp384386.7 Xsnp382087.5 Xsnp200894.1 Xsnp447297.9 Xsnp385598.8 Xsnp85699.6 Xsnp3867101.2 Xsnp3836102.1 Xsnp3833102.9 Xsnp3863107.4 Xsnp3845108.2 Xsnp3865125.4 Xsnp3802127.1 Xsnp3783129.0 Xsnp3859134.4 Xsnp3853136.1 Xsnp3835143.1 Xsnp3761165.6 Xsnp3862170.5 Xsnp3787171.3 Xsnp663172.2 Xsnp3747174.0 Xsnp3849176.6 Xsnp3841186.2 Xsnp3803187.0 Xsnp3775188.0 Xsnp3860190.0 Q Fls.cz-5A Q Flw .cz-5A QPht.cz-5A.1 Q Pht.cz-5A.2 5A Xsnp42960.0 Xsnp427613.7 Xsnp424320.0 Xsnp429820.8 Xsnp427123.6 Xsnp443525.6 Xsnp428027.8 Xsnp424728.6 Xsnp425937.0 Xsnp428637.9 Xsnp429038.9 Xsnp429940.9 Xsnp424541.7 Xsnp36742.5 Xsnp427843.3 Xsnp448944.3 Xsnp426545.1 Xsnp418346.0 Xsnp421647.6 Xsnp421749.3 Xsnp422650.2 Xsnp422551.1 Xsnp421151.9 Xsnp418675.0 Xsnp422278.3 Xsnp49279.9 Xsnp419780.8 Xsnp47381.6 Xsnp422883.2 Xsnp421992.6 Xsnp7093.4 Xsnp436494.2 Xsnp420795.1 Q Fll.cz-6A Q Flw .cz-6A.1 Q Fls.cz-6A Q Flw .cz-6A.2 Q Flw .cz-6A.3 Q Flw .cz-6A.4 Q Flw .cz-6A.5 Q Fla.cz-6A Q Flw .cz-6A.6 6A 82 Xsnp47250.0 Xsnp47662.1 Xsnp13983.0 Xsnp47323.9 Xsnp47795.6 Xsnp47646.5 Xsnp47527.5 Xsnp47178.4 Xsnp47369.3 Xsnp472310.3 Xsnp472712.8 Xsnp466513.6 Xsnp468214.6 Xsnp471815.5 Xsnp475918.1 Xsnp477125.7 Xsnp477326.6 Xsnp477731.1 Xsnp474132.9 Xsnp473935.4 Xsnp469736.5 Xsnp476837.6 Xsnp130238.5 Xsnp476239.4 Xsnp475446.6 Xsnp473447.5 Xsnp466748.5 Xsnp471549.3 Xsnp472250.2 Xsnp469051.0 Xsnp466851.9 Xsnp474953.6 Xsnp32454.5 Xbarc12755.3 Xsnp137657.1 Xsnp474558.8 Xsnp477459.7 Xsnp476760.8 Xsnp476361.6 Xsnp474671.8 Xsnp467573.6 Xsnp477677.7 Xsnp84978.5 Xsnp477581.0 Xsnp155384.5 Xsnp167485.3 Xsnp477086.9 Xsnp473388.5 Xsnp475890.2 Xsnp475791.2 Xsnp476192.2 Xsnp477294.8 Xsnp454795.7 Xsnp453596.6 Xsnp4660101.0 Xsnp4637103.0 Xsnp4567105.4 Xsnp4946106.3 Xsnp4546107.1 Xsnp4624107.9 Xsnp4612109.6 Xsnp1385110.5 Xsnp4523112.1 Xsnp4584113.0 Xsnp4935114.7 Xsnp4622115.7 Xsnp4639117.4 Xsnp1181118.3 Xsnp321120.0 Xsnp4593120.8 Xsnp4588121.6 Xsnp4620123.3 Xgwm282124.1 Xsnp4563127.6 Xsnp4936129.3 Xsnp4602137.2 Xsnp4659138.1 Xsnp4658139.0 Xsnp4606149.1 Xsnp4570150.8 Xsnp4623152.6 Xsnp4536154.2 Xsnp4556155.1 Xsnp4557156.2 Xsnp4947159.1 Xsnp1727159.9 Xsnp4608160.9 Xwmc273167.4 Xsnp4655170.7 Xsnp4643171.5 Q Fls.cz-7A .1 Q Fll.cz-7A Q Fls.cz-7A.2 Q Pht.cz-7A.1 Q Pht.cz-7A .2 7A Xsnp41400.0 Xsnp41140.9 Xsnp414211.4 Xsnp4812.2 Xsnp415213.8 Xsnp62515.6 Xsnp415016.6 Xsnp415517.7 Xsnp412018.7 Xsnp413019.5 Xsnp388420.3 Xsnp407921.2 Xsnp401622.1 Xsnp396423.0 Xsnp33025.0 Xsnp388225.8 Xsnp416827.4 Xsnp402828.3 Xsnp390129.5 Xsnp399730.5 Xsnp409231.3 Xsnp72532.2 Xsnp400533.0 Xsnp401834.1 Xsnp401435.0 Xsnp397536.7 Xsnp396337.7 Xsnp389138.8 Xsnp397344.9 Xsnp406245.9 Xsnp44646.7 Xsnp408347.6 Xsnp398857.5 Xsnp100658.3 Xsnp82359.1 Xsnp405964.8 Xsnp406166.4 Xsnp402768.1 Xsnp401769.8 Xsnp404971.5 Xsnp409072.3 Xsnp406073.2 Xsnp407274.2 Xsnp408578.2 Xsnp405879.2 Xsnp405080.9 Xsnp402082.6 Xsnp406883.6 Xsnp401294.7 Xsnp135696.5 Xsnp54998.9 Xsnp3983100.6 Xsnp797101.5 Xsnp794102.3 Xsnp4086103.2 Xsnp4089104.1 Xsnp29106.7 Xbarc59108.4 Xsnp4077113.0 Xsnp4011114.7 Xsnp4073117.9 Xsnp3970118.8 Xsnp4753122.4 Xsnp435123.2 Q Fla.cz-5B .1 Q Fll.cz-5B.1 Q Fll.cz-5B .2 Q Fla.cz-5B.2 Q Fls.cz-5B Q Pht.cz-5B.1 Q Pht.cz-5B.2 5B Xsnp22000.0 Xsnp22210.9 Xsnp22173.8 Xsnp22057.8 Xsnp450312.4 Xsnp218118.5 Xsnp221819.4 Xsnp218021.3 Xsnp220922.1 Xsnp218623.1 Xsnp220624.2 Xsnp221325.1 Xsnp170730.9 Xgwm1131.9 Xsnp207132.8 Xsnp76733.8 Xsnp213034.6 Xsnp207339.6 Xsnp211049.2 Xsnp85450.0 Xsnp212150.8 Xsnp205352.7 Xsnp213254.5 Xsnp209258.9 Xsnp206459.7 Xsnp208760.6 Xsnp211561.4 Xsnp211763.1 Xsnp208064.0 Xsnp211464.9 Xsnp322766.8 Xsnp212867.7 Xsnp212668.6 Xsnp492871.2 Xsnp210774.1 Xsnp210675.1 Xsnp208484.5 Xsnp211386.3 Xsnp209187.1 Xsnp205287.9 Xsnp1777117.4 Xsnp2059124.8 Xsnp2131125.6 Xsnp2116128.9 Xsnp2067133.2 Xsnp2127141.7 Xbarc80144.1 Q Fla.cz-1B Q Fll.cz-1B 1B Xsnp27790.0 Xsnp27500.9 Xsnp277110.2 Xsnp278017.3 Xsnp373419.9 Xsnp274324.7 Xsnp45625.5 Xsnp74633.7 Xsnp278534.5 Xsnp277836.2 Xsnp276946.4 Xbarc1047.3 Xsnp274458.3 Xsnp77459.1 Xsnp270560.0 Xsnp275261.1 Xsnp278662.2 Xsnp277763.2 Xsnp277364.1 Xgwm31966.1 Xsnp276767.0 Xsnp259169.0 Xsnp256970.9 Xsnp268871.8 Xsnp269772.6 Xsnp259874.2 Xsnp268675.1 Xsnp263576.9 Xsnp264677.8 Xsnp260778.6 Xsnp341379.5 Xsnp265980.4 Xsnp269882.0 Xsnp257186.4 Xsnp266787.4 Xsnp266888.3 Xsnp269689.1 Xsnp251590.0 Xsnp117690.8 Xsnp269492.5 Xsnp264893.5 Xsnp261594.5 Xsnp268295.5 Xsnp261997.4 Xsnp267098.3 Xsnp2523100.2 Xsnp2620102.1 Xsnp2665103.8 Xsnp2633105.8 Xsnp2614107.7 Xsnp2649109.9 Xsnp2585115.9 Xsnp681116.8 Xsnp2666119.4 Xsnp2723122.1 Xsnp2574123.7 Xsnp448125.4 Xsnp2603130.3 Xsnp2586132.3 Xsnp2600133.9 Xsnp2679134.8 Xsnp2599137.1 Xsnp2802138.1 Xsnp2496139.0 Xsnp2663139.8 Q Pht.cz-2B.1 Q Pht.cz-2B.2 Q Fls.cz-2B 2B Xsnp30740.0 Xsnp5991.7 Xsnp34074.3 Xbarc1475.1 Xsnp33289.2 Xsnp342113.5 Xsnp331815.5 Xsnp315617.4 Xsnp340418.2 Xsnp341119.3 Xsnp339920.3 Xsnp307021.3 Xsnp328822.4 Xsnp340523.3 Xsnp338927.2 Xsnp334431.0 Xsnp325335.3 Xsnp334941.8 Xsnp336742.7 Xsnp332043.5 Xsnp333544.3 Xsnp311946.3 Xsnp339547.3 Xsnp338748.1 Xsnp341549.9 Xsnp153951.6 Xsnp340852.4 Xbarc16453.2 Xsnp320555.1 Xsnp338555.9 Xsnp314656.7 Xsnp338257.6 Xsnp337260.9 Xsnp61161.9 Xsnp319262.7 Xsnp338163.6 Xsnp339364.5 Xsnp336866.2 Xsnp315367.1 Xsnp88768.8 Xsnp341072.5 Xsnp341673.4 Xsnp338674.3 Xsnp324775.4 Xsnp331276.2 Xsnp169779.6 Xsnp18780.4 Xsnp334281.3 Xsnp311285.6 Xsnp386487.3 Xsnp329988.2 Xsnp334593.1 Xsnp328993.9 Xsnp316097.3 Xsnp317498.1 Xsnp3397101.5 Xsnp3417102.4 Xsnp3418107.2 Xsnp3175109.0 Xsnp3401128.2 Xsnp3358129.1 Xsnp3400130.0 Xsnp326130.9 Xsnp3406131.8 Xsnp3403132.8 Xsnp3181134.0 Q Fla.cz-3B Q Flw .cz-3B.1 Q Flw .cz-3B.2 Q Flw .cz-3B.3 Q Pht.cz-3B.1 Q Pht.cz-3B.2 Q Pht.cz-3B.3 Q Pht.cz-3B.4 Q Pht.cz-3B.5 3B 83 Figure 3.3 Position of quantitative trait loci (QTLs) detected in a doubled haploid mapping population derived from MD01W233-06-1 × SS8641. Locus marker names are shown on the right side of the chromosomes and values to the left of chromosomes show the genetic distance (cM) for each marker. QTLs are labeled with trait abbreviations and the QTL number for each trait. QTLs for the same trait are in the same color. Xsnp13040.0 Xsnp22524.3 Xsnp6065.2 Xsnp223814.6 Xsnp223717.2 Xsnp164218.0 Xsnp224022.4 Xsnp223926.5 Xsnp225127.3 Xsnp225550.0 Xsnp225650.8 Xsnp224452.6 Xsnp222953.4 Xsnp223555.3 Xsnp223160.0 Xsnp222468.3 Xsnp223473.3 Xsnp223285.6 Q Fll.cz-1D Q Fls.cz-1D 1D Xsnp28700.0 Xsnp28545.8 Xsnp28199.4 Xsnp286812.5 Xsnp288113.4 Xsnp282319.6 Xsnp288220.5 Xgwm26125.7 Xsnp281027.5 Xsnp287528.5 Xsnp285029.4 Xsnp286247.0 PpdD161.7 Xsnp286976.4 Xsnp284477.4 Xsnp287778.5 Xsnp284879.5 Xsnp63480.4 Xsnp286381.3 Xsnp287682.1 Xsnp52285.6 Xsnp280888.5 Xsnp280489.4 Xsnp176691.2 Xsnp280995.5 Xsnp280696.4 Xsnp17999.7 Xsnp2790105.0 Xsnp2807107.8 Xsnp2788108.7 Xsnp2805109.5 Xsnp2795112.5 Xsnp708119.4 Xsnp1745125.9 Q Flw .cz-2D.1 Q Fla.cz-2D.1 Q Pht.cz-2D.1 Q Fla.cz-2D.2 Q Fll.cz-2D QFlw .cz-2D.2 Q Fla.cz-2D.3 Q Flw .cz-2D.3 Q Flw .cz-2D.4 Q Fls.cz-2D Q Pht.cz-2D.2 Q Pht.cz-2D.3 2D Xsnp49800.0 Xsnp343116.6 Xsnp343418.6 Xsnp342528.1 Xsnp342735.0 Xsnp343035.8 Xsnp343236.7 Xsnp94238.3 Xsnp78639.1 Xsnp341947.6 Xsnp342256.0 Xsnp318772.7 Q Fls.cz-3D Q Pht.cz-3D 3D Xsnp41770.0 Xsnp41793.5 Xsnp17516.7 Xsnp417522.1 Xsnp19823.8 Xsnp415624.6 Xsnp417327.1 Xsnp417828.1 Xgdm13647.9 Xsnp417256.3 Xsnp417178.9 Xsnp87689.5 Xsnp417094.6 Xsnp4157117.6 Xsnp1271124.4 Xsnp4163125.3 Xsnp4162139.4 Xsnp4169165.6 Xsnp4166178.6 Xsnp553179.5 Q Fll.cz-5D Q Fls.cz-5D 5D 84 Table 3.3 Quantitative trait loci (QTLs) for plant height (PHT, cm), flag leaf length (FLL, cm), flag leaf width (FLW, cm), flag leaf area (FLA, cm2), and flag leaf shape (FLS, cm) in six environments from 2012 to 2014. QTL Trait Environment Position (cM) Marker interval LOD score PVE (%) Additive effect QPht.cz-2A.1 PHT Clarksville 2013 7 Xsnp4490-Xsnp2445 3.9 5.7 -1.35 QPht.cz-2A.2 PHT Queenstown 2014 51 Xsnp768-Xsnp2400 3.8 7.6 -1.46 QPht.cz-4A PHT Clarksville 2013 48 Xsnp3614-Xsnp3636 4.6 7.0 1.49 QPht.cz-5A.1 PHT Greenhouse 2012 122 Xsnp3845-Xsnp3865 4.6 10.5 -2.06 QPht.cz-5A.2 PHT Queenstown 2014 183 Xsnp3849-Xsnp3841 3.3 7.1 1.40 QPht.cz-7A.1 PHT Clarksville 2013 92 Xsnp4757-Xsnp4761 6.2 9.6 -1.79 QPht.cz-7A.1 PHT Clarksville 2014 92 Xsnp4757-Xsnp4761 4.6 11.1 -1.95 QPht.cz-7A.2 PHT Queenstown 2014 94 Xsnp4761-Xsnp4772 5.3 10.8 -1.78 QPht.cz-2B.1 PHT Clarksville 2013 58 Xbarc10-Xsnp2744 5.2 7.9 1.58 QPht.cz-2B.2 PHT Queenstown 2013 83 Xsnp2698-Xsnp2571 4.1 9.8 1.86 QPht.cz-3B.1 PHT Clarksville 2014 50 Xsnp3415-Xsnp1539 6.5 16.0 -2.29 QPht.cz-3B.2 PHT Clarksville 2013 52 Xsnp1539-Xsnp3408 9.3 15.2 -2.20 QPht.cz-3B.3 PHT Queenstown 2014 55 Xbarc164-Xsnp3205 4.1 8.0 -1.50 QPht.cz-3B.4 PHT Greenhouse 2013 61 Xsnp3372-Xsnp611 5.1 13.8 -2.07 QPht.cz-3B.5 PHT Queenstown 2013 62 Xsnp611-Xsnp3192 4.6 11.1 -1.98 QPht.cz-5B.1 PHT Greenhouse 2012 84 Xsnp4068-Xsnp4012 9.3 20.1 2.86 QPht.cz-5B.2 PHT Greenhouse 2012 122 Xsnp3970-Xsnp4753 3.9 7.7 -1.77 QPht.cz-2D.1 PHT Queenstown 2014 59 Xsnp2862-XPpdD1 7.4 16.6 -2.16 QPht.cz-2D.1 PHT Clarksville 2013 60 Xsnp2862-XPpdD1 4.9 7.8 -1.58 QPht.cz-2D.2 PHT Greenhouse 2012 119 Xsnp2795-Xsnp708 9.1 19.5 2.81 QPht.cz-2D.2 PHT Queenstown 2013 119 Xsnp2795-Xsnp708 8.4 22.0 2.79 QPht.cz-2D.2 PHT Clarksville 2013 119 Xsnp2795-Xsnp708 6.5 10.2 1.80 QPht.cz-2D.2 PHT Clarksville 2014 119 Xsnp2795-Xsnp708 3.6 8.6 1.68 QPht.cz-2D.3 PHT Queenstown 2014 120 Xsnp708-Xsnp1745 7.9 17.1 2.17 QPht.cz-2D.3 PHT Greenhouse 2013 120 Xsnp708-Xsnp1745 5.8 15.8 2.21 QPht.cz-3D PHT Greenhouse 2013 64 Xsnp3422-Xsnp3187 3.3 9.6 1.75 QFlw.cz-2A.1 FLW Greenhouse 2012 0 Xsnp2477-Xsnp2432 4.3 9.5 0.06 QFlw.cz-2A.2 FLW Greenhouse 2013 16 Xsnp2471-Xsnp2461 13.3 31.2 0.13 QFlw.cz-2A.3 FLW Queenstown 2013 24 Xsnp2427-Xsnp2479 4.8 11.2 0.04 QFlw.cz-2A.3 FLW Clarksville 2014 24 Xsnp2427-Xsnp2479 3.8 7.1 0.03 QFlw.cz-2A.3 FLW Queenstown 2014 24 Xsnp2427-Xsnp2479 3.6 7.0 0.03 QFlw.cz-2A.3 FLW Clarksville 2013 24 Xsnp2427-Xsnp2479 3.4 7.3 0.03 QFlw.cz-3A FLW Greenhouse 2012 122 Xbarc45-Xsnp3037 5.8 13.4 -0.07 QFlw.cz-5A FLW Clarksville 2014 105 Xsnp3833-Xsnp3863 5.2 10.4 0.04 QFlw.cz-6A.1 FLW Clarksville 2014 42 Xsnp4245-Xsnp367 3.0 5.4 -0.03 QFlw.cz-6A.2 FLW Queenstown 2014 46 Xsnp4183-Xsnp4216 4.7 9.1 -0.03 QFlw.cz-6A.3 FLW Clarksville 2013 52 Xsnp4211-Xsnp4186 4.4 9.5 -0.03 QFlw.cz-6A.4 FLW Queenstown 2013 78 Xsnp4186-Xsnp4222 5.0 11.9 -0.05 QFlw.cz-6A.5 FLW Greenhouse 2013 81 Xsnp4197-Xsnp473 3.3 6.4 -0.06 QFlw.cz-6A.6 FLW Clarksville 2014 84 Xsnp4228-Xsnp4219 3.4 6.4 -0.03 QFlw.cz-3B.1 FLW Clarksville 2014 31 Xsnp3389-Xsnp3344 5.9 11.3 0.04 QFlw.cz-3B.2 FLW Queenstown 2014 35 Xsnp3344-Xsnp3253 3.6 6.9 0.03 QFlw.cz-3B.3 FLW Queenstown 2013 46 Xsnp3335-Xsnp3119 3.0 6.9 0.03 QFlw.cz-2D.1 FLW Clarksville 2013 57 Xsnp2862-XPpdD1 7.0 17.2 -0.05 QFlw.cz-2D.1 FLW Greenhouse 2013 59 Xsnp2862-XPpdD1 6.5 13.6 -0.09 QFlw.cz-2D.2 FLW Queenstown 2013 80 Xsnp2848-Xsnp634 3.3 7.5 -0.04 QFlw.cz-2D.3 FLW Queenstown 2014 90 Xsnp2804-Xsnp1766 9.3 19.9 -0.05 QFlw.cz-2D.3 FLW Clarksville 2014 91 Xsnp2804-Xsnp1766 4.2 7.7 -0.03 QFlw.cz-2D.4 FLW Greenhouse 2012 92 Xsnp1766-Xsnp2809 4.5 9.9 -0.06 QFls.cz-2A FLS Greenhouse 2013 59 Xsnp2377-Xsnp81 4.5 11.1 -0.44 QFls.cz-5A FLS Queenstown 2013 60 Xbarc100-Xsnp4843 4.7 11.7 -0.35 QFls.cz-5A FLS Clarksville 2014 61 Xbarc100-Xsnp4843 6.6 19.6 -0.49 QFls.cz-6A FLS Greenhouse 2013 46 Xsnp4183-Xsnp4216 4.9 12.3 0.46 QFls.cz-7A.1 FLS Queenstown 2014 74 Xsnp4675-Xsnp4776 6.3 17.4 -0.41 QFls.cz-7A.2 FLS Clarksville 2013 91 Xsnp4758-Xsnp4757 7.0 18.2 -0.46 QFls.cz-7A.2 FLS Queenstown 2013 91 Xsnp4758-Xsnp4757 5.4 13.7 -0.38 QFls.cz-2B FLS Greenhouse 2013 66 Xsnp2773-Xgwm319 5.0 12.8 -0.47 QFls.cz-2B FLS Clarksville 2013 66 Xsnp2773-Xgwm319 3.4 8.3 -0.30 QFls.cz-5B FLS Greenhouse 2012 73 Xsnp4090-Xsnp4060 3.8 11.3 -0.75 QFls.cz-1D FLS Queenstown 2013 85 Xsnp2234-Xsnp2232 4.6 11.5 0.35 QFls.cz-2D FLS Greenhouse 2013 119 Xsnp2795-Xsnp708 3.0 7.3 0.35 QFls.cz-3D FLS Queenstown 2014 50 Xsnp3419-Xsnp3422 3.2 8.5 0.29 QFls.cz-3D FLS Greenhouse 2012 56 Xsnp3419-Xsnp3422 3.8 11.1 0.76 QFls.cz-5D FLS Clarksville 2014 25 Xsnp4156-Xsnp4173 4.7 12.7 0.39 85 Table 3.3 Continued QTL Trait Environment Position (cM) Marker interval LOD score PVE (%) Additive effect QFll.cz-1A.1 FLL Queenstown 2013 1 Xsnp1970-Xbarc28 7.5 18.5 0.69 QFll.cz-1A.1 FLL Greenhouse 2013 1 Xsnp1970-Xbarc28 4.5 10.0 0.76 QFll.cz-1A.1 FLL Clarksville 2014 1 Xsnp1970-Xbarc28 3.9 11.5 0.46 QFll.cz-1A.2 FLL Queenstown 2014 2 Xbarc28-Xsnp2005 6.4 16.0 0.51 QFll.cz-1A.3 FLL Clarksville 2013 59 Xsnp1996-Xsnp1950 3.1 6.4 0.36 QFll.cz-2A FLL Greenhouse 2013 15 Xsnp2471-Xsnp2461 5.2 11.7 0.83 QFll.cz-2A FLL Queenstown 2013 16 Xsnp2471-Xsnp2461 3.5 8.2 0.46 QFll.cz-4A FLL Greenhouse 2012 0 Xsnp3634-Xsnp3632 3.3 10.2 1.26 QFll.cz-6A FLL Greenhouse 2013 25 Xsnp4271-Xsnp4435 4.4 9.7 0.75 QFll.cz-7A FLL Clarksville 2013 75 Xsnp4675-Xsnp4776 6.4 14.5 -0.54 QFll.cz-1B FLL Queenstown 2014 87 Xsnp2113-Xsnp2091 3.9 9.2 0.39 QFll.cz-5B.1 FLL Greenhouse 2012 32 Xsnp4092-Xsnp725 5.0 16.3 -1.58 QFll.cz-5B.2 FLL Clarksville 2013 44 Xsnp3891-Xsnp3973 5.4 12.0 -0.49 QFll.cz-1D FLL Greenhouse 2013 1 Xsnp1304-Xsnp2252 3.8 8.5 0.71 QFll.cz-2D FLL Queenstown 2013 63 XPpdD1-Xsnp2869 6.2 15.3 -0.63 QFll.cz-2D FLL Clarksville 2013 66 XPpdD1-Xsnp2869 7.8 19.6 -0.63 QFll.cz-2D FLL Queenstown 2014 70 XPpdD1-Xsnp2869 7.5 20.7 -0.58 QFll.cz-5D FLL Clarksville 2014 1 Xsnp4177-Xsnp4179 4.7 14.6 0.53 QFla.cz-1A.1 FLA Queenstown 2013 1 Xsnp1970-Xbarc28 8.3 16.8 1.36 QFla.cz-1A.2 FLA Queenstown 2014 2 Xbarc28-Xsnp2005 4.3 9.1 0.70 QFla.cz-2A.1 FLA Greenhouse 2013 17 Xsnp2471-Xsnp2461 13.1 28.7 4.18 QFla.cz-2A.2 FLA Queenstown 2013 20 Xsnp2461-Xsnp2466 6.5 13.4 1.23 QFla.cz-6A FLA Clarksville 2014 83 Xsnp473-Xsnp4228 3.2 10.1 -0.76 QFla.cz-1B FLA Queenstown 2014 17 Xsnp4503-Xsnp2181 4.1 9.4 0.71 QFla.cz-3B FLA Greenhouse 2013 5 Xsnp3407-Xbarc147 3.8 6.8 2.03 QFla.cz-5B.1 FLA Clarksville 2013 0 Xsnp4140-Xsnp4114 3.3 7.9 -0.63 QFla.cz-5B.2 FLA Greenhouse 2012 48 Xsnp4083-Xsnp3988 3.2 11.4 -2.47 QFla.cz-2D.1 FLA Queenstown 2013 59 Xsnp2862-XPpdD1 9.5 20.9 -1.53 QFla.cz-2D.2 FLA Greenhouse 2013 62 XPpdD1-Xsnp2869 5.2 9.7 -2.43 QFla.cz-2D.2 FLA Queenstown 2014 62 XPpdD1-Xsnp2869 5.0 10.6 -0.77 QFla.cz-2D.2 FLA Clarksville 2013 63 XPpdD1-Xsnp2869 8.7 24.1 -1.10 QFla.cz-2D.3 FLA Queenstown 2014 90 Xsnp2804-Xsnp1766 4.6 9.6 -0.72 Table 3.4 QTL × Environment interactions influencing plant height (PHT, cm), flag leaf length (FLL, cm), flag leaf width (FLW, cm), flag leaf area (FLA, cm2), and flag leaf shape (FLS, cm) in four field environments during 2013 and 2014. † AE is the additive × environment interaction effect at each environment. E1: Clarksville 2013; E2: Clarksville 2014; E3: Queenstown 2013; E4: Queenstown 2014. ‡ h2(ae) is heritability estimate of the additive × environment interaction effect across four field trails. § Interval with significant additive effect. * Significant at 0.05 probability level. ***Significant at 0.001 probability level. Trait Chr. Position Interval AE1† AE2† AE3† AE4† h 2(ae) ‡ PHT 3B 49.9 Xsnp3415-Xsnp1539 0.61* 1.0% FLL 1A 0.9 Xsnp1970-Xbarc28§ 0.16* 1.1% FLL 2D 66.7 XPpd-D1-Xsnp2869§ 0.23* 1.4% FLA 1A 0 Xwmc496-Xsnp1970§ -0.31* 0.45*** 2.0% FLA 2D 65.7 XPpd-D1-Xsnp2869 -0.33* 1.2% 86 Table 3.5 Digenetic epistatic QTLs for plant height (PHT, cm), flag leaf length (FLL, cm), flag leaf width (FLW, cm), flag leaf area (FLA, cm2), and flag leaf shape (FLS) in four field trails during 2013 and 2014. Trait Interval† Chr.† Position† Interval‡ Chr.‡ Position‡ AA§ E1¶ E2¶ E3 ¶ E4 ¶ h2(aa) # h2(aae) †† PHT Xsnp4757-Xsnp4761‡‡ 7A 91.2 Xsnp3754-Xsnp4981 4D 28 0.72*** 1.3% 0.2% PHT Xsnp3064-Xsnp3040 3A 31.4 Xsnp849-Xsnp4775 7A 80.5 -1.05*** 4.3% 0.1% PHT Xsnp3734-Xsnp2743 2B 19.9 Xsnp786-Xsnp3419 3D 39.1 -0.99*** 4.7% 0.3% PHT Xsnp3389-Xsnp3344‡‡ 3B 30.2 Xsnp3417-Xsnp3418 3B 102.4 1.04*** 4.8% 0.2% FLL Xsnp2362-Xsnp494 2A 104.7 Xsnp3444-Xsnp318 4A 73.3 -0.30*** 3.4% 0.7% FLW Xsnp4763-Xsnp4746 7A 71.6 Xsnp2117-Xsnp2080 1B 63.1 0.02*** 2.7% 0.2% FLW Xsnp4061-Xsnp4027 5B 66.4 Xsnp4860-Xsnp4831 7B 137.3 0.02*** 0.012* 3.5% 0.9% FLA Xwmc496-Xsnp1970 1A 0 Xsnp2471-Xsnp2461‡‡ 2A 15.9 0.27*** 0.8% 0.6% FLA Xsnp2471-Xsnp2461‡‡ 2A 15.9 Xsnp4177-Xsnp4179‡‡ 5D 1 0.29*** 1.0% 0.3% FLA Xsnp1995-Xsnp2227 1A 63.2 Xsnp2885-Xsnp2987 3A 142.1 -0.41*** 2.3% 0.3% FLS Xsnp2351-Xsnp2277 2A 62.6 Xsnp4444-Xsnp4453 6B 5.6 0.14*** 0.6% 1.4% FLS Xsnp2401-Xsnp2339 2A 75.6 Xsnp4444-Xsnp4453 6B 5.6 -0.29*** 2.7% 1.0% † The flanking markers, chromosome and position of the first interval involved in the epistasis. ‡ The flanking markers, chromosome and position of the second interval involved in the epistasis. § The additive × additive effect. ¶ The epistasis × environment effect at each environment. E1: Clarksville 2013; E2: Clarksville 2014; E3: Queenstown 2013; E4: Queenstown 2014; # The heritability estimate for additive × additive interaction effects across five environment. †† The heritability estimate for epistasis × environment interaction effects across four field trials. ‡‡ Interval with significant additive effect. * Significant at the 0.05 probability level *** Significant at the 0.001 probability level ► Figure 3.4 Distribution of genetic and non-genetic components for yield and yield related traits: plant height (PHT, cm), Flag leaf length (FLL, cm), Flag leaf width (FLW, cm), Flag leaf area (FLA, cm2), Flag leaf shape (FLS). a) total number of QTLs detected for additive (a), additive × environment (ae), epistasis (aa), and epistasis × environment interactions (aae) effects. b) relative magnitude of a, ae, aa, aae effects. 87 Discussion Plant architecture is important for grain yield potential in cereal crops. Understanding the genetic control of plant architecture can lay the foundation for further genetic improvement. In this study, a winter wheat DH population was used to study plant architecture traits including PHT, FLL, FLW, FLA, and FLS with the aim of locating the underlying QTLs and to provide targets for marker-assisted selection (MAS) in breeding programs. QTLs for plant architecture traits Twenty QTLs for plant height were mapped to chromosomes 2A, 4A, 5A, 7A, 1B, 2B, 5B, 2D, and 3D. A major QTL (QPht.cz-2D.1) flanked by Xsnp2862 and Ppd-D1 was detected in two environments with high LOD score and PVE (Table 3.3). This region also co-localized with QTLs for FLW and FLA and was closely linked with QTLs for FLL (Figure 3.3). The multiple effects of this region were possibly due to the pleiotropic effects of Ppd-D1 which is one of the two major genes controlling photoperiod-sensitivity in wheat. Among all alleles of the Ppd-D1 gene, Ppd-D1b is the intact allele and is photoperiod sensitive (Guo et al., 2010) which is also carried by MDW233. Ppd-D1b is known to reduce the dates to heading and plant height in many wheat cultivars worldwide (Wilhelm et al., 2013). Similarly, the MDW233 allele of QPht.cz-2D.1 reduced plant height by an average of 1.87 cm in Queenstown 2014 and Clarksville 2013. Additionally, two other major QTLs (QPht.cz-2D.2 and QPht.cz-2D.3) were detected for PHT, which were about 60 cM downstream of Ppd- D1b on chromosome 2D (Table 3.3). QPht.cz-2D.2 was detected in four 88 environments with an average PVE=10.2%. QPht.cz-2D.3 was detected in two environments with an average PVE of 16.5%. The favorable alleles for these were contributed by SS8641 and both of their additive effects were greater than that of QPht.cz-2D.1. In previous studies, Wang et al. (2010) reported a QTL for PHT on 2DS using a winter wheat population and McCartney et al. (2005) detected a QTL on 2DS for PHT using a population generated from the spring wheat cross RL4452 × ‘AC Domain’ to study the inheritance of multiple agronomic traits. The location of these two QTLs was very close to the well-known Rht8 gene which is upstream of Ppd-D1(Gasperini et al., 2012). Thus, it is possible that QTLs QPht.cz-2D.2 and QPht.cz-2D.2, identified in the present study, are novel loci for PHT. Moreover, QPht.cz-2D.2 was found to co-localize with QFll.cz-2D, a QTL detected for FLS with LOD=4.7 and PVE=12.7 in the 2013 greenhouse study, suggesting the presence of pleiotropy in this loci. Based on these results, QPht.cz-2D.2 and QPht.cz-2D.3 are good candidates for fine mapping and gene cloning to get further understanding of their genetic function and develop gene-specific markers for MAS. Additionally, a cluster of five PHT QTLs was detected in a 12.6 cM region on chromosome 3B. Four of them were major QTLs explaining an average of 14% of the phenotypic variation and all their favorable alleles were from MDW233. In this region, QTLs for agronomic traits such as grain yield, thousand grain weight and plant height as well as QTL co-localizations have been reported by several independent studies (Bennett et al., 2012a; Cuthbert et al., 2008; Kumar et al., 2007; Rebetzke et al., 2008). Furthermore, a 2 cM region that contained two QTLs (QPht.cz-7A.1 and QPht.cz- 7A.2) on chromosome 7A was significant for PHT. QPht.cz-7A.1 was detected in 89 Clarksville 2013 and Clarksville 2014 with an average PVE=10.35%. The interaction of QPht.cz-7A.1 with another loci flanked by Xsnp3754-Xsnp4981 on chromosome 4D explained 1.3% of the phenotypic variation of PHT. QPht.cz-7A.2 was detected in Queenstown 2014 explaining 10.8% of the phenotypic variation. Both favorable alleles in these two loci were from MDW233. McCartney et al. (2005) mapped a QTL, QHt.crc-7A, in the same region for PHT but with a smaller PVE and LOD score. All three QTLs were located around 30 cM downstream of SSR marker barc127 suggesting that QHt.crc-7A may be QPht.cz-7A.1 or QPht.cz-7A.2. Another major QTL, QPht.cz-5B.1 (LOD=9.3, PVE=20.1%), detected in the present study was comparable to the one identified by Zanke et al. (2014) in a whole genome association mapping of plant height. Previous studies also identified QTLs for plant height or other agronomic traits in the same or nearby region with QPht.cz-2A.1 (Jia et al., 2013a; Zanke et al., 2014) QPht.cz-2A.2 (Li et al., 2007b; McCartney et al., 2005), QPht.cz-2B.1 (Jia et al., 2013a), QPht.cz-2B.2 (McCartney et al., 2005), QPht.cz-3D (Hai et al., 2008), QPht.cz-4A (Hai et al., 2008), QPht.cz-5A.1 (Jia et al., 2013a), QPht.cz-5A.2 (Huang et al., 2006), QPht.cz-5B.2 (Zanke et al., 2014). Although considerable progress has been made in the genetic understanding of grain yield and yield components, reports of QTLs for flag leaf morphology in wheat are still limited. In this study, FLW data was collected from six environments for QTL analysis. QFlw.cz-2A.2 on chromosome 2A associated with FLW had the largest effect and explained 31.2% of the phenotypic variation in the 2013 greenhouse study. In addition, QFlw.cz-2A.2 had significant large effects on FLL and FLA with PVE 90 ranging from 8.2% to 28.7% and also interacted with the loci flanked by Xsnp4177- Xsnp4179 on chromosome 5D to increase FLA. The favorable alleles for FLW, FLL, and FLA at this locus came from SS8641. Given its significant pleiotropic effects, additional markers are needed in order to resolve the QTL position more precisely and to develop reliable diagnostic markers for MAS. In a previous study, Jia et al. (2013a) found this region to be involved in epistatic interactions and contributed to FLL in the Nanda2419×Wangshuibai population. Similarly, in my study, QFlw.cz- 2A.2 interacted with locus Xsnp4177-Xsnp4179 on chromosome 5D and locus Xwmc496-Xsnp1970 on chromosome 1A to contribute to the expression of FLA. In the nearby region of QFlw.cz-2A.2, a consistent QTL QFlw.cz-2A.3 was detected. QFlw.cz-2A.3 was significant for FLW in all four field environments with LOD score ranging from 3.4 to 4.8 and was related to the QTLs associated with plant height (Kulwal et al., 2003) and yield components (Zhang et al., 2010). On chromosome 2D, there were two major QTLs: QFlw.cz-2D.1 and QFlw.cz-2D.3. QFlw.cz-2D.1 co- localized with QPht.cz-2D.1. QFlw.cz-2D.3 was co-located with QFla.cz-2D.3 for FLA with favorable alleles from MDW233. In the same region with QFlw.cz-2D.3/ QFla.cz-2D, a QTL with additive effects for FLL, FLW, and heat susceptibility index (HIS) was reported by Mason et al. (2013) where its Halberd allele was favorable for a longer or wider flag leaf and also improved heat tolerance. It was noticeable that FLW QTLs on chromosome 6A had same direction additive effects as well as the ones on 3B but the direction associated with QTLs on 6A was opposite to that of QTLs on 3B suggesting an antagonistic relationship. Other major QTLs associated with FLW, such as QFlw.cz-3A and QFlw.cz-5A, were related to grain yield, grains 91 m-2, spikes m-2, and grains per spike as reported by Dilbirligi et al. (2006) and Kato et al. (2000). Thirteen QTLs were detected for FLL. The MDW233 alleles increased FLL at four loci located on chromosomes 7A, 5B, and 2D accounting for 14.4-20.7% of the phenotypic variation whereas SS8641 increased FLL at the other nine loci on 1A, 2A, 4A, 6A, 1B, 1D, and 5D, accounting for 6.4-18.5% of the phenotypic variation. Among them, two QTLs (QFll.cz-1A.1 and QFll.cz-1A.2) on chromosome 1A overlapped at Xbarc28 which also flanked QFla.cz-1A.1 and QFla.cz-1A.2 for FLA. At these four loci, favorable alleles were from SS8641 and explained 9.1-18.5% of the phenotypic variation. In previous studies, Xbarc28 was also linked to QTLs for spike length (Marza et al., 2006). Additionally, the same region was also associated with QTLs and meta-QTLs for yield components (Zhang et al., 2010). These results suggested the existence of important genes/QTLs and that high resolution mapping would be necessary to determine if the effects were due to pleiotropy or closely linked QTLs. Two major QTLs on chromosome 4A and 5B contributed more than 1 cm to FLL in the 2012 greenhouse study. The SS8641 allele increased FLL at QFll.cz-4A but decreased FLL at QFll.cz-5B.1. Both of these two QTLs were located in the same region associated with agronomic traits such as spike length, spike compactness, and plant height (Sourdille et al., 2003). Moreover, major QTL QFll.cz- 5D was significant for both additive and epistatic interaction effects. This same region was also reported to contain QTLs for grain quality traits related to dough physical properties (Huang et al., 2006) and epistatic QTLs for yield related traits 92 such as grains spike-1 and 100-grain weight (Jia et al., 2013a). Furthermore, a PHT QTL on chromosome 7A (McCartney et al., 2005) was located in the same region as QFll.cz-7A identified in this study. This region, flanked by Ppd-D1-Xsnp2869, was associated with both FLL and FLA explaining an average of 16.6% of the phenotypic variation across four environments. This is possibly due to the pleiotropic effects of Ppd-D1 which accelerates wheat development in long days and affects the number of leaf and spikelet primordia number (Borràs-Gelonch et al., 2012; Foulkes et al., 2004). QTLs for the derived traits FLS and FLA were also identified. Of the eleven QTLs detected for FLS, nine (81.8%) explained more than 10% of the phenotypic variation and QTLs on 5A, 7A, 2B, and 3D were detected in more than one environment. In addition, the twelve QTLs identified for FLA explained, on average, 13.5% of the phenotypic variation. To my knowledge, these are some of the first QTLs reported for these leaf morphology traits in wheat. Genetic complexity of plant architecture Compared with studies involving only additive QTLs (Bian et al., 2014; Xue et al., 2008a), I also examined epistatic effects and their interactions with environment revealing additional information on the genetic composition of plant architecture traits. In the six environments included in this study, seventy four additive QTLs and twelve pairs of epistatic QTLs were identified. Among them four additive QTLs and one pair of epistatic QTLs interacted with the environment. The results showed that both additive and epistatic effects were essential genetic bases of wheat plant 93 architecture and their effects were subject to environment modifications. The relative magnitude of these effects is shown in Figure 3.4. This indicated that, among all genetic effects, additive effects were the main contributors (>70%) to plant architecture variation in this DH population. It is interesting to note that only four significant additive QTLs were involved in epistatic interactions suggesting that epistasis can contribute to quantitative traits expression through the interactions of non-significant loci. Similarly, Zhang et al. (2008) found that 25% of additive-effect QTLs were involved in the epistatic interactions in wheat plant height. Additionally, I found that the locus flanked by Xsnp3389-Xsnp3344 (significant additive effect for FLW) on 2B and the locus flanked by Xsnp4177-Xsnp4179 (significant additive effect for FLL) on 5D, contributed to PHT and FLA respectively, when they were involved in epistatic interactions. This suggests that QTLs may express pleiotropic effects through their interactions with other loci. Furthermore, the additive effect of QPht.cz-7A.1 was reduced after taking into account its epistatic interaction with the locus flanked by Xsnp3754-Xsnp4981 and that the additive effect of QFla.cz-2A.1 was enhanced by interacting with QFll.cz-5D. These antagonistic and synergistic epistatic interactions not only added complexity to the genetic control of plant architecture traits but also provides important information for designing schemes to pyramid beneficial alleles in breeding programs. Conclusion This study is one of the few dedicated to QTL mapping of plant architecture traits in hexaploid wheat. I identified several new QTLs and QTL clusters that were shown to affect the expression of PHT, FLL, FLW, FLA, and FLS such as QPht.cz-2D.2 for 94 PHT, QFll.cz-1A.1 for FLL, and the QTL clusters on chromosome 6A and 3B for FLW. Those QTLs could be used for marker assisted selection in breeding programs to modify plant architecture traits. 95 Chapter 4: Quantitative trait loci mapping of spike characteristics in a doubled haploid population of soft red winter wheat Abstract Understanding the genetic basis of spike characteristics in wheat is important for breeding wheat cultivars with higher yield potential. In this study, a doubled haploid population of 124 lines was used to evaluate six spike traits 1) spike length (SL), 2) fertile spikelet number per spike (FSN), 3) sterile spikelet number per spike (SSN), 4) total spikelet number per spike (TSN), 5) spike compactness (SC), and 6) grains per spikelet (GSP). Quantitative trait loci (QTL) mapping was conducted based on the data collected from five year-location trials. A total of 109 QTLs were detected for all traits. In addition, 13 QTL-by-environment and 20 epistatic interactions were also identified. Major QTLs QSl.cz-1A/ QFsn.cz-1A for SL and FSN explained up to 30.9% of the phenotypic variation, QGsp.cz-2B.1 for GSP explained up to 15.6% of the phenotypic variation, and QSc.cz-5A.3 for SC explained up to 80.2% of the phenotypic variation. When combining the digenic interaction effect, the average contribution of QFsn.cz-1A to FSN in each environment was enhanced by 19%. QTLs for correlated traits in the same genomic region formed QTL clusters on chromosomes 1A, 5A, 2B, 3B, 5B, 1D, and 5D.The findings of this study will aid in the improvement of wheat spike characteristics and hence the grain yield potential in breeding programs. 96 Introduction Wheat (Triticum aestivum L.) is a major food crop across the globe. Improving its yield potential has irrefutable importance in meeting the food demand from increasing population worldwide. The grain yield of wheat is largely determined by yield components out of which the three most important are spikes per unit area, grains per spike, and grain weight (Dilbirligi et al., 2006; Mengistu et al., 2012). Previous studies have shown that grain yield variation is mostly associated with grain number changes where grain number, expressed as grains m-2, is the product of spikes m-2 and grains per spike and that there appears to be less opportunity for genetic yield improvement by selecting heavier grains (Fischer, 2011; Frederick and Bauer, 1999). Increases in grains per spike or/and spikes m-2 have contributed to wheat yield improvement in the past decades (Ma et al., 2007b). Spike characteristics including spike length (SL), total spikelet number per spike (TSN), fertile spikelet number per spike (FSN), sterile spikelet number per spike (SSN), spike compactness (SC), and grains per spikelet (GSP) determine the number of grains per spike, and thus, to a certain extent, determine the yield potential. Spike characteristics are quantitative traits under quantitative trait loci (QTL) control and subject to environmental influence (Cui et al., 2012; Ma et al., 2007b). Genetic dissection of spike characteristics could facilitate improving grain yield potential of wheat. Several domestication genes, such as Q, compactum (C), and sphaerococcum (S1) are related to wheat spike morphology and have been identified on chromosomes 5A, 2D, and 3D respectively (Faris et al., 2003; Faris and Gill, 2002; Johnson et al., 97 2007a; Rao, 1977). The Q gene confers a free-threshing spike and pleiotropically influences many other domestication related traits, including plant height, glume keeledness, rachis toughness, spike type and spike emergence time, resulting in tougher stems and higher yields (Faris et al., 2003; Simons et al., 2006; Sormacheva et al., 2014). The C gene is located on the long arm of chromosome 2D near the centromere and affects spike compactness, grain size, grain shape, and grain number per spike (Johnson et al., 2007a). The S1 gene confers rigid short culms, straight flag leaves, dense spikes, hemispherical glumes, and small spherical grains (Rao, 1977). In addition to these loci, previous studies have identified genomic regions associated with spike-related traits on all twenty one wheat chromosomes (Borner et al., 2002; Cui et al., 2012; Deng et al., 2011; Kumar et al., 2007; Ma et al., 2007b; Marza et al., 2006; Wang et al., 2011). For example, Cui et al. (2012) detected 190 QTLs across all wheat chromosomes for seven spike-related traits in two recombinant inbred line populations. Eighteen of the detected QTLs were major QTLs and were significant across multiple environments. Ma et al. (2007b) investigated the additive, dominant and epistatic effects of QTLs for SL, FSN, SSN, TSN, and SC in a recombinant inbred line population and also from an immortalized F2 population derived from the same parents and found 18 genomic regions on chromosomes 1A, 1B, 2D, 3B, 4A, 5A, 5B, and 7A to be associated with spike characteristics. Additionally, Kumar et al. (2007) identified QTLs for SL on chromosomes 1A, 1B, 1D, 2B, 2D, 4A, 5A, and 5D and QTLs for TSN on 2D, 4A, 4D, 5A, and 6A. These results demonstrated that multiple loci with unequal effects can affect spike traits and that epistasis and dominance effects are also indisputable components of genetic architecture of spike 98 characteristics. Furthermore, mapping agronomically important QTLs as Mendelian factors in wheat was also reported by Uauy et al. (2006) after rice (Ashikari et al., 2005) and tomato (Frary et al., 2000). Similarly, Deng et al. (2011) investigated wheat spike traits in a F2 population, derived from the cross between an elite cultivar Laizhou953 and an introgression line 05210 (in Laizhou953 background). This population showed a clear 3:1 segregation ratio for spike number per plant, spike length, and grain number per spike. The underlying QTL was mapped to chromosome 4B and explained 30.1 to 67.6% of the phenotypic variation in two environments. Fine mapping and molecular characterization of this region have not been reported yet. In this study, I used a doubled haploid population derived from two soft red winter wheat cultivars that showed a wide range of phenotypic variation for spike characteristics. A previously constructed linkage map that spanned 1978 cM was used to study the genetic basis of six spike traits (Chapter 2 of this dissertation). The objectives of this study were to identify QTLs affecting spike characteristics as well as their closely linked markers for use by breeding programs and future fine mapping. Materials and Methods Genetic resources and phenotypic traits evaluation A doubled-haploid (DH) population derived from a cross between a soft red winter wheat germplasm line MD01W233-06-1 (MDW233) (Costa et al., 2010) and a soft red winter wheat cultivar SS8641 (Johnson et al., 2007b) was used. MDW233 carries the Rht-D1b dwarfing gene the Ppd-D1b photoperiod sensitive allele as well as the 99 1RS/1AL translocation. A genetic linkage map with single nucleotide polymorphism (SNPs), simple sequence repeats (SSRs), and a morphological marker (coleoptile color) was previously constructed with an average interval length of 2.3 cM . The DH population, comprised of 124 lines, and its two parents were evaluated at five year-location environments in Maryland and North Carolina: Clarksville, MD 2013 (E1), Clarksville, MD 2014 (E2), Queenstown, MD 2013 (E3), Queenstown, MD 2014 (E4), and Kinston, NC 2014 (E5). The population was grown in field plots arranged in a randomized complete block design with two replications. Each field plot consisted of seven rows separated by 15.2 cm. Seed density was 22 seeds per 0.305 m in each row. Soil fertility management followed recommended management practices for each location. All trials were sprayed with the metconazole fungicide (Caramba®, BASF Corporation) at anthesis to reduce potential infection by Fusarium graminearum and other diseases. Ten plants in the middle rows from each plot were randomly selected for spike traits evaluation. Traits examined included spike length (SL) in centimeters, measured from the base of the rachis to the top of the uppermost spikelet, fertile spikelet number per spike (FSN), and sterile spikelet number per spike (SSN). Total spikelet number per spike (TSN) was equal to FSN plus SSN. Spike compactness (SC) was derived by dividing TSN by SL and grains per spikelet (GSP) was derived by dividing grain number per spike by FSN. 100 Phenotypic data analysis Phenotypic data analysis was performed using SAS version 9.3 (SAS Institute, Raleigh, NC 2013) to compare differences among DH lines and environments. Phenotypic value for SL, FSN, SSN, TSN, SC, and GSP for 10 plants from each DH line in each replication was averaged before analyses. Simple summary statistics for six spike traits were calculated by the PROC MEANS procedure of SAS. Analysis of variance (ANOVA) for SL, FSN, SSN, TSN, SC, and GSP was performed separately for each environment and for five environments combined by the PROC GLM procedure. The linear model for ANOVA for single environment analysis was Yij=µ + gi+ rj+ɛij, where µ is the overall mean, Yij is the phenotypic value of the ith DH line in jth replication, gi is the fixed effect of the ith DH line, rj is the fixed effects of jth replication, and ɛij is the random effects of error associated with Yij and for combined analysis Yijk=µ + gi+ rjk + ek + ɛijk, where µ is the overall mean, Yijk is the phenotypic value of the ith DH line in jth replication of kth environment, gi is the fixed effect of the ith DH line, rjk is the fixed effects of jth replication of kth environment, ek is the fixed effect of the kth environment, and ɛijk is the random effect of error associated with Yijk. Pearson’s correlation coefficients were calculated by the PROC CORR procedure to detect the association among spike traits. Broad-sense heritability (h2) (defined as h2= ???/(???+(???? /?)+ (???/??)), where ??? is the variance of genotypic effect, ???? is the genotype × environment variance, and ? and ? are the number of environments and replicates, respectively) for each trait was calculated on a family mean basis by the PROC MIXED procedure, as described by Holland et al. (2003). 101 QTL detection Mapping QTLs for spike characteristics was performed using two methods. First, inclusive composite interval mapping (ICIM) was conducted to detect QTLs with additive effects by the ICIM-ADD module of IciMapping version 4.0 (Li et al., 2008). The walking speed for all traits was 1 cM. Reference LOD values were determined by 1, 000 permutations (Doerge, 2002). Type I error to determine the LOD from the permutation test was 0.05 and the LOD threshold to declare the presence of a significant QTL was 3.0. Secondly, QTL epistasis (Q×Q), QTL× environment (Q×E) and epistasis × environment (QQ×E) interaction effects were detected by QTLNetwork version 2.1 using a mixed-model based composite interval mapping (MCIM) (Wang et al., 1999; Yang et al., 2007). Q×E, Q×Q, and QQ×E effects were estimated by the Monte Carlo Markov Chain method with a scanning speed of 1 cM step with the experiment-wise type I error for putative QTL detection of 0.05. In both methods, the position at which the LOD score curve reached its maximum was used as the estimate of the QTL location. Results Phenotypic analysis Five different field trials were conducted at three locations over two years to evaluate spike characteristics of the DH population as well as the parental genotypes MDW233 and SS8641. Mean values of traits at each trial are shown in Table 4.1. SS8641 had longer spikes, also more fertile and total spikelets per spike as well as more grains per spikelet; MDW233 had more sterile spikelets per spike. The compactness was similar between the parents. In all trials, the DH population showed 102 significant variation and transgressive segregation was obvious with data distributed beyond the parental values, suggesting polygenic inheritance of the investigated traits (Table 4.1). ANOVA results showed that significant differences existed between DH lines and between environments at p<0.001 level in the performance of six spike traits (Table 4.2). Estimates of heritability (on a family mean basis) of the traits varied from trait to trait, ranging from 88% to 95%. The TSN had the highest heritability of 95% whereas GSP had the lowest (Table 4.2). Correlation coefficients among the spike traits in different trials are presented in Table 4.3. SL showed a significant positive correlation with FSN and TSN but a negative correlation with SC across all five environments. There was a positive correlation between TSN and FSN. A positive correlation was also found between TSN and SC. SC was positively correlated with SSN, FSN and TSN in almost all of the environments. GSP was negatively correlated with SSN and had no significant relationships with both SL and FSN except in E3. Significant negative correlations were also observed between GSP and TSN in E1 and E2 so was GSP and SC in E1, E2, and E3. The strongest correlation was observed between TSN and FSN. 103 Table 4.1 Phenotypic values for spike characteristics: spike length (SL, cm), sterile spikelet number per spike (SSN), fertile spikelet number per spike (FSN), total spikelet number per spike (TSN), spikelet compactness (SC), and grain number per spikelet (GSP) in the MD01W233-06-1 × SS8641 doubled haploid population evaluated in five field trials from 2013 to 2014: Clarksville 2013 (E1), Clarksville 2014 (E2), Queenstown 2013 (E3), Queenstown 2014 (E4), Kinston 2014 (E5). Parents DH lines Traits Environments MDW233 SS8641 Mean Std. Dev. Minimum Maximum CV§ SL E1 6.6 7.3 7.0 0.5 5.7 8.4 7.5% E2 6.9 7.7 7.3 0.5 6.3 8.5 6.9% E3 7.0 8.0 7.2 0.5 6.0 8.6 7.3% E4 6.1 6.9 6.8 0.5 5.6 8.3 7.5% E5 7.1 8.4 7.5 0.5 6.2 9.0 7.1% FSN E1 13.0 15.7 14.3 1.1 11.8 17.3 7.6% E2 14.4 16.5 15.2 0.9 13.0 17.6 6.0% E3 14.6 16.3 14.9 1.0 12.5 17.4 6.8% E4 12.5 13.8 14.0 0.9 11.5 16.2 6.3% E5 14.8 17.0 16.1 1.2 13.7 20.2 7.4% SSN E1 2.1 1.8 1.8 0.5 0.8 3.1 25.4% E2 1.9 1.8 2.2 0.4 1.3 3.6 20.3% E3 1.6 1.1 1.7 0.5 0.3 3.5 31.8% E4 1.6 1.5 1.2 0.4 0.3 2.5 34.1% E5 2.5 1.8 2.4 0.6 1.2 4.1 23.0% TSN E1 15.1 17.5 16.2 1.1 14.1 19.2 6.6% E2 16.2 18.3 17.4 1.0 15.2 20.2 5.6% E3 16.1 17.4 16.6 1.1 14.3 19.4 6.5% E4 14.0 15.3 15.2 0.9 13.0 17.7 6.0% E5 17.2 18.8 18.5 1.3 16.1 22.3 6.9% SC E1 2.3 2.4 2.3 0.2 2.0 2.9 6.5% E2 2.4 2.4 2.4 0.1 2.0 2.8 5.5% E3 2.3 2.2 2.3 0.1 1.9 2.7 6.4% E4 2.3 2.2 2.3 0.1 2.0 2.6 5.3% E5 2.4 2.2 2.5 0.2 2.1 3.0 6.7% GSP E1 2.7 2.9 2.8 0.2 2.1 3.3 8.5% E2 2.3 2.4 2.2 0.2 1.8 2.6 8.1% E3 3.0 3.1 2.8 0.3 2.2 3.4 9.2% E4 2.8 2.8 2.8 0.2 2.3 3.3 7.1% E5 2.4 2.7 2.6 0.2 2.3 3.2 7.2% § coefficient of variation 104 Table 4.2 Pooled analysis of variance and heritability estimates for spike characteristics: spike length (SL, cm), sterile spikelet number per spike (SSN), fertile spikelet number per spike (FSN), total spikelet number per spike (TSN), spikelet compactness (SC), and grain number per spikelet (GSP) in the MD01W233-06-1 × SS8641 doubled haploid population evaluated in five field trials from 2013 to 2014. Mean square Source of Variation df SL SSN FSN TSN SC SSP Environment 4 20.82*** 55.20*** 161.70*** 388.27*** 1.71*** 17.08*** Rep (environment) 5 0.89*** 2.74*** 4.41*** 4.68*** 0.05*** 3.62*** Genotype 123 2.25*** 1.65*** 7.94*** 8.92*** 0.17*** 0.31*** Genotype × environment 492 0.11*** 0.18*** 0.64*** 0.63*** 0.01*** 0.04*** R2 0.91 0.87 0.89 0.95 0.91 0.91 Heritability (h2) 0.95 (0.01) 0.89 (0.02) 0.92 (0.01) 0.92 (0.01) 0.94 (0.01) 0.88 (0.02) * Significant at the 0.05 probability level. ** Significant at the 0.01 probability level. *** Significant at the 0.001 probability level. Table 4.3 Pearson correlation coefficients among spike characteristics: spike length (SL, cm), sterile spikelet number per spike (SSN), fertile spikelet number per spike (FSN), total spikelet number per spike (TSN), spikelet compactness (SC) in the MD01W233-06-1 × SS8641 the doubled haploid population evaluated in five field trials from 2013 to 2014. * Significant at the 0.05 probability level. ** Significant at the 0.01 probability level. *** Significant at the 0.001 probability level. Environments SSN FSN TSN SC GSP Clarksville 2013 SL -0.26** 0.68*** 0.58*** -0.55*** 0.14 SSN -0.26** 0.17 0.47*** -0.39*** FSN 0.91*** 0.15 -0.05 TSN 0.36*** -0.22* SC -0.38*** Clarksville 2014 SL 0.00 0.66*** 0.61*** -0.60*** 0.07 SSN -0.09 0.37*** 0.36*** -0.62*** FSN 0.89*** 0.10 0.05 TSN 0.25** -0.23* SC -0.31*** Queenstown 2013 SL -0.18* 0.71*** 0.58*** -0.56*** 0.25** SSN -0.14 0.37*** 0.56*** -0.62*** FSN 0.87*** 0.07 0.23** TSN 0.35*** -0.09 SC -0.36*** Queenstown 2014 SL 0.03 0.71*** 0.71*** -0.60*** 0.06 SSN -0.19* 0.27** 0.27** -0.39*** FSN 0.90*** 0.01 0.15 TSN 0.13 -0.03 SC -0.12 Kinston 2014 SL -0.05 0.62*** 0.55*** -0.50*** 0.13 SSN -0.06 0.38*** 0.45*** -0.40*** FSN 0.90*** 0.27** 0.16 TSN 0.44*** -0.03 SC -0.16 105 QTL detection Up to 109 putative additive QTLs for the six spike traits were detected by ICIM (Table 4.4) and their map positions are shown in Figure 4.1. The number of QTLs detected for each trait ranged from 16 to 22, specifically, 20 for FSN, 12 for SC, 20 for SL, 19 for SSN, 16 for GSP, and 22 for TSN. These QTLs were located on 15 chromosomes and formed QTL clusters. In addition, 21 regions were detected to be associated with more than one trait. Spike length Twenty chromosome regions were identified to govern SL in the present study. Chromosome 1A and 1D each had one QTL. Chromosome 3A and 6A each had two QTLs. Three QTLs were detected on each of the chromosomes 3B, 2D, and 5D, and five QTLs were detected on 5A. Major QTLs (PVE >10%) for SL were identified on chromosomes 1A, 5A, 6A, 3B, and 5D. SS8641 alleles were associated with longer spikes at seventeen (85%) loci whereas MDW233 alleles were associated with longer spike at the other three loci on chromosome 2D. QTL QSl.cz-1A was detected in four environments (E1, E2, E3, and E5) and mapped to the interval Xsnp1970- Xbarc28 on chromosome 1A (Figure 4.1), explaining 9.2-23.6% of the phenotypic variation of SL. This QTL was also significantly associated with FSN and TSN explaining 16.7 to 30.9% and 8.4 to 20.2% of the phenotypic variation of FSN and TSN, respectively. In all cases, the favorable alleles were contributed by SS8641 and the additive effects of QSl.cz-1A were the largest among all QTLs for SL, FSN, and TSN suggesting an essential region for spike characteristics. Major SL QTL QSl.cz-3B.1 localized in the 106 same interval with FSN QTL QFsn.cz-3B.1. QTLs formed clusters on chromosomes 5D, 3B, and 5A. Spike compactness Twelve QTLs, distributed on six chromosomes, were significantly associated with SC. The major QTL QSc.cz-5A.3 was detected in E2, E3, and E5 and had mostly large additive effects explaining up to 80.2% of the phenotypic variation. QSc.cz-5A.1 also explained a large portion of the observed variation (26.7%) in E1. Additionally, QSc.cz-5A.3 clustered with QSc.cz-5A.1 and QSc.cz-5A.2. Clustering of consistent major QTLs was also identified on chromosome 2B and 5D. MDW233 contributed positive alleles at clusters on 5A and 5D whereas SS8641 increased SC at loci on chromosome 3A, 2B, 5B, and 6D. Grains per spikelet Sixteen QTLs were detected for GSP. They were distributed on chromosomes 1A, 5A, 6A, 7A, 2B, 3B, 5B, 6B, 1D, and 2D. QTL QGsp.cz-2B.1 was detected in E1, E3, E4, and E5 accounting for 6.0-15.6% of the phenotypic variation and mapped to a position close to the major QTL QGsp.cz-2B.2 (LOD=7.4, PVE=13.7%). Four major QTLs mapped to similar positions and overlapped along the short arm of chromosome 5B explaining 11.6 to 14.5% of the phenotypic variation. Another two major QTLs QGsp.cz-1A.2 and QGsp.cz-2D explained 14.2 to 15.5% of the phenotypic variation, respectively. SS8641 contributed favorable alleles for QTLs on chromosomes 1A, 3B, 6B, and 2D. 107 Fertile spikelet number per spike Twenty QTLs significantly influenced FSN and mapped to nine chromosomes. QTLs on chromosomes 2D and 2A favored high FSN through MDW233 alleles and the rest were associated with high FSN through SS8641 alleles except for QTL QFsn.cz- 5A.1.The QTL on chromosome 1A, mapped to the interval Xsnp1970-Xbarc28, consistently showed a large effect on FSN. Another consistent QTL QFsn.cz-2D.2 was mapped to chromosome 2D with a LOD score of 3.3 to 5.3. For QFsn.cz-2D.2, the MDW233 allele increased FSN. The remaining QTLs were detected in only one environment. There were six major QTLs for FSN and the phenotypic variation explained by each individual QTL ranged from 10.4 to 30.9%. Sterile spikelet number per spike Nineteen QTLs were associated with SSN. For the QTLs located on chromosome 2A, 2B, 5B, and 3D, the SS8641 allele increased SSN, whereas for the QTLs on 1A, 3A, 2D, and 6D, the MDW233 alleles increased SSN. The phenotypic variation explained by these individual QTLs ranged from 3.7 to 30%. QTL QSsn.cz-2B.2 was identified in E1, E2, E3, and E4 as a major QTL, sharing this interval with QTsn.cz-2B.3 and QSc.cz-2B.3. QTL QSsn.cz-1A was coincident with QGsp.cz-1A.2. At locus Ppd-D1, QSsn.cz-2D.1 and QSsn.cz-2D.2 overlapped, each explaining 30% and 7.5% of the phenotypic variation, respectively. Total spikelet number per spike Twenty-two chromosome regions were associated with TSN. However, seventeen of them were only detected once. Consistent QTLs included QTsn.cz-1A, QTsn.cz-2D.2, QTsn.cz-2D.3, QTsn.cz-2D.4 and QTsn.ca-5D.1 explaining 8.4 to 20.9% of the 108 phenotypic variation. For QTLs detected on chromosome 2A, 6B, and 2D, MDW233 alleles decreased TSN. SS8641alleles increased TSN at the remaining loci. QTL clusters were found on chromosomes 2B, 2D, and 5D and the genetic effects of QTLs in each cluster were in the same direction. QTL× environment, epistasis, and epistasis× environment interactions In this study, I used a mixed-model based composite interval mapping method to estimate the QTL× environment (Q×E), epistasis (Q×Q), and epistasis× environment (QQ×E) interactions. Thirteen Q×E interactions were detected for SSN, FSN, and TSN, out of which eleven involved intervals associated with significant additive effects. The other two were non-significant QTLs (LOD<3) for additive effects. E4, E5, and E3 each had six, five, and two Q×E interactions, respectively. No Q×E interaction was detected in E1 and E2. The contribution of Q×E interactions ranged from 0.6-2.2%. Twenty pairs of Q×Q interactions were detected for all six traits in the DH population and three QQ×E interactions were also identified (Table 4.6). Twelve intervals involved in Q×Q interactions were significant for additive effects. The heritability estimates of Q×Q and QQ×E interactions ranged from 0.3 to 4.9% and 0.9 to 1.4%. 109 Xwmc4960.0 Xsnp19700.9 Xbarc281.7 Xsnp200513.6 Xsnp199914.4 Xsnp212915.3 Xsnp199718.2 Xsnp185519.1 Xsnp93920.8 Xsnp145221.6 Xsnp189522.4 Xsnp198323.3 Xsnp194328.1 Xsnp183430.0 Xsnp200131.0 Xsnp195336.9 Xsnp194037.7 Xsnp199338.7 Xsnp131641.2 Xsnp197743.6 Xsnp184048.7 Xsnp188950.4 Xsnp186251.3 Xsnp182353.8 Xsnp193154.8 Xsnp198655.6 Xsnp199658.7 Xsnp195059.6 Xsnp186862.3 Xsnp199563.2 Xsnp222767.7 Q Fsn.cz-1A Q Sl.cz-1A Q Ssn.cz-1A Q Ssp.cz-1A.1 Q Ssp.cz-1A.2 Q Tsn.cz-1A 1A Xsnp24770.0 Xsnp24322.7 Xsnp44906.8 Xsnp24457.9 Xsnp248014.0 Xsnp247114.9 Xsnp246117.6 Xsnp246622.8 Xsnp242723.6 Xsnp247925.3 Xsnp242326.3 Xsnp132729.1 Xsnp248131.6 Xsnp243634.4 Xsnp244839.8 Xsnp247547.3 Xsnp243148.1 Xsnp8749.0 Xsnp232349.8 Xsnp76850.7 Xsnp240051.5 Xsnp226852.4 Xsnp236653.3 Xsnp232054.2 Xsnp240455.0 Xsnp237755.9 Xsnp8159.1 Xsnp240960.0 Xsnp239061.7 Xsnp235162.6 Xsnp227764.2 Xsnp237265.1 Xsnp239466.0 Xsnp237568.0 Xsnp236569.0 Xsnp238273.7 Xsnp240175.6 Xsnp233978.7 Xsnp239779.6 Xsnp238181.7 Xsnp232182.7 Xsnp231387.0 Xsnp240587.8 Xsnp240688.8 Xsnp236392.9 Xsnp231593.8 Xsnp235396.3 Xsnp235097.3 Xsnp238398.2 Xsnp233799.1 Xsnp2355100.1 Xsnp2360102.9 Xsnp2362104.7 Xsnp494105.5 Xsnp2291108.0 Xsnp2398109.8 Xsnp533112.3 Q Fsn.cz-2A Q Ssn.cz-2A.1 Q Ssn.cz-2A.2 Q Tsn.cz-2A 2A Xsnp30270.0 Xsnp37440.9 Xsnp30481.8 Xsnp14663.6 Xsnp30494.4 Xsnp30218.8 Xsnp305912.5 Xbarc1213.4 Xsnp300814.3 Xsnp299323.2 Xsnp152225.1 Xsnp303426.7 Xsnp305127.7 Xsnp304628.6 Xsnp306431.4 Xsnp304032.2 Xsnp309433.1 Xsnp305634.0 Xsnp300534.9 Xsnp304145.1 Xsnp298879.1 Xsnp3065115.2 Xbarc45121.0 Xsnp3037123.6 Xsnp3023124.4 Xsnp3383126.1 Xsnp3009127.0 Xsnp3052127.8 Xsnp758128.7 Xsnp310129.5 Xsnp4288132.0 Xsnp1758134.7 Xsnp1485136.3 Xsnp2964137.1 Xsnp2885142.1 Xsnp2987143.0 Xsnp2937144.0 Xsnp4728147.9 Xsnp2968148.9 Xsnp443152.4 Xsnp2927153.3 Xsnp2924154.1 Xsnp2956155.9 Xsnp2950159.3 Xsnp3313160.2 Xsnp2977161.0 Xsnp2900161.9 Xsnp2973162.9 Xsnp2989168.0 Xsnp2983169.8 Xsnp2990173.9 Xsnp2905179.7 Xsnp2979180.7 Xsnp445181.5 Xsnp2984187.8 Xsnp2934188.7 Xsnp2970189.5 Xsnp2920197.5 Xsnp2960200.5 Xsnp2948201.4 Xsnp2951206.5 Xsnp2971215.9 Xsnp2909216.8 Q Fsn.cz-3A.1 Q Fsn.cz-3A.2 Q Fsn.cz-3A .3 Q Sc.cz-3A.1 Q Sc.cz-3A.2 Q Sc.cz-3A .3 Q Sl.cz-3A.1 Q Sl.cz-3A.2 Q Ssn.cz-3A .1 Q Ssn.cz-3A.2 Q Tsn.cz-3A 3A Xsnp38740.0 Xsnp38722.0 Xsnp38695.0 Xsnp38796.8 Xsnp387715.8 Xsnp62120.8 Xsnp174921.6 Xsnp136822.4 Xsnp27924.0 Xsnp387848.7 Xsnp61750.4 Xsnp84252.0 Xsnp21852.8 Xsnp4953.6 Xgwm30454.4 Xsnp99655.3 Xsnp383756.2 Xsnp383857.1 Xbarc10060.0 Xsnp484367.3 Xsnp381968.3 Xsnp378971.2 Xsnp384477.1 Xsnp385177.9 Xsnp416778.9 Xsnp376079.9 Xsnp383980.9 Xsnp381282.0 Xsnp385683.9 Xsnp377684.9 Xsnp385285.8 Xsnp384386.7 Xsnp382087.5 Xsnp200894.1 Xsnp447297.9 Xsnp385598.8 Xsnp85699.6 Xsnp3867101.2 Xsnp3836102.1 Xsnp3833102.9 Xsnp3863107.4 Xsnp3845108.2 Xsnp3865125.4 Xsnp3802127.1 Xsnp3783129.0 Xsnp3859134.4 Xsnp3853136.1 Xsnp3835143.1 Xsnp3761165.6 Xsnp3862170.5 Xsnp3787171.3 Xsnp663172.2 Xsnp3747174.0 Xsnp3849176.6 Xsnp3841186.2 Xsnp3803187.0 Xsnp3775188.0 Xsnp3860190.0 Q Fsn.cz-5A.1 Q Fsn.cz-5A.2 Q Fsn.cz-5A.3 Q Sc.cz-5A.1 Q Sc.cz-5A.2 Q Sc.cz-5A.3 Q Sl.cz-5A.1 Q Sl.cz-5A.2 Q Sl.cz-5A.3 Q Sl.cz-5A.4 Q Sl.cz-5A.5 Q Ssn.cz-5A.1 Q Ssn.cz-5A.2 Q Ssp.cz-5A Q Tsn.cz-5A 5A Xsnp42960.0 Xsnp427613.7 Xsnp424320.0 Xsnp429820.8 Xsnp427123.6 Xsnp443525.6 Xsnp428027.8 Xsnp424728.6 Xsnp425937.0 Xsnp428637.9 Xsnp429038.9 Xsnp429940.9 Xsnp424541.7 Xsnp36742.5 Xsnp427843.3 Xsnp448944.3 Xsnp426545.1 Xsnp418346.0 Xsnp421647.6 Xsnp421749.3 Xsnp422650.2 Xsnp422551.1 Xsnp421151.9 Xsnp418675.0 Xsnp422278.3 Xsnp49279.9 Xsnp419780.8 Xsnp47381.6 Xsnp422883.2 Xsnp421992.6 Xsnp7093.4 Xsnp436494.2 Xsnp420795.1 Q Sl.cz-6A.1 Q Sl.cz-6A.2 Q Ssp.cz-6A 6A 110 Xsnp47250.0 Xsnp47662.1 Xsnp13983.0 Xsnp47323.9 Xsnp47795.6 Xsnp47646.5 Xsnp47527.5 Xsnp47178.4 Xsnp47369.3 Xsnp472310.3 Xsnp472712.8 Xsnp466513.6 Xsnp468214.6 Xsnp471815.5 Xsnp475918.1 Xsnp477125.7 Xsnp477326.6 Xsnp477731.1 Xsnp474132.9 Xsnp473935.4 Xsnp469736.5 Xsnp476837.6 Xsnp130238.5 Xsnp476239.4 Xsnp475446.6 Xsnp473447.5 Xsnp466748.5 Xsnp471549.3 Xsnp472250.2 Xsnp469051.0 Xsnp466851.9 Xsnp474953.6 Xsnp32454.5 Xbarc12755.3 Xsnp137657.1 Xsnp474558.8 Xsnp477459.7 Xsnp476760.8 Xsnp476361.6 Xsnp474671.8 Xsnp467573.6 Xsnp477677.7 Xsnp84978.5 Xsnp477581.0 Xsnp155384.5 Xsnp167485.3 Xsnp477086.9 Xsnp473388.5 Xsnp475890.2 Xsnp475791.2 Xsnp476192.2 Xsnp477294.8 Xsnp454795.7 Xsnp453596.6 Xsnp4660101.0 Xsnp4637103.0 Xsnp4567105.4 Xsnp4946106.3 Xsnp4546107.1 Xsnp4624107.9 Xsnp4612109.6 Xsnp1385110.5 Xsnp4523112.1 Xsnp4584113.0 Xsnp4935114.7 Xsnp4622115.7 Xsnp4639117.4 Xsnp1181118.3 Xsnp321120.0 Xsnp4593120.8 Xsnp4588121.6 Xsnp4620123.3 Xgwm282124.1 Xsnp4563127.6 Xsnp4936129.3 Xsnp4602137.2 Xsnp4659138.1 Xsnp4658139.0 Xsnp4606149.1 Xsnp4570150.8 Xsnp4623152.6 Xsnp4536154.2 Xsnp4556155.1 Xsnp4557156.2 Xsnp4947159.1 Xsnp1727159.9 Xsnp4608160.9 Xwmc273167.4 Xsnp4655170.7 Xsnp4643171.5 Q Fsn.cz-7A Q Ssp.cz-7A Q Tsn.cz-7A 7A Xsnp27790.0 Xsnp27500.9 Xsnp277110.2 Xsnp278017.3 Xsnp373419.9 Xsnp274324.7 Xsnp45625.5 Xsnp74633.7 Xsnp278534.5 Xsnp277836.2 Xsnp276946.4 Xbarc1047.3 Xsnp274458.3 Xsnp77459.1 Xsnp270560.0 Xsnp275261.1 Xsnp278662.2 Xsnp277763.2 Xsnp277364.1 Xgwm31966.1 Xsnp276767.0 Xsnp259169.0 Xsnp256970.9 Xsnp268871.8 Xsnp269772.6 Xsnp259874.2 Xsnp268675.1 Xsnp263576.9 Xsnp264677.8 Xsnp260778.6 Xsnp341379.5 Xsnp265980.4 Xsnp269882.0 Xsnp257186.4 Xsnp266787.4 Xsnp266888.3 Xsnp269689.1 Xsnp251590.0 Xsnp117690.8 Xsnp269492.5 Xsnp264893.5 Xsnp261594.5 Xsnp268295.5 Xsnp261997.4 Xsnp267098.3 Xsnp2523100.2 Xsnp2620102.1 Xsnp2665103.8 Xsnp2633105.8 Xsnp2614107.7 Xsnp2649109.9 Xsnp2585115.9 Xsnp681116.8 Xsnp2666119.4 Xsnp2723122.1 Xsnp2574123.7 Xsnp448125.4 Xsnp2603130.3 Xsnp2586132.3 Xsnp2600133.9 Xsnp2679134.8 Xsnp2599137.1 Xsnp2802138.1 Xsnp2496139.0 Xsnp2663139.8 Q Sc.cz-2B .1 Q Sc.cz-2B.2 Q Sc.cz-2B .3 Q Ssn.cz-2B.1 Q Ssn.cz-2B .2 Q Ssp.cz-2B .1 Q Ssp.cz-2B .2 Q Tsn.cz-2B.1 Q Tsn.cz-2B.2 Q Tsn.cz-2B .3 2B Xsnp30740.0 Xsnp5991.7 Xsnp34074.3 Xbarc1475.1 Xsnp33289.2 Xsnp342113.5 Xsnp331815.5 Xsnp315617.4 Xsnp340418.2 Xsnp341119.3 Xsnp339920.3 Xsnp307021.3 Xsnp328822.4 Xsnp340523.3 Xsnp338927.2 Xsnp334431.0 Xsnp325335.3 Xsnp334941.8 Xsnp336742.7 Xsnp332043.5 Xsnp333544.3 Xsnp311946.3 Xsnp339547.3 Xsnp338748.1 Xsnp341549.9 Xsnp153951.6 Xsnp340852.4 Xbarc16453.2 Xsnp320555.1 Xsnp338555.9 Xsnp314656.7 Xsnp338257.6 Xsnp337260.9 Xsnp61161.9 Xsnp319262.7 Xsnp338163.6 Xsnp339364.5 Xsnp336866.2 Xsnp315367.1 Xsnp88768.8 Xsnp341072.5 Xsnp341673.4 Xsnp338674.3 Xsnp324775.4 Xsnp331276.2 Xsnp169779.6 Xsnp18780.4 Xsnp334281.3 Xsnp311285.6 Xsnp386487.3 Xsnp329988.2 Xsnp334593.1 Xsnp328993.9 Xsnp316097.3 Xsnp317498.1 Xsnp3397101.5 Xsnp3417102.4 Xsnp3418107.2 Xsnp3175109.0 Xsnp3401128.2 Xsnp3358129.1 Xsnp3400130.0 Xsnp326130.9 Xsnp3406131.8 Xsnp3403132.8 Xsnp3181134.0 Q Fsn.cz-3B.1 Q Fsn.cz-3B.2 Q Fsn.cz-3B.3 Q Sl.cz-3B.1 Q Sl.cz-3B .2 Q Sl.cz-3B.3 Q Ssp.cz-3B Q Tsn.cz-3B 3B Xsnp41400.0 Xsnp41140.9 Xsnp414211.4 Xsnp4812.2 Xsnp415213.8 Xsnp62515.6 Xsnp415016.6 Xsnp415517.7 Xsnp412018.7 Xsnp413019.5 Xsnp388420.3 Xsnp407921.2 Xsnp401622.1 Xsnp396423.0 Xsnp33025.0 Xsnp388225.8 Xsnp416827.4 Xsnp402828.3 Xsnp390129.5 Xsnp399730.5 Xsnp409231.3 Xsnp72532.2 Xsnp400533.0 Xsnp401834.1 Xsnp401435.0 Xsnp397536.7 Xsnp396337.7 Xsnp389138.8 Xsnp397344.9 Xsnp406245.9 Xsnp44646.7 Xsnp408347.6 Xsnp398857.5 Xsnp100658.3 Xsnp82359.1 Xsnp405964.8 Xsnp406166.4 Xsnp402768.1 Xsnp401769.8 Xsnp404971.5 Xsnp409072.3 Xsnp406073.2 Xsnp407274.2 Xsnp408578.2 Xsnp405879.2 Xsnp405080.9 Xsnp402082.6 Xsnp406883.6 Xsnp401294.7 Xsnp135696.5 Xsnp54998.9 Xsnp3983100.6 Xsnp797101.5 Xsnp794102.3 Xsnp4086103.2 Xsnp4089104.1 Xsnp29106.7 Xbarc59108.4 Xsnp4077113.0 Xsnp4011114.7 Xsnp4073117.9 Xsnp3970118.8 Xsnp4753122.4 Xsnp435123.2 Q Sc.cz-5B Q Ssn.cz-5B.1 Q Ssn.cz-5B.2 Q Ssn.cz-5B.3 Q Ssn.cz-5B.4 Q Ssp.cz-5B.1 Q Ssp.cz-5B.2 Q Ssp.cz-5B.3 Q Ssp.cz-5B.4 Q Tsn.cz-5B 5B Xsnp44340.0 Xsnp43810.9 Xsnp44562.7 Xsnp1073.6 Xsnp44454.5 Xsnp44445.6 Xsnp44539.7 Xsnp443714.1 Xsnp443315.0 Xsnp443616.7 Xsnp441917.5 Xsnp445820.3 Xsnp439821.2 Xsnp440223.0 Xsnp442028.1 Xsnp442436.4 Xsnp441341.4 Xsnp438048.8 Xsnp441857.8 Xbarc10160.7 Xsnp479361.6 Xsnp442162.5 Xsnp445164.2 Xsnp168365.0 Xsnp440565.8 Xsnp435866.8 Xsnp431287.9 Xsnp143893.8 Xsnp41696.2 Xsnp170398.6 Xsnp4326101.4 Xsnp4360106.4 Xsnp4359108.0 Xsnp4352108.9 Xsnp1582110.7 Xsnp4357112.4 Q Ssp.cz-6B Q Tsn.cz-6B 6B 111 Figure 4.1 Position of quantitative trait loci (QTLs) detected in a doubled haploid mapping population derived from MD01W233-06-1 × SS8641. Locus marker names are shown on the right side of the chromosomes and values to the left of chromosomes show the genetic distance (cM) for each marker. QTLs are labeled with trait abbreviations and the QTL number for each trait. QTLs for the same trait are in the same color. Xsnp13040.0 Xsnp22524.3 Xsnp6065.2 Xsnp223814.6 Xsnp223717.2 Xsnp164218.0 Xsnp224022.4 Xsnp223926.5 Xsnp225127.3 Xsnp225550.0 Xsnp225650.8 Xsnp224452.6 Xsnp222953.4 Xsnp223555.3 Xsnp223160.0 Xsnp222468.3 Xsnp223473.3 Xsnp223285.6 Q Fsn.cz-1D.1 Q Fsn.cz-1D .2 Q Sl.cz-1D Q Ssp.cz-1D .1 Q Ssp.cz-1D.2 Q Tsn.cz-1D.1 Q Tsn.cz-1D .2 1D Xsnp28700.0 Xsnp28545.8 Xsnp28199.4 Xsnp286812.5 Xsnp288113.4 Xsnp282319.6 Xsnp288220.5 Xgwm26125.7 Xsnp281027.5 Xsnp287528.5 Xsnp285029.4 Xsnp286247.0 PpdD161.7 Xsnp286976.4 Xsnp284477.4 Xsnp287778.5 Xsnp284879.5 Xsnp63480.4 Xsnp286381.3 Xsnp287682.1 Xsnp52285.6 Xsnp280888.5 Xsnp280489.4 Xsnp176691.2 Xsnp280995.5 Xsnp280696.4 Xsnp17999.7 Xsnp2790105.0 Xsnp2807107.8 Xsnp2788108.7 Xsnp2805109.5 Xsnp2795112.5 Xsnp708119.4 Xsnp1745125.9 Q Fsn.cz-2D .1 Q Fsn.cz-2D.2 Q Fsn.cz-2D .3 Q Fsn.cz-2D .4 Q Sl.cz-2D .1 Q Sl.cz-2D .2 Q Sl.cz-2D .3 Q Ssn.cz-2D.1 Q Ssn.cz-2D .2 Q Ssn.cz-2D .3 Q Ssp.cz-2D Q Tsn.cz-2D .1 QTsn.cz-2D .2 Q Tsn.cz-2D.3 Q Tsn.cz-2D .4 Q Tsn.cz-2D.5 2D Xsnp49800.0 Xsnp343116.6 Xsnp343418.6 Xsnp342528.1 Xsnp342735.0 Xsnp343035.8 Xsnp343236.7 Xsnp94238.3 Xsnp78639.1 Xsnp341947.6 Xsnp342256.0 Xsnp318772.7 Q Ssn.cz-3D .1 Q Ssn.cz-3D .2 3D Xsnp41770.0 Xsnp41793.5 Xsnp17516.7 Xsnp417522.1 Xsnp19823.8 Xsnp415624.6 Xsnp417327.1 Xsnp417828.1 Xgdm13647.9 Xsnp417256.3 Xsnp417178.9 Xsnp87689.5 Xsnp417094.6 Xsnp4157117.6 Xsnp1271124.4 Xsnp4163125.3 Xsnp4162139.4 Xsnp4169165.6 Xsnp4166178.6 Xsnp553179.5 Q Fsn.cz-5D.1 Q Fsn.cz-5D.2 Q Sc.cz-5D Q Sl.cz-5D.1 Q Sl.cz-5D.2 Q Sl.cz-5D.3 Q Tsn.cz-5D.1 Q Tsn.cz-5D.2 Q Tsn.cz-5D.3 Q Tsn.cz-5D.4 5D Xsnp45210.0 Xsnp45011.0 Xsnp45135.1 Xsnp450814.2 Xsnp449629.2 Xsnp450430.0 Xsnp451530.9 Xsnp451231.9 Xsnp451851.4 Xsnp448260.5 Xsnp81466.4 Xsnp447067.3 Xsnp449171.3 Xsnp4484130.0 Xsnp4488131.8 Xsnp4485136.1 Xsnp4465137.0 Xsnp4487138.8 Xsnp4468139.7 Xsnp4483141.4 Xsnp4486142.2 Xsnp230144.6 Q Sc.cz-6D Q Ssn.cz-6D 6D 112 Table 4.4 Quantitative trait loci (QTLs) for spike characteristics: spike length (SL, cm), sterile spikelet number per spike (SSN), fertile spikelet number per spike (FSN), total spikelet number per spike (TSN), spikelet compactness (SC), and grain number per spikelet (GSP) in the MD01W233-06-1 × SS8641 doubled haploid population evaluated in five field trials from 2013 to 2014. QTL Trait Environment Position (cM) Marker interval LOD score PVE (%) Additive effect QSl.cz-1A SL Clarksville 2013 1 Xsnp1970-Xbarc28 11.6 23.6 0.25 QSl.cz-1A SL Clarksville 2014 1 Xsnp1970-Xbarc28 5.7 11.1 0.17 QSl.cz-1A SL Kinston 2014 1 Xsnp1970-Xbarc28 6.1 9.2 0.16 QSl.cz-1A SL Queenstown 2013 1 Xsnp1970-Xbarc28 9.3 22.4 0.25 QSl.cz-3A.1 SL Clarksville 2013 1 Xsnp3744-Xsnp3048 3.7 6.5 0.13 QSl.cz-3A.2 SL Kinston 2014 4 Xsnp1466-Xsnp3049 5.0 7.3 0.15 QSl.cz-5A.1 SL Kinston 2014 70 Xsnp3819-Xsnp3789 8.2 13.0 0.20 QSl.cz-5A.1 SL Queenstown 2013 71 Xsnp3819-Xsnp3789 5.0 11.0 0.18 QSl.cz-5A.2 SL Clarksville 2014 72 Xsnp3789-Xsnp3844 6.2 12.5 0.18 QSl.cz-5A.3 SL Queenstown 2014 82 Xsnp3812-Xsnp3856 4.0 9.7 0.16 QSl.cz-5A.4 SL Clarksville 2013 86 Xsnp3852-Xsnp3843 7.0 13.1 0.19 QSl.cz-5A.5 SL Queenstown 2014 186 Xsnp3849-Xsnp3841 2.8 6.7 0.13 QSl.cz-6A.1 SL Queenstown 2013 87 Xsnp4228-Xsnp4219 3.4 7.8 0.15 QSl.cz-6A.2 SL Clarksville 2013 93 Xsnp4219-Xsnp70 6.0 11.0 0.17 QSl.cz-3B.1 SL Queenstown 2014 21 Xsnp3399-Xsnp3070 5.5 13.9 0.19 QSl.cz-3B.2 SL Clarksville 2013 44 Xsnp3320-Xsnp3335 5.3 9.5 0.16 QSl.cz-3B.2 SL Kinston 2014 44 Xsnp3320-Xsnp3335 6.6 9.9 0.17 QSl.cz-3B.3 SL Clarksville 2014 45 Xsnp3335-Xsnp3119 3.0 6.0 0.12 QSl.cz-3B.3 SL Queenstown 2013 45 Xsnp3335-Xsnp3119 5.4 12.1 0.18 QSl.cz-1D SL Kinston 2014 59 Xsnp2235-Xsnp2231 4.4 6.5 0.14 QSl.cz-2D.1 SL Kinston 2014 28 Xsnp2810-Xsnp2875 2.7 3.7 -0.11 QSl.cz-2D.2 SL Clarksville 2013 39 Xsnp2850-Xsnp2862 2.9 5.5 -0.12 QSl.cz-2D.3 SL Clarksville 2014 105 Xsnp179-Xsnp2790 3.0 5.6 -0.12 QSl.cz-5D.1 SL Clarksville 2014 37 Xsnp4178-Xgdm136 6.1 13.5 0.18 QSl.cz-5D.2 SL Queenstown 2014 93 Xsnp876-Xsnp4170 4.7 12.0 0.17 QSl.cz-5D.3 SL Kinston 2014 95 Xsnp4170-Xsnp4157 4.0 5.8 0.13 QFsn.cz-1A FSN Clarksville 2013 1 Xsnp1970-Xbarc28 14.2 30.9 0.60 QFsn.cz-1A FSN Clarksville 2014 1 Xsnp1970-Xbarc28 9.4 16.7 0.37 QFsn.cz-1A FSN Kinston 2014 1 Xsnp1970-Xbarc28 20.6 30.0 0.65 QFsn.cz-1A FSN Queenstown 2013 1 Xsnp1970-Xbarc28 11.2 23.9 0.49 QFsn.cz-2A FSN Clarksville 2014 43 Xsnp2448-Xsnp2475 4.2 7.2 -0.25 QFsn.cz-3A.1 FSN Kinston 2014 2 Xsnp3048-Xsnp1466 3.9 4.1 0.24 QFsn.cz-3A.2 FSN Clarksville 2013 112 Xsnp2988-Xsnp3065 6.1 12.4 0.39 QFsn.cz-3A.3 FSN Kinston 2014 148 Xsnp4728-Xsnp2968 5.0 5.3 0.27 QFsn.cz-5A.1 FSN Kinston 2014 0 Xsnp3874-Xsnp3872 4.3 4.5 -0.25 QFsn.cz-5A.2 FSN Clarksville 2014 97 Xsnp2008-Xsnp4472 2.8 4.5 0.20 QFsn.cz-5A.3 FSN Queenstown 2014 100 Xsnp856-Xsnp3867 3.2 9.3 0.27 QFsn.cz-7A FSN Clarksville 2014 29 Xsnp4773-Xsnp4777 4.1 6.9 0.24 QFsn.cz-3B.1 FSN Queenstown 2014 21 Xsnp3399-Xsnp3070 2.8 8.1 0.25 QFsn.cz-3B.2 FSN Kinston 2014 31 Xsnp3389-Xsnp3344 4.1 4.3 0.25 QFsn.cz-3B.3 FSN Queenstown 2013 35 Xsnp3344-Xsnp3253 4.7 8.9 0.30 QFsn.cz-1D.1 FSN Clarksville 2013 50 Xsnp2251-Xsnp2255 3.6 6.3 0.27 QFsn.cz-1D.2 FSN Kinston 2014 53 Xsnp2244-Xsnp2229 10.4 12.4 0.41 QFsn.cz-2D.1 FSN Kinston 2014 28 Xsnp2810-Xsnp2875 3.2 3.3 -0.22 QFsn.cz-2D.2 FSN Clarksville 2013 50 Xsnp2862-XPpdD1 3.3 6.3 -0.28 QFsn.cz-2D.2 FSN Queenstown 2013 56 Xsnp2862-XPpdD1 5.3 10.4 -0.33 QFsn.cz-2D.3 FSN Clarksville 2014 104 Xsnp179-Xsnp2790 6.7 11.6 -0.31 QFsn.cz-2D.4 FSN Kinston 2014 119 Xsnp2795-Xsnp708 3.6 3.7 -0.23 QFsn.cz-5D.1 FSN Queenstown 2014 25 Xsnp4156-Xsnp4173 2.7 7.7 0.25 QFsn.cz-5D.2 FSN Clarksville 2014 28 Xsnp4173-Xsnp4178 7.4 12.6 0.33 113 Table 4.4 Continued QTL Trait Environment Position (cM) Marker interval LOD score PVE (%) Additive effect QSsn.cz-1A SSN Clarksville 2013 5 Xbarc28-Xsnp2005 2.6 7.0 -0.12 QSsn.cz-2A.1 SSN Kinston 2014 13 Xsnp2445-Xsnp2480 3.8 5.4 0.13 QSsn.cz-2A.2 SSN Queenstown 2014 31 Xsnp1327-Xsnp2481 4.8 7.7 0.11 QSsn.cz-3A.1 SSN Queenstown 2014 170 Xsnp2983-Xsnp2990 8.1 13.8 -0.15 QSsn.cz-3A.2 SSN Clarksville 2013 177 Xsnp2990-Xsnp2905 3.9 9.8 -0.15 QSsn.cz-5A.1 SSN Kinston 2014 99 Xsnp3855-Xsnp856 5.8 8.4 -0.16 QSsn.cz-5A.2 SSN Kinston 2014 162 Xsnp3835-Xsnp3761 3.2 4.8 0.12 QSsn.cz-2B.1 SSN Kinston 2014 69 Xsnp2767-Xsnp2591 8.5 13.0 0.20 QSsn.cz-2B.2 SSN Clarksville 2014 73 Xsnp2697-Xsnp2598 5.3 14.4 0.17 QSsn.cz-2B.2 SSN Clarksville 2013 74 Xsnp2697-Xsnp2598 5.2 12.9 0.17 QSsn.cz-2B.2 SSN Queenstown 2013 74 Xsnp2697-Xsnp2598 6.3 18.3 0.23 QSsn.cz-2B.2 SSN Queenstown 2014 74 Xsnp2697-Xsnp2598 3.2 5.1 0.09 QSsn.cz-5B.1 SSN Clarksville 2014 58 Xsnp3988-Xsnp1006 2.9 7.5 0.12 QSsn.cz-5B.2 SSN Queenstown 2014 74 Xsnp4060-Xsnp4072 4.5 7.5 0.11 QSsn.cz-5B.3 SSN Kinston 2014 117 Xsnp4011-Xsnp4073 4.8 6.8 0.15 QSsn.cz-5B.4 SSN Queenstown 2014 119 Xsnp3970-Xsnp4753 6.0 10.0 0.13 QSsn.cz-2D.1 SSN Kinston 2014 60 Xsnp2862-XPpdD1 16.3 30.0 -0.31 QSsn.cz-2D.2 SSN Queenstown 2014 63 XPpdD1-Xsnp2869 4.5 7.5 -0.11 QSsn.cz-2D.3 SSN Clarksville 2013 78 Xsnp2844-Xsnp2877 5.6 14.4 -0.18 QSsn.cz-2D.3 SSN Queenstown 2013 78 Xsnp2844-Xsnp2877 3.6 10.2 -0.17 QSsn.cz-3D.1 SSN Kinston 2014 19 Xsnp3434-Xsnp3425 2.7 3.7 0.11 QSsn.cz-3D.2 SSN Clarksville 2014 35 Xsnp3427-Xsnp3430 2.7 6.8 0.12 QSsn.cz-6D SSN Queenstown 2014 0 Xsnp4521-Xsnp4501 3.1 4.8 -0.09 QTsn.cz-1A TSN Clarksville 2013 1 Xsnp1970-Xbarc28 9.5 19.7 0.47 QTsn.cz-1A TSN Clarksville 2014 1 Xsnp1970-Xbarc28 5.9 8.4 0.28 QTsn.cz-1A TSN Kinston 2014 1 Xsnp1970-Xbarc28 13.2 20.0 0.57 QTsn.cz-1A TSN Queenstown 2013 1 Xsnp1970-Xbarc28 9.3 14.1 0.40 QTsn.cz-2A TSN Clarksville 2014 49 Xsnp87-Xsnp2323 6.2 8.8 -0.29 QTsn.cz-3A TSN Clarksville 2013 115 Xsnp2988-Xsnp3065 5.4 10.3 0.34 QTsn.cz-5A TSN Queenstown 2014 100 Xsnp856-Xsnp3867 2.8 7.4 0.25 QTsn.cz-7A TSN Clarksville 2014 38 Xsnp4768-Xsnp1302 5.2 7.4 0.27 QTsn.cz-2B.1 TSN Clarksville 2014 62 Xsnp2752-Xsnp2786 2.8 3.8 0.19 QTsn.cz-2B.2 TSN Queenstown 2013 71 Xsnp2569-Xsnp2688 3.5 4.8 0.24 QTsn.cz-2B.3 TSN Clarksville 2013 73 Xsnp2697-Xsnp2598 3.5 6.4 0.27 QTsn.cz-3B TSN Queenstown 2013 42 Xsnp3349-Xsnp3367 3.5 4.6 0.23 QTsn.cz-5B TSN Kinston 2014 11 Xsnp4114-Xsnp4142 2.9 3.6 0.24 QTsn.cz-6B TSN Queenstown 2013 106 Xsnp4326-Xsnp4360 3.2 4.4 -0.23 QTsn.cz-1D.1 TSN Clarksville 2013 50 Xsnp2251-Xsnp2255 3.1 5.7 0.25 QTsn.cz-1D.2 TSN Kinston 2014 53 Xsnp2244-Xsnp2229 5.7 7.4 0.35 QTsn.cz-2D.1 TSN Kinston 2014 33 Xsnp2850-Xsnp2862 3.8 5.2 -0.29 QTsn.cz-2D.2 TSN Clarksville 2013 53 Xsnp2862-XPpdD1 5.4 11.2 -0.36 QTsn.cz-2D.2 TSN Queenstown 2013 59 Xsnp2862-XPpdD1 9.4 14.7 -0.42 QTsn.cz-2D.3 TSN Clarksville 2014 62 XPpdD1-Xsnp2869 4.2 5.9 -0.24 QTsn.cz-2D.3 TSN Kinston 2014 63 XPpdD1-Xsnp2869 8.1 11.5 -0.43 QTsn.cz-2D.3 TSN Queenstown 2014 68 XPpdD1-Xsnp2869 6.5 20.9 -0.41 QTsn.cz-2D.4 TSN Clarksville 2014 103 Xsnp179-Xsnp2790 4.9 7.4 -0.27 QTsn.cz-2D.4 TSN Queenstown 2013 105 Xsnp179-Xsnp2790 3.8 5.1 -0.24 QTsn.cz-2D.5 TSN Kinston 2014 120 Xsnp708-Xsnp1745 4.8 6.5 -0.33 QTsn.cz-5D.1 TSN Clarksville 2013 11 Xsnp1751-Xsnp4175 2.6 5.0 0.24 QTsn.cz-5D.1 TSN Kinston 2014 12 Xsnp1751-Xsnp4175 3.6 5.0 0.28 QTsn.cz-5D.2 TSN Queenstown 2014 26 Xsnp4156-Xsnp4173 4.6 12.6 0.32 QTsn.cz-5D.3 TSN Clarksville 2014 28 Xsnp4173-Xsnp4178 11.6 18.4 0.42 QTsn.cz-5D.4 TSN Queenstown 2013 29 Xsnp4178-Xgdm136 5.0 7.1 0.29 114 Table 4.4 Continued QTL Trait Environment Position (cM) Marker interval LOD score PVE (%) Additive effect QSc.cz-3A.1 SC Clarksville 2013 131 Xsnp310-Xsnp4288 4.1 7.7 0.04 QSc.cz-3A.2 SC Clarksville 2014 142 Xsnp2964-Xsnp2885 5.0 8.8 0.04 QSc.cz-3A.2 SC Queenstown 2013 142 Xsnp2964-Xsnp2885 3.6 7.4 0.04 QSc.cz-3A.3 SC Kinston 2014 216 Xsnp2971-Xsnp2909 3.7 6.5 0.04 QSc.cz-5A.1 SC Kinston 2014 48 Xsnp279-Xsnp3878 12.7 26.7 0.09 QSc.cz-5A.2 SC Clarksville 2013 54 Xsnp49-Xgwm304 6.3 12.7 -0.05 QSc.cz-5A.2 SC Queenstown 2014 54 Xsnp49-Xgwm304 3.8 9.3 -0.04 QSc.cz-5A.3 SC Clarksville 2014 55 Xgwm304-Xsnp996 10.9 21.9 -0.06 QSc.cz-5A.3 SC Kinston 2014 55 Xgwm304-Xsnp996 27.7 80.2 -0.15 QSc.cz-5A.3 SC Queenstown 2013 55 Xgwm304-Xsnp996 6.0 13.0 -0.05 QSc.cz-2B.1 SC Queenstown 2014 65 Xsnp2773-Xgwm319 5.8 15.4 0.05 QSc.cz-2B.1 SC Clarksville 2013 66 Xsnp2773-Xgwm319 8.7 18.3 0.06 QSc.cz-2B.1 SC Queenstown 2013 66 Xsnp2773-Xgwm319 9.3 21.6 0.07 QSc.cz-2B.2 SC Kinston 2014 71 Xsnp2569-Xsnp2688 9.4 18.2 0.07 QSc.cz-2B.3 SC Clarksville 2014 74 Xsnp2697-Xsnp2598 7.2 13.2 0.05 QSc.cz-5B SC Clarksville 2014 11 Xsnp4114-Xsnp4142 3.1 5.3 0.03 QSc.cz-5D SC Clarksville 2014 95 Xsnp4170-Xsnp4157 3.3 5.7 -0.03 QSc.cz-5D SC Kinston 2014 95 Xsnp4170-Xsnp4157 7.4 13.9 -0.06 QSc.cz-5D SC Queenstown 2014 95 Xsnp4170-Xsnp4157 6.6 16.8 -0.05 QSc.cz-5D SC Clarksville 2013 99 Xsnp4170-Xsnp4157 2.8 5.6 -0.04 QSc.cz-5D SC Queenstown 2013 100 Xsnp4170-Xsnp4157 3.5 8.1 -0.04 QSc.cz-6D SC Clarksville 2013 131 Xsnp4484-Xsnp4488 3.1 5.8 0.04 QGsp.cz-1A.1 GSP Clarksville 2013 0 Xwmc496-Xsnp1970 3.6 6.7 0.06 QGsp.cz-1A.2 GSP Queenstown 2013 3 Xbarc28-Xsnp2005 5.6 14.2 0.10 QGsp.cz-1A.2 GSP Clarksville 2014 5 Xbarc28-Xsnp2005 6.1 13.3 0.06 QGsp.cz-5A GSP Queenstown 2014 63 Xbarc100-Xsnp4843 3.8 9.7 -0.06 QGsp.cz-6A GSP Kinston 2014 95 Xsnp4364-Xsnp4207 2.9 7.4 -0.05 QGsp.cz-7A GSP Queenstown 2014 129 Xsnp4563-Xsnp4936 3.8 9.0 -0.06 QGsp.cz-2B.1 GSP Clarksville 2013 64 Xsnp2777-Xsnp2773 3.2 6.0 -0.06 QGsp.cz-2B.1 GSP Kinston 2014 64 Xsnp2777-Xsnp2773 5.8 15.6 -0.08 QGsp.cz-2B.1 GSP Queenstown 2013 64 Xsnp2777-Xsnp2773 5.4 12.9 -0.09 QGsp.cz-2B.1 GSP Queenstown 2014 64 Xsnp2777-Xsnp2773 4.2 9.9 -0.06 QGsp.cz-2B.2 GSP Clarksville 2014 72 Xsnp2688-Xsnp2697 7.4 13.7 -0.07 QGsp.cz-3B GSP Clarksville 2014 66 Xsnp3393-Xsnp3368 4.3 7.7 0.05 QGsp.cz-5B.1 GSP Queenstown 2014 47 Xsnp446-Xsnp4083 4.8 11.6 -0.07 QGsp.cz-5B.2 GSP Clarksville 2013 49 Xsnp4083-Xsnp3988 5.7 12.0 -0.08 QGsp.cz-5B.3 GSP Clarksville 2014 58 Xsnp3988-Xsnp1006 7.7 14.5 -0.07 QGsp.cz-5B.4 GSP Kinston 2014 59 Xsnp1006-Xsnp823 4.7 12.5 -0.07 QGsp.cz-5B.4 GSP Queenstown 2013 59 Xsnp1006-Xsnp823 3.4 7.9 -0.07 QGsp.cz-6B GSP Clarksville 2013 63 Xsnp4421-Xsnp4451 2.7 5.3 0.05 QGsp.cz-1D.1 GSP Queenstown 2013 53 Xsnp2244-Xsnp2229 2.7 6.0 -0.06 QGsp.cz-1D.2 GSP Clarksville 2014 85 Xsnp2234-Xsnp2232 4.1 7.1 -0.05 QGsp.cz-2D GSP Clarksville 2013 125 Xsnp708-Xsnp1745 7.5 15.5 0.09 115 Table 4.5 QTL × Environment interactions influencing spike characteristics: spike length (SL, cm), sterile spikelet number per spike (SSN), fertile spikelet number per spike (FSN), total spikelet number per spike (TSN), spikelet compactness (SC) and grain number per spikelet (GSP) in the MD01W233-06-1 × SS8641 doubled haploid population evaluated in five field trials from 2013 to 2014. † AE is the additive × environment interaction effect at each environment. E1: Clarksville 2013; E2: Clarksville 2014; E3: Queenstown 2013; E4: Queenstown 2014. ‡ h2(ae) is heritability estimate of the additive × environment interaction effect across four field trails. § Interval with significant additive effect. * Significant at the 0.05 probability level **Significant at the 0.01 probability level ** *Significant at the 0.001 probability level Trait QTL Interval Position AE1† AE2† AE3† AE4† AE5† h2(ae)‡ SSN 5A Xsnp3820-Xsnp2008 92.5 0.05* 0.9% SSN 2B Xsnp2591-Xsnp2569 69 -0.05* 0.7% SSN 2D Ppd-D1-Xsnp2869§ 67.7 -0.12*** 2.0% FSN 1A Xsnp1970-Xbarc28§ 0.9 -0.22*** 0.14** 2.2% FSN 2A Xsnp2448-Xsnp2475§ 44.8 0.10* 0.6% FSN 1D Xsnp2244-Xsnp2229§ 52.6 -0.10* 0.11* 0.9% TSN 1A Xsnp1970-Xbarc28§ 0.9 -0.15*** 0.13* 1.2% TSN 2D Xsnp2850-Xsnp2862§ 34.4 -0.12* 0.19** -0.16** 1.8% Figure 4.2 Distribution of genetic and non-genetic components for yield and yield related traits: spike length (SL, cm), sterile spikelet number per spike (SSN), fertile spikelet number per spike (FSN), total spikelet number per spike (TSN), spikelet compactness (SC), and grain number per spikelet (GSP). a) total number of QTLs detected for additive (a), additive × environment (ae), epistasis (aa), and epistasis × environment interactions (aae) effects. b) relative magnitude of a, ae, aa, aae effects. 116 Table 4.6 Digenic epistatic QTLs for spike characteristics: spike length (SL, cm), sterile spikelet number per spike (SSN), fertile spikelet number per spike (FSN), total spikelet number per spike (TSN), spikelet compactness (SC) and grain number per spikelet (GSP) in the “MD01W233-06-1 × SS8641” doubled haploid population evaluated in five field trials from 2013 to 2014. † The flanking markers, chromosome and position of the first interval involved in the epistasis. ‡ The flanking markers, chromosome and position of the second interval involved in the epistasis. § The additive × additive effect. ¶ The epistasis × environment effect at each environment. E1: Clarksville 2013; E2: Clarksville 2014; E3: Queenstown 2013; E4: Queenstown 2014; # The heritability estimate for additive × additive interaction effects across five environment. †† The heritability estimate for epistasis × environment interaction effects across four field trials. ‡‡ Interval with significant additive effect. * Significantly different from zero at the 0.05 probability level **Significant different from zero at the 0.01 probability level ** *Significant different from zero at the 0.001 probability level Trait Chr† Position† Interval† Chr‡ Position‡ Interval‡ AA§ E1¶ E2¶ E3¶ E4¶ E5¶ h2(aa)# h2(aae)†† SL 1A 0 Xwmc496-Xsnp1970‡‡ 5A 78.9 Xsnp4167-Xsnp3760 0.07*** 2.0% 0.2% SL 5A 78.9 Xsnp4167-Xsnp3760 2D 118.5 Xsnp2795-Xsnp708‡‡ -0.04** 0.9% 0.2% SL 5A 78.9 Xsnp4167-Xsnp3760 5D 44.1 Xsnp4178-Xgdm136‡‡ 0.07*** 2.3% 0.0% SL 2D 28.5 Xsnp2875-Xsnp2850 2D 118.5 Xsnp2795-Xsnp708§ 0.05*** 0.6% 0.1% SL 3A 148.9 Xsnp2968-Xsnp443 3A 58.8 Xsnp4745-Xsnp4774 -0.07*** 3.1% 0.4% SL 3B 93.1 Xsnp3345-Xsnp3289 3B 64.2 Xsnp3737-Xsnp3751 0.07*** 2.9% 0.2% SSN 3A 169.8 Xsnp2983-Xsnp2990‡‡ 2B 69 Xsnp2591-Xsnp2569 -0.05*** 1.0% 0.1% SSN 1A 51.3 Xsnp1862-Xsnp1823 2D 101.7 Xsnp179-Xsnp2790§ -0.07*** 2.5% 0.3% SSN 5A 1 Xsnp3874-Xsnp3872‡‡ 1B 87.1 Xsnp2091-Xsnp2052 0.01 0.07* 0.0% 1.4% SSN 7A 169.4 Xwmc273-Xsnp4655 5D 0 Xsnp4177-Xsnp4179 -0.08*** 2.6% 0.2% FSN 1A 0.9 Xsnp1970-Xbarc28‡‡ 5D 28.1 Xsnp4178-Xgdm136‡‡ 0.10*** 1.8% 0.0% FSN 2A 44.8 Xsnp2448-Xsnp2475‡‡ 3A 173.9 Xsnp2990-Xsnp2905‡‡ 0.08*** 0.7% 0.2% FSN 3A 173.9 Xsnp2990-Xsnp2905‡‡ 5D 28.1 Xsnp4178-Xgdm136‡‡ -0.12*** 0.6% 0.3% FSN 1A 38.7 Xsnp1993-Xsnp1316 6D 60.4 Xsnp4482-Xsnp814 0.12*** -0.12** 0.10* 1.6% 0.9% FSN 1B 137.2 Xsnp2067-Xsnp2127 2B 88.3 Xsnp2668-Xsnp2696 0.21*** 4.9% 0.3% TSN 2B 49.8 Xsnp2323-Xsnp768 2D 119.4 Xsnp708-Xsnp1745‡‡ -0.08** 0.3% 0.2% TSN 5B 81.9 Xsnp4050-Xsnp4020 1D 52.6 Xsnp2244-Xsnp2229‡‡ -0.11*** 0.8% 0.0% TSN 2D 34.4 Xsnp2850-Xsnp2862‡‡ 2D 119.4 Xsnp708-Xsnp1745‡‡ 0.15*** 1.3% 0.1% SC 6A 0 Xsnp4296-Xsnp4276 5B 103.2 Xsnp4086-Xsnp4089 0.02*** 2.0% 0.0% GSP 7A 15.5 Xsnp4718-Xsnp4759 5B 12.2 Xsnp48-Xsnp4152 0.05*** 4.2% 0.4% 117 Discussion There are very few QTL mapping studies on wheat spike characteristics that integrate additive, epistasis, additive × environment interaction, and epistasis × environment interaction effects. In this study, I evaluated a soft red winter wheat DH population to identify QTLs influencing six spike traits and to investigate their interactions. QTLs for spike characteristics In the present study, major QTLs for SL, FSN, TSN, and GSP were co-localized and clustered in the 5cM- region on chromosome 1A in three marker intervals: Xwmc496- Xsnp1970, Xsnp1970-Xbarc28 and Xbarc28-Xsnp2005. In previous studies, Xbarc28 was also found to flank QTLs for spike length that explained 10.8% of the phenotypic variation (Marza et al., 2006). Similarly, QTLs for canopy temperature (Shukla et al., 2014), QTLs for pre-harvest sprouting (Munkvold et al., 2009) and QTLs and meta- QTLs for yield components (Zhang et al., 2010) have also been detected in this region. These results indicate the existence of large-effect genes in this interval and thus, high resolution mapping would be recognized to determine if the effects are due to pleiotropy or closely linked QTLs. Five QTLs for SL were identified on chromosomes 5A. Among them, two QTLs with large effects, QSl.cz-5A.1 and QSl.cz-5A.2, overlapped at Xsnp3789. At 10 cM downstream were QSl.cz-5A.2 and QSl.cz-5A.3. QTL QSl.cz-5A.5 was at the distal end of the long arm of 5A. Previous studies have reported vernalization response genes (Vrn genes) and the major wheat domestication gene Q on chromosome 5A (Kato et al., 1998). Vrn genes together with photoperiod response genes (Ppd genes) and earliness per se genes (Eps genes) 118 determine flowering time of wheat and hence, in part, confer wheat wide adaptation to diverse regions around the world (Snape et al., 2001). The Q gene is a well-known domestication locus conferring the free-threshing character and is responsible for many other domestication-related traits such as rachis fragility, glume shape and tenacity, spike length, plant height, and spike emergence time (Faris et al., 2003; Simons et al., 2006; Sormacheva et al., 2014). The five SL QTLs on chromosome 5A were in the same regions where Vrn-A1 and Q are located. Diagnostic markers will be employed to further verify the existence of Vrn-A1 and Q in this DH population. QTL fine mapping is also necessary to determine if one or both of these two genes contributed to SL in this study and if new locus other than Vrn-A1 and Q was detected. Additionally, consistent QTLs for SL (QSl.cz-3B.2 and QSl.cz-3B.3) were identified on chromosome 3B in E1, E2, E4, and E5. QSl.cz-3B.2 and QSl.cz-3B.3 overlapped at locus Xsnp3335 and were located in a region harboring QTLs for FSN, TSN, and GSP. In the same region, Li et al. (2007b) detected QTLs for grain yield and grain number per spike in two environments using a population of recombinant inbred lines derived from two winter wheat cultivars. Wang et al. (2009) also found this region significant for grain filling rate and yield-related traits over multiple environments. Three QTLs on 2D (Table 4.4) were of special interest because these were the only three loci where MDW233 alleles were associated with a longer spike. In a QTL mapping study for spike-related traits, Ma et al. (2007b) detected two QTLs on chromosome 2B flanked by marker Xgwm261 for SL and SC in the cross of winter genotypes Nanda 2419 and Wangshuibai where the QTLs linked to Xgwm261 explained 8.8 to 23.2% of the phenotypic variation. In my study, Xgwm261 was 2.3 119 cM and 13.3 cM away from QSl.cz-2D.1/QFsn.cz-2D.1 and QSl.cz-2D.2, respectively. In addition, Xgwm261 was reported to flank co-localized QTLs and a QTL cluster for yield related traits including plant height, harvest index, days to maturity, thousand grain weight, and grain weight per spike (Mason et al., 2013). These results suggest that these regions on chromosome 2D may be the same. Furthermore, QSl.cz-2D.3 mapped to the long arm of chromosome 2D and shared the same interval with major QTL QFsn.cz-2D.3 and QTsn.cz-2D.4 which coincided with the QTLs for FSN and TSN in Ma et al. (2007). The same position and genetic effects suggested the possibiligy of similar underlying QTLs. A few studies have documented QTLs/genes for SSN, FSN, and TSN (Cui et al., 2012; Ma et al., 2007b). Some previously reported QTLs were confirmed in the present study. A minor QTL, QSsn.cz-6D, is consistent with the QTL detected by Cui et al. (2012) who also located a cluster of QTLs for spike characteristics on chromosome 2B corresponding with the major QTL clusters identified in the present study. QTLs in this cluster were repeatedly detected in almost all environments evaluated. At these loci, SS8641 contributed positive additive effects for SSN, TSN, and SC, whereas MDW233 was associated with positive GSP suggesting that the SS8641 allele of this cluster may lower spikelet fertility and increase TSN and SC by increasing the number of sterile spikelets. The SS8641 allele of this region should be avoided in breeding programs. In addition, a QTL cluster for FSN and TSN was identified on chromosome 5D flanked by Xsnp4156-Xgdm136 and was located in the same region of previously reported QTLs detected by Li et al. (2007b) and Cui et al. 120 (2012). Cuthbert et al. (2008) reported a QTL cluster for grain numbers per spike, grain yield, thousand grain weight, grain filling time, and days to heading on chromosome 2D which may correspond to the region of major QTL QSsn.cz-2D.3 identified in this study (Table 4.4). At the distal end of chromosome 2D, I detected three closely linked QTLs QFsn.cz-2D.4, QTsn.cz-2D.5, and QGsp.cz-2D. These QTLs were not located at the region of the compactum (C) locus, a spike-compacting gene on the long arm of chromosome 2D (Johnson et al., 2007a). The SS8641 alleles in this region decreased FSN and TSN but increased GSP. The association of this region with spike traits has not been reported elsewhere. Furthermore, I found that the major QTL QFsn.cz-2D.2 shared the interval with QTsn.cz-2D.2 and QSsn.cz-2D.1 and overlapped with QSn.cz-2D.2 and QTsn.cz-2D.3 at the locus Ppd-D1. The effects of these QTLs were possibly caused by the locus Ppd-D1 which is a member of the Ppd1 genes known to confer photoperiod sensitivity and influence agronomic traits such as plant height, days to heading and thousand grain weight (Guo et al., 2010). Recently, the Ppd-D1 locus was shown to control photoperiod-dependent floral induction and that it has a major inhibitory effect on paired spikelet formation by regulating the expression of the FLOWERING LOCUS T (FT) (Boden et al., 2015). The QTL cluster on chromosome 5A for SC included the locus Xgwm304 that is neither close to the Q gene nor the Vrn-A1 gene but it has been related to grain yield and thousand grain weight by Cuthbert et al. (2008) and SL and SC by Ma et al. (2007b). In these two studies, this region was identified as harboring major QTLs because of high PVE values similar to my results. Thus, it is possible that this region 121 may contribute to grain yield by increasing spikelet numbers and grain weight. Sourdille et al. (2003) used a DH population derived from the cross Courtot × Chinese Spring to study wheat development traits and detected one QTL on the long arm of chromosome 5D for SC. This QTL explained 13.6% of the phenotypic variation and was similar to the genomic region Xsnp876-Xsnp4157 where two major QTLs for SL and SC were identified in the present study. Another QTL cluster comprising of four major QTLs for GSP on chromosome 5B (Table 4.4) coincided with the interval of the SL QTL QSl.ccsu-5B.2 identified by Kumar et al. (2007). Chromosome 3A of wheat is known to contain QTLs for grain yield and other important agronomic traits. Using a recombinant inbred line population derived from the winter wheat cultivar Cheyenne (CNN) and its single chromosome substitution line CNN (WI3A) where chromosome 3A of CNN was substituted for Wichita (WI) chromosome 3A, Mengistu et al. (2012) and Dilbirligi et al. (2006) detected QTLs for grain yield, plant height, spikes per square meter, and grain number per spike and found that most of the detected QTLs on 3A were co-localized in two regions. In the present study, I detected five QTLs on chromosome 3A for SC and SSN among which QSsn.cz-3A.1 explained 13.8% of the phenotypic variation while the rest were minor QTLs. Based on the mapping positions of SSR markers used in the current and previous studies (Somers et al., 2004), these five QTLs were similar to the QTLs previously identified by Dilbirligi et al. (2006) and Mengistu et al. (2012). 122 Genetic complexity of spike characteristics Most important agronomic traits are quantitative in nature controlled by polygenes and influenced by the environment. Understanding the genetic and environmental factors causing the phenotypic variation of quantitative traits is essential for the genetic improvement of crops via knowledge-based breeding (Mackay, 2001; Würschum, 2012). In the present study, the effects of major, minor, and epistatic QTLs as well as their interactions with the environment and their relative contributions to spike characteristics were estimated (Figure 4.2). The QTLs with additive effects were the largest in total number and had the largest genetic contribution to phenotypic variation. This agreed with previous QTL studies involving epistasis, Q×E and QQ×E interactions (Kuchel et al., 2007a; Wu et al., 2012; Xing et al., 2002; Zhang et al., 2014). In addition, QTLs for spike characteristics were not evenly distributed within and across chromosomes and tended to cluster (Figure 4.1). I identified QTL clusters on chromosome 1A, 5A, 2B, 3B, 5B, 1D, 2D, and 5D where QTLs for multiple spike characteristics were co- localized or closely linked within a 10-cM region. In most cases, each cluster contained at least one major QTL. The clustering of QTLs also partially explained the correlation between spike characteristics. In this study, SL was highly correlated with FSN across environments (Table 4.3). This could be caused by the co-localization of QSl.cz-1A and QFsn.cz-1A plus the effects of closely linked QTLs QSl.cz-3A.1, QSl.cz-3A.2 and QFsn.cz-3A.1. Despite of the slight difference in interpretation, characterizing the interaction at two or more loci or epistasis is as important in quantitative genetics as in classical genetics. I found that interactions (Q×E, Q×Q and 123 QQ×E) served as modifiers for spike characteristics determination in my DH population. For example, the interval Xsnp4167-Xsnp3760 on chromosome 5A was not detected with significant additive effects but contributed to SL through its interactions with Xsnp2795-Xsnp708, Xwmc496-Xsnp1970, and Xsnp4178-Xgdm136, which were associated with significant additive effects for FSN, GSP, and SL, respectively. Significant epistasis was also detected between non-significant intervals such as Xsnp2067-Xsnp2127 and Xsnp2668-Xsnp2696 which increased FSN and accounted for 4.9% of the phenotypic variation. Similar results were reported by Ma et al. (2007b) where the interaction of two non-significant loci on chromosome 3D decreased TSN and FSN. These results confirmed that loci without main effects may contribute to trait determination through epistasis (Li et al., 2001). Additionally, I found that the SS8641 allele at the interval Xsnp1970-Xbarc28 increased FSN and TSN in E5 and these effects was enhanced by 21.5% through the Q×E interaction. Although the effects and contribution from Q×E, Q×Q and QQ×E interactions were relatively smaller compared to additive main effects, they were important terms fine- tuning the expression of spike traits. This is valuable information for pyramiding QTLs in breeding programs. Conclusion Spike characteristics determine the number of grains produced on each spike. Genetically improving grain number per spike is widely accepted as one of the key paths towards higher grain yield. In this study, QTL mapping in a bi-parental population was performed and detected a total of 109 QTLs among which consistent QTLs such as QSl.cz-1A or QFsn.cz-1A for SL and FSN, QGsp.cz-2B.1 for GSP, and 124 QSc.cz-5A.3 for SC, explained up to 30.9%, 15.6%, and 80.2% of the phenotypic variation, respectively. I also found that the average contribution of QFsn.cz-1A to FSN at each trial was enhanced by 19% via interaction with the interval Xsnp4178- Xgdm136. In addition, QTLs clusters on chromosomes 1A, 5A, 2B, 3B, 5B, 1D, and 5D with synergistic or antagonistic genetic effects partially explained the phenotypic correlation between spike traits. These results provide valuable information for manipulating spike morphology for breeding purposes. 125 Chapter 5: Multivariate analysis of grain yield and yield related traits in a doubled haploid population of soft red winter wheat Abstract To study the interrelationships among grain yield and yield contributing traits, a series of statistical analyses including correlation, multiple linear regression, cluster analysis, principal component analysis and structural equation modeling were conducted in a soft red winter wheat doubled haploid population derived from the cross MD01W233-06-1 by SS8641. Six structural equation models with feedback loops were constructed and showed that spikes per square meter had the highest positive contribution to grain yield followed by grain weight per spike and that grains per spikelet and fertile spikelet number per spike were compensatory targets that mediate yield component compensation. In addition, DH84 and DH96 which yielded 24.13% and 22.64% higher than the mean performance of the whole population, respectively, may have potential as new cultivars. Introduction Wheat (Triticum aestivum L.) is one of the most important food crops, occupying 17% of the world’s crop acreage, feeding about 40% of the world’s population and providing 20% of total food calories and protein in human nutrition (Gupta et al., 2005). Continuous genetic improvement of wheat yield potential via breeding is essential to securing a stable food supply. In a wheat breeding program, a breeder usually records a number of agronomic characters on which statistical analyses are made to get a better understanding of the germplasm. The information is then utilized to make selections. Therefore, analytical methods that can extract the most 126 information from large datasets and provide insights into the nature and magnitude of association of plant traits are needed, especially when clear experimental control of the inter-correlated traits is difficult. Several statistical methods have been utilized to investigate wheat grain yield and its related characters. For example, phenotypic correlation analysis is an important way to evaluate the association between plant characters. However, simple correlation does not necessarily imply a cause-and-effect relationship. The observed correlation could be due to unknown environmental or genetic factors. Genetic correlation is a measure of the extent to which plant characters are associated at the genetic level (Waitt and Levin, 1998) and is used as a supplement to phenotypic correlation when making selection decisions (Holland, 2006). Alternatively, multiple regression analysis can be useful when the main interest is the prediction of the response variable from a set of predictor variables or to select candidate variables for further analyses. Using multiple linear regression analysis, Leilah and Al-Khateeb (2005) reported that grain weight per spike, harvest index, biological yield, spike number per square meter and spike length were major contributors to wheat grain yield. Additionally, cluster and principal component analyses are often used separately or combined to group cultivars or agronomic variables into main groups or subgroups based on similarity, which is also useful for parental selection in breeding programs and crop modeling (Khodadadi et al., 2011; Leilah and Al-Khateeb, 2005). Furthermore, path analysis divides the correlation coefficients into direct and indirect effects and has been employed to study yield formation in cereal crops by separating 127 the direct influence of each yield component on grain yield from the indirect effects caused by mutual relationships among yield components themselves (Kashif and Khaliq, 2003; Li et al., 2006; Moral et al., 2003). Most path analysis studies on yield formation, however, have two main general limitations: 1) researchers assume bidirectional causal pathways between yield components and yield related traits; and 2) grain yield is modeled as a resultant variable and all other traits as causal variables with direct path toward grain yield (Kashif and Khaliq, 2003; Li et al., 2006). Additionally, whether a yield component can influence others that develop earlier is questionable given that yield components develop sequentially (Dofing and Knight, 1992). Furthermore, path analysis assumes that all variables are measured without error and that no correlation between the error terms and causal loops exist (Meehl and Waller, 2002). Structural equation modeling (SEM) is a powerful multivariate approach to model complex relationships between latent and measured variables while accounting for measurement error (Ullman, 2006). SEM is an extension of general linear modeling (GLM) procedures, such as the ANOVA and multiple regression analysis. Its main goal is to determine if a specified theory about the causal pattern of multiple inter- correlated variables, usually represented by a path diagram, is consistent with empirical data. This consistency is evaluated through data-model fit indices that measure the extent to which the proposed network of relations is plausible. Four most commonly used fit indices are 1) standardized root mean squared residual (SRMR), 2) root mean squared error of approximation (RMSEA), 3) normed fit index (NFI), 128 and 4) nonnormed fit index (NNFI) (Hooper et al., 2008). Similar to classic path analysis, SEM is capable of conveying casual relationships among mutually inter- correlated dependent and independent variables (Kline, 2011). One of the primary advantages of SEM (vs. other applications of GLM such as ANOVA and path analysis) is that less restrictive assumptions exist in SEM which makes SEM a popular confirmatory and exploratory approach in social sciences (Marsh et al., 2014). SEM has been adapted to the quantitative genetics mixed-effects models settings by Gianola and Sorensen (2004) and promoted by Lamb et al. (2011) in plant sciences to study yield components, complex multi-site field trails etc. However, no applications have been reported in major crop plants. The present study was undertaken to investigate and model the phenotype network regarding wheat grain yield formation through multivariate analyses. The novelty of this research is twofold: 1) it provides an overall view on grain yield formation by including yield components, spike morphology and plant architecture traits and 2) it introduces SEM as a supplement to traditional multivariate approaches to resolve the interrelationships among yield contributing traits. Data used in this study was collected at the end of growing seasons and, thus, phenotype network constructed in this study did not represent dynamic regulating network or mimic any developmental processes. 129 Materials and Methods Field trials and data collection Data used in this chapter was collected from a doubled haploid population of soft red winter evaluated in five field environments (refer to the chapter 2, 3, and 4 of this dissertation for details). Statistical analyses Phenotypic correlation analysis was performed by PROC CORR procedure of SAS, Version 9.3 (SAS Institute, Cary, NC 2013). Genetic correlation coefficients were estimated using MANOVA method (Liu et al., 1997) by PROC GLM procedure of SAS. Multiple linear regression and stepwise multiple linear regression was conducted using PROC REG procedure of SAS. Cluster analysis (using standardized data and Ward method) and principal component analysis (using correlation matrix and REML method) were performed by JMP® Pro, Version 11 (SAS Institute, Cary, NC, 2014). Structural equation modeling was based on correlation matrix and performed using LISREL, Version 9.1 (Joreskog and Sorbom, 2012). Results and Discussion Phenotypic and genetic correlation analyses According to quantitative genetics theory, genetic and environmental causes of correlation combine together to produce phenotypic correlations. The magnitude and sign of phenotypic and genetic correlations, however, are not necessarily related (Waitt and Levin, 1998). It is important to know for breeders if the phenotypic correlation is due to heritable genetic factors or external environmental conditions. In 130 this study, a matrix of pairwise phenotypic and genetic correlation coefficients were computed and are presented in Table 5.1. GYLD was positively associated with SPSM and TGW but was negatively correlated with FLL, FLW, FLA, SL, FSN, TSN, and HD. SPSM and TGW had the highest positive phenotypic and genotypic association with GYLD implying that improving these traits could result in higher grain yield and this effect would be highly heritable. A significant positive correlation between GWPS and GYLD was not found in pooled correlation analysis but was detected in two environments: E4 and E5, which was similar with the results reported by Marza et al. (2006) and Heidari et al. (2011). The negative correlations between GYLD and FLL, FLW, FLA, SL, FSN, TSN, and HD suggested that early heading genotypes with smaller flag leaves, shorter spikes, and less fertile spikelets, and thus with lower grain number and lighter grain weight would contribute to higher grain yield. This was true in E1, E2, E3, and E4 where these unfavorable traits were compensated by higher SPSM but not in E5 where the compensation from SPSM was not enough, probably due to higher temperatures during the growing season. In a study to evaluate wheat yield formation under Mediterranean conditions, Moral et al. (2003) reported that durum wheat yielded less in warmer environments than in cooler regions mainly due to reduced SPSM and TGW. Similarly, Hou et al. (2012) found that winter wheat grew faster and produced more tillers but tended to decrease SPSM under warmer conditions also resulting in lower grain yield. Cluster and principal component analysis Cluster analysis has been used to classify wheat ecotypes and to evaluate genetic diversity in wheat germplasm collections. Cluster analysis groups genotypes into 131 clusters where genotypes in the same cluster exhibit high homogeneity but have high heterogeneity among clusters. In this study, I clustered the 124 DH lines into five clusters (Figure 5.21~5.26). Membership of each line is presented in Appendix D. The means of dendrogram clusters at each environment are presented in Table 5.6. The cluster with the highest grain yield at each environment was consistently associated with higher SPSM, smaller FLA, less GPS, lighter GWPS, shorter SL, fewer FSN and TSN, which also agreed with the results from phenotypic and genetic correlation analysis of this study. Principal component analysis (PCA) is a standard multivariate technique for complex dataset analysis where observations are described by multiple inter-correlated variables. Its objective is to extract the most important information from the original inter-correlated variables by maximizing the variance of a set of new orthogonal variables called principal components, and to display the pattern of similarity of the observations and of the variables in maps (Abdi and Williams, 2010). Principal components are linear combinations of original variables. The first principal component has the maximal variance. The second principal component has maximal variance in a direction orthogonal to the first principal component, and so on. In this study, PCA grouped the investigated wheat variables into five main components explaining more than 80% of the total variation (Table 5.4). Specifically, the first two principal components explained more than half of the total variance. The first principal component accounted for 30.6%, 27.1%, 32.5%, 30.7%, 34.5%, and 33.1% of the total variation and second principal component for 20.2%, 18.1%, 22%, 18.7%, 132 and 23.7% of the total variation at E1, E2, E3, E4, and E5 trials, respectively. The first principal component was related to yield components and yield contributing traits whereas the second principal component was related to vegetative growth and spikelet fertility across trials (Table 5.5). The traits with largest loadings to the first principal component were GWPS, GPS, SPSM, SL and FSN, suggesting these were indicative of yield potential. The first two principal components and wheat variables were plotted in biplots (Figure 5.1). From the biplots, vectors representing uncorrelated traits formed right angles (90°) (e.g. GPS vs. HD, SSN vs. FSN), whereas highly correlated traits formed either acute (positive correlation; e.g. SPSM vs. GYLD) or obtuse (negative correlation; e.g. GYLD vs. FLA) angles. In general, three observations were made from the biplots: 1) SPSM and TGW were mostly positively associated with GYLD, 2) SSN, SC, GSP, and FLS were independent of GYLD, 3) HD, FLW, FSN, FLA, FLL, and GPS were negatively associated with GYLD. GWPS showed a slightly positive to no correlation with GYLD, which agreed with the results of previous phenotypic correlation analyses. Additionally, cluster analysis coupled with PCA was used to select high yielding DH lines in this study. The first two principal components from each environment were plotted with DH cluster membership as labels (Figure 5.21~5.26). At E1, the highest yielding cluster (Cluster 1) was separated from the lowest yielding cluster (Cluster 4) as was Cluster 2 from Cluster 4 at E2, Cluster 5 from Cluster 3 at E3, Cluster 1 from Cluster 4 at E4, and Cluster 1 from Cluster 4 at E5. The extracted principal components were able to distinguish different clusters and, thus, largely confirmed 133 the generated cluster membership. Two DH lines, DH96 and DH84, stayed in the highest yielding clusters across all five environments. Furthermore, when data from the five environments were averaged, DH96 and DH84 ranked second and third among all lines, increasing grain yield by 24.13% and 22.64% respectively. Thus, DH96 and DH84 could be candidates to be new cultivars with a stable performance across these environments. Multiple linear regression analysis Regression coefficients and the associated probability values for each variable in predicting wheat grain yield are presented in Table 5.2 and 5.3. The final models from stepwise linear regression analyses explained more than 95% of the total variation in grain yield. Although the variables remaining in the models varied at different environments, GWPS and SPSM were shared by all, suggesting the importance of SPSM and GWPS as selection criteria in wheat breeding for grain yield. Similarly, Leilah and Al-Khateeb (2005) also observed that SPSM and GWPS were the most effective variables influencing wheat grain yield. Structure equation modeling (SEM) Phenotypic traits can have causal effects on each other (Rosa et al., 2011). Information regarding phenotype networks describing the cause-and-effect relationships and feedback between traits is very helpful to predict the performance of biological systems. In this study, a phenotype network regarding grain yield and yield contributing traits was modeled under the frame of SEM. The purpose was to quantify the relative contributions of correlated causal sources of variation once a certain 134 network of interrelated variables with biological significance has been accepted (Shipley, 2004). Initial models were constructed separately for grain yield and yield components and spike characteristics based on the results obtained from previous multivariate analyses and published results on the interrelationships among grain yield and yield contributing traits (Dofing and Knight, 1992; Moral et al., 2003). The initial models were then integrated into one. I included paths from GPS and TGW to GYLD at my first attempt to integrate initial models. The path coefficients were not significant and overall model fitting failed although this seemed meaningful biologically. LISREL suggested a list of paths that could improve fit indices. Based this list, the modification of paths was performed to obtain the best combination of four fit indices. Final models are shown in Figure 5.3. All the path coefficients in the phenotype network were highly significantly different from zero (Figure 5.3). Across six models, GWPS and SPSM had direct causal influence on GYLD. The loadings for the path from SPSM to GYLD were higher than that for the path from GWPS suggesting that SPSM had a relatively more direct contribution to GYLD. No direct contribution from GPS or TGW to GYLD was established. However, GPS and TGW had an indirect effect on GYLD via GWPS. Additionally, FSN and GSP were feedback targets where depressing effect from GWPS, SPSM, TGW and GYLD were observed. GSP had more feedback effect than FSN. Previous studies found that SPSM had a direct negative effect on GPS and TGW (Moral et al., 2003) and that this compensation arose from the fact that these traits develop sequentially with later-developing traits under control of earlier- 135 developing ones (Slafer, 2007). However, in this study, a direct negative effect on GWPS from SPSM was significant only in Clarksville 2014 and Queenstown 2014. Although a direct path from SPSM to GWPS was absent in the other three trials, SPSM negatively affected GWPS by depressing GSP and FSN and hence GPS (Figure 5.3) suggesting GSP and FSN as mediators in yield component compensation. To my knowledge, this is the first report of GSP and FSN as direct feedback regulating targets in wheat. Direct genetic evidence of feedback paths in SEM The results of the QTL analyses of the set of agronomic traits involved in the present study (Chapter 2, 3, and 5 of this dissertation) were used to evaluate the validity of feedback paths in the structural equation models at each trial. The feedback path from SPSM to GSP in the model at Clarksville 2013 and Queenstown 2013 could be partially explained by the interval Xwmc496-Xsnp1970 where QTL QSsm.cz-1A.1 and QTL QGsp.cz-1A.1 co-localized and the interval Xbar28-Xsnp2005 where QTL QSsm.cz-1A.3 and QTL QGsp.cz-1A.2 also co-localized. The MDW233 allele at these two loci increased SPSM but decreased GSP. The feedback path from GYLD to GSP in the model at Clarksville 2014 could be associated with the region Xsnp3382- Xsnp3368 on chromosome 3B where QTLs with opposite genetic effects on GYLD and GSP were located closely. The feedback path from GYLD to FSN in the model at Clarksville 2013 could be supported by the genomic region Xwmc496-Xbar28 on chromosome 1A where QTL QGld.cz-1A and QTL QFsn.cz-1A co-localized but showed opposite genetic effects on GYLD and FSN. Another genetic evidence might be the interval Xsnp2862-XPpd1 on chromosome 2D where its SS8641 allele 136 increased grain yield but decreased FSN. Additionally, the interval Xsnp1970-Xbar28 on chromosome 1A and Xsnp3389-Xsnp3344 on chromosome 3B may be the underlying genetic factors for the feedback path from SPSM to FSN in the model at Kinston 2014, where the MDW233 allele increased SPSM but decreased FSN. No QTLs were found to directly support the feedback paths from GWPS to GSP and FSN and from TGW to FSN. This could be due to two reasons: 1) the threshold level set to detect a significant QTL was too high so that QTLs with minor effects were overlooked. A consequence of this is that researchers would miss QTLs that could explain the feedback paths and 2) the causal relations could be due to methylation quantitative trait loci (meQTLs) in addition to DNA sequence changes (Koch, 2014) which are not detected in conventional QTL analyses. Conclusion Multivariate analyses were used to construct a phenotype network involving grain yield and yield related traits. Results showed that SPSM (spikes per square meter) was the most important trait that directly and positively contributed to grain yield followed by GWPS (grain weight per spike). In addition, GPS (grains per spike) had more weight on GWPS (grain weight per spike) than TGW (thousand grain weight) and GSP (grains per spikelet) had more weight on GPS (grains per spike) than FSN (fertile spikelet number). Therefore, high SPSM and GSP and moderate TGW and FSN could be the targets for breeding for higher grain yield in the Mid-Atlantic region. 137 Table 5.1 Genotypic (rg) and phenotypic (rp) correlation coefficients among the grain yield and yield contributing traits in the MD01W233-06-1 × SS8641 doubled haploid population. rp is shown in the upper triangular and rg in the lower triangular. Traits evaluated include grain yield (GYLD), grains per spike (GPS), grain weight per spike (GWPS), spikes per square meter (SPSM), and thousand-grain-weight (TGW), plant height (PHT), flag leaf length (FLL), flag leaf width (FLW), flag leaf area (FLA), flag leaf shape (FLS), spike length (SL), sterile spikelet number per spike (SSN), fertile spikelet number per spike (FSN), total spikelet number per spike (TSN), spike compactness (SC), grain number per spikelet (GSP), and dates to heading (HD). rg and rp were estimated from all five trials’ data. Significance was not tested for rg. GYLD GPS GWPS SPSM TGW PHT FLL FLW FLA FLS SL SSN FSN TSN SC GSP HD GYLD -0.17 0.12 0.70*** 0.37*** 0.13 -0.25** -0.43*** -0.41*** 0.09 -0.24** -0.14 -0.40*** -0.44*** -0.15 0.11 -0.46*** GPS -0.15 0.66*** -0.58*** -0.38*** 0.06 0.45*** 0.19* 0.40*** 0.26** 0.49*** -0.36*** 0.71*** 0.51*** -0.06 0.76*** 0.03 GWPS 0.14 0.66 -0.60*** 0.36*** 0.29*** 0.36*** 0.16 0.33*** 0.21* 0.56*** -0.25** 0.41*** 0.28** -0.36*** 0.56*** -0.24*** SPSM 0.69 -0.59 -0.63 0.02 -0.11 -0.45*** -0.45*** -0.56*** -0.08 -0.56*** 0.07 -0.58*** -0.52*** 0.13 -0.30*** -0.19* TGW 0.36 -0.37 0.38 -0.01 0.36*** -0.11 0.02 -0.05 -0.12 0.08 0.21* -0.35*** -0.24*** -0.34*** -0.24** -0.29** PHT 0.08 0.08 0.31 -0.17 0.33 0.24** -0.21* 0.04 0.37*** 0.12 0.18* -0.01 0.07 -0.07 0.07 0.01 FLL -0.36 0.47 0.40 -0.57 -0.10 0.27 0.31*** 0.82*** 0.66*** 0.42** 0.19* 0.45*** 0.50*** 0.02 0.22* 0.36*** FLW -0.59 0.17 0.20 -0.59 0.08 -0.23 0.30 0.79*** -0.51*** 0.30*** 0.18* 0.39*** 0.45*** 0.09 -0.09 0.54*** FLA -0.57 0.40 0.38 -0.72 -0.01 0.05 0.82 0.80 0.12 0.45*** 0.23* 0.52** 0.59** 0.06 0.09 0.55*** FLS 0.12 0.28 0.20 -0.07 -0.15 0.41 0.65 -0.52 0.10 0.16 0.01 0.11 0.10 -0.06 0.27** -0.11 SL -0.30 0.47 0.56 -0.62 0.12 0.13 0.45 0.31 0.48 0.17 -0.03 0.65*** 0.60*** -0.58*** 0.10 0.08 SSN -0.26 -0.38 -0.27 -0.02 0.23 0.20 0.21 0.17 0.23 0.04 -0.02 -0.09 0.34*** 0.38*** -0.47*** 0.51*** FSN -0.42 0.69 0.42 -0.61 -0.31 -0.03 0.47 0.44 0.57 0.09 0.64 -0.12 0.90*** 0.15 0.09 0.35*** TSN -0.51 0.49 0.28 -0.59 -0.20 0.06 0.54 0.49 0.64 0.10 0.61 0.32 0.90 0.30*** -0.11 0.55*** SC -0.13 -0.08 -0.39 0.16 -0.34 -0.09 -0.01 0.10 0.05 -0.09 -0.62 0.33 0.10 0.24 -0.22* 0.46*** GSP 0.15 0.78 0.55 -0.29 -0.26 0.11 0.22 -0.15 0.06 0.31 0.07 -0.47 0.09 -0.12 -0.20 -0.28** HD -0.58 0.02 -0.25 -0.27 -0.29 -0.01 0.41 0.61 0.63 -0.12 0.12 0.47 0.39 0.58 0.41 -0.31 * Significant at 0.05 probability level ** Significant at 0.01 probability level *** Significant at 0.001 probability level 138 Table 5.2 Multiple linear regression of the MD01W233-06-1 × SS8641 doubled haploid population. Grain yield (GYLD) as dependent variable and grains per spike (GPS), grain weight per spike (GWPS), spikes per square meter (SPSM), and thousand-grain-weight (TGW), plant height (PHT), flag leaf length (FLL), flag leaf width (FLW), flag leaf area (FLA), flag leaf shape (FLS), spike length (SL), sterile spikelet number per spike (SSN), fertile spikelet number per spike (FSN), spike compactness (SC), grain number per spikelet (GSP), and heading date (HD) as independent variables. Total spikelet number per spike (TSN) was not included in the analysis because of its multicollinearity with SSN and FSN. Estimates of regression coefficients and the associated p values are shown. † PHT, FLL, FLW, FLA, FLS were not evaluated in Kinston 2014. Clarksville 2013 Clarksville 2014 Queenstown 2013 Queenstown 2014 Kinston 2014 † Overall Variable Estimate P value Estimate P value Estimate P value Estimate P value Estimate P value Estimate P value Intercept -1233.71 0.0213 -520.30 0.2398 221.20 0.5448 -994.20 <.0001 -232.08 0.3173 -188.71 0.6140 GPS -13.60 0.0472 -10.51 0.0213 1.28 0.5790 -13.96 <.0001 -2.15 0.5470 -9.94 0.0340 GWPS 412.69 <.0001 767.35 <.0001 484.42 <.0001 483.40 <.0001 446.89 <.0001 517.05 <.0001 SPSM 1.19 <.0001 0.93 <.0001 1.29 <.0001 1.05 <.0001 1.10 <.0001 1.08 <.0001 TGW 0.97 0.5116 1.20 0.3731 -0.56 0.6868 2.01 0.0092 0.64 0.4663 1.49 0.3043 PHT 0.83 0.0184 0.05 0.8966 0.05 0.8722 0.46 0.0145 -- -- -83.71 0.0132 FLL 23.39 0.2645 8.59 0.6147 5.08 0.7402 20.00 0.0318 -- -- 32.65 0.0294 FLW 156.64 0.2374 117.05 0.3538 -68.27 0.6104 130.07 0.0648 -- -- 58.11 0.0051 FLA -16.81 0.2006 -8.48 0.3928 0.74 0.9313 -13.88 0.0077 -- -- -234.26 0.0212 FLS -2.62 0.8495 1.55 0.9075 -10.10 0.4558 -4.00 0.6037 -- -- -0.16 0.5415 SL -45.07 0.2789 -100.51 0.0319 -87.00 0.0218 -52.81 0.0168 -50.26 0.0380 12.11 0.3927 SSN 16.84 0.3571 39.68 0.0487 25.74 0.1169 17.15 0.1000 16.35 0.1014 -116.24 0.3442 FSN 55.92 0.0164 66.72 0.0030 33.69 0.0585 62.31 <.0001 24.84 0.0949 -0.33 0.9676 SC -149.28 0.2291 -290.59 0.0411 -240.23 0.0370 -158.32 0.0166 -141.71 0.0524 -14.75 0.2511 GSP 208.35 0.0403 151.10 0.0367 -24.48 0.4378 202.47 <.0001 27.72 0.6457 144.65 0.0406 HD 1.25 0.3804 -0.32 0.8399 -0.59 0.4884 -0.10 0.8936 0.25 0.5540 -0.22 0.8005 R sq 0.9669 0.9620 0.9838 0.9848 0.9827 0.9722 R sq (adj) 0.9623 0.9568 0.9815 0.9827 0.9812 0.9683 139 Table 5.3 Stepwise multiple linear regression of the MD01W233-06-1 × SS8641 doubled haploid population. Grain yield (GYLD) as dependent variable and grains per spike (GPS), grain weight per spike (GWPS), spikes per square meter (SPSM), and thousand-grain-weight (TGW), plant height (PHT), flag leaf length (FLL), flag leaf width (FLW), flag leaf area (FLA), flag leaf shape (FLS), spike length (SL), sterile spikelet number per spike (SSN), fertile spikelet number per spike (FSN), spikelet compactness (SC), grain number per spikelet (GSP), and heading date (HD) as independent variables. Total spikelet number per spike (TSN) was not included in the analysis because of its multicollinearity with SSN and FSN. Variables kept in the final model, their regression coefficients, and the associated p values are shown. Clarksville 2013 Clarksville 2014 Queenstown 2013 Queenstown 2014 Kinston 2014 † Overall Variable Estimate P value Estimate P value Estimate P value Estimate P value Estimate P value Estimate P value Intercept -613.72 <.0001 -732.93 <.0001 -662.59 <.0001 -599.60 <.0001 -497.80 <.0001 -518.98 <.0001 GPS GWPS 436.68 <.0001 769.62 <.0001 475.04 <.0001 493.77 <.0001 430.65 <.0001 520.18 <.0001 SPSM 1.19 <.0001 0.93 <.0001 1.30 <.0001 1.08 <.0001 1.10 <.0002 1.10 <.0001 TGW 1.18 0.0171 0.85 0.0436 PHT 0.95 0.0031 0.49 0.0131 FLL FLW FLA FLS SL SSN -9.71 0.0083 -7.82 0.0029 FSN -4.62 0.0019 SC 25.42 0.0447 GSP HD R sq 0.9607 0.9545 0.9825 0.9783 0.9816 0.9676 R sq (adj) 0.9598 0.9537 0.9819 0.9774 0.9812 0.9668 † PHT, FLL, FLW, FLA, FLS were not evaluated in Kinston 2014. 140 Table 5.4 Principal component analysis of the MD01W233-06-1 × SS8641 doubled haploid population based on sixteen agronomic traits including grain yield (GYLD), grains per spike (GPS), grain weight per spike (GWPS), spikes per square meter (SPSM), and thousand-grain-weight (TGW), plant height (PHT), flag leaf length (FLL), flag leaf width (FLW), flag leaf area (FLA), flag leaf shape (FLS), spike length (SL), sterile spikelet number per spike (SSN), fertile spikelet number per spike (FSN), total spikelet number per spike (TSN), spikelet compactness (SC), and grain number per spikelet (GSP). Eigen values for each extracted principle component (PC), percentage (Per.) explained by each PC and cumulative percentage (Cum. Per.) are shown. Clarksville 2013 Clarksville 2014 Queenstown 2013 Queenstown 2014 Kinston 2014† Overall Eigen value Per. Cum. Per. Eigen value Per. Cum. Per. Eigen value Per. Cum. Per. Eigen value Per. Cum. Per. Eigen value Per. Cum. Per. Eigen value Per. Cum. Per. PC1 5.21 30.62 30.62 4.61 27.13 27.13 5.52 32.49 32.49 5.22 30.68 30.68 4.14 34.53 34.53 5.62 33.07 33.07 PC2 3.44 20.22 50.84 3.08 18.13 45.26 3.74 21.98 54.46 3.18 18.71 49.40 2.85 23.71 58.24 3.35 19.71 52.78 PC3 2.06 12.13 62.97 2.30 13.55 58.80 1.83 10.74 65.20 2.19 12.88 62.28 1.87 15.57 73.80 2.02 11.87 64.65 PC4 1.70 9.99 72.96 1.98 11.64 70.44 1.55 9.12 74.32 1.73 10.19 72.46 1.36 11.30 85.11 1.85 10.91 75.56 PC5 1.30 7.67 80.62 1.41 8.29 78.73 1.20 7.08 81.40 1.23 7.24 79.70 0.84 7.03 92.14 1.08 6.34 81.89 PC6 0.99 5.81 86.43 1.06 6.24 84.97 1.02 6.02 87.42 1.15 6.77 86.47 0.54 4.53 96.67 0.98 5.77 87.67 PC7 0.88 5.16 91.60 0.95 5.59 90.56 0.92 5.41 92.83 0.87 5.13 91.61 0.32 2.68 99.35 0.86 5.06 92.72 PC8 0.52 3.07 94.67 0.78 4.57 95.12 0.48 2.83 95.67 0.62 3.67 95.27 0.07 0.56 99.90 0.52 3.05 95.77 PC9 0.44 2.61 97.27 0.41 2.40 97.52 0.35 2.06 97.73 0.42 2.50 97.77 0.01 0.06 99.96 0.44 2.56 98.33 PC10 0.34 1.98 99.26 0.27 1.57 99.09 0.28 1.62 99.35 0.28 1.63 99.40 0.00 0.03 99.99 0.21 1.21 99.54 PC11 0.10 0.59 99.84 0.13 0.75 99.84 0.08 0.49 99.83 0.08 0.50 99.90 0.00 0.01 100.00 0.06 0.35 99.89 PC12 0.01 0.08 99.92 0.01 0.07 99.91 0.01 0.07 99.91 0.01 0.04 99.94 0.00 0.00 100.00 0.01 0.06 99.94 PC13 0.01 0.05 99.97 0.01 0.04 99.94 0.01 0.04 99.95 0.01 0.03 99.97 0.00 0.02 99.96 PC14 0.00 0.02 99.98 0.01 0.03 99.98 0.01 0.03 99.98 0.00 0.01 99.98 0.00 0.02 99.98 PC15 0.00 0.01 99.99 0.00 0.01 99.99 0.00 0.02 99.99 0.00 0.01 99.99 0.00 0.01 99.99 PC16 0.00 0.01 100.00 0.00 0.01 100.00 0.00 0.01 100.00 0.00 0.01 100.00 0.00 0.01 100.00 PC17 0.00 0.00 100.00 0.00 0.00 100.00 0.00 0.00 100.00 0.00 0.00 100.00 † PHT, FLL, FLW, FLA, FLS were not evaluated in Kinston 2014. 141 Table 5.5 Principle component analysis of the MD01W233-06-1 × SS8641 doubled haploid population based on sixteen agronomic traits including grain yield (GYLD), grains per spike (GPS), grain weight per spike (GWPS), spikes per square meter (SPSM), and thousand-grain-weight (TGW), plant height (PHT), flag leaf length (FLL), flag leaf width (FLW), flag leaf area (FLA), flag leaf shape (FLS), spike length (SL), sterile spikelet number per spike (SSN), fertile spikelet number per spike (FSN), total spikelet number per spike (TSN), spike compactness (SC), grain number per spikelet (GSP), and heading date (HD). Eigenvectors of the first two principle components (PC) are shown. Clarksville 2013 Clarksville 2014 Queenstown 2013 Queenstown 2014 Kinston 2014 † Overall PC1 PC2 PC1 PC2 PC1 PC2 PC1 PC2 PC1 PC2 PC1 PC2 GYLD -0.197 -0.293 -0.086 -0.042 -0.241 -0.196 -0.045 -0.303 -0.187 -0.152 -0.230 0.227 GPS 0.374 -0.118 0.349 -0.262 0.298 -0.302 0.343 -0.227 0.439 -0.149 0.311 0.255 GWPS 0.315 -0.254 0.302 -0.250 0.215 -0.375 0.302 -0.321 0.250 -0.314 0.230 0.367 SPSM -0.355 -0.081 -0.312 0.174 -0.332 0.039 -0.286 0.013 -0.356 0.075 -0.338 -0.080 TGW -0.046 -0.264 0.046 0.095 -0.135 -0.109 -0.079 -0.098 -0.215 -0.172 -0.094 0.114 PHT 0.086 -0.165 -0.013 0.010 -0.003 0.029 0.149 -0.070 -- -- 0.043 0.138 FLL 0.277 -0.025 0.277 0.148 0.307 0.099 0.317 0.119 -- -- 0.311 0.029 FLW 0.156 0.177 0.262 0.207 0.246 0.172 0.189 0.378 -- -- 0.247 -0.233 FLA 0.281 0.075 0.339 0.220 0.335 0.162 0.309 0.293 -- -- 0.347 -0.119 FLS 0.166 -0.140 0.046 -0.028 0.098 -0.045 0.132 -0.217 -- -- 0.089 0.212 SL 0.341 -0.060 0.354 -0.012 0.299 -0.183 0.344 -0.063 0.321 -0.163 0.295 0.163 SSN -0.144 0.291 -0.051 0.449 -0.011 0.413 -0.019 0.273 -0.046 0.423 0.031 -0.346 FSN 0.334 0.191 0.361 0.087 0.363 -0.046 0.375 0.001 0.450 0.101 0.356 -0.016 TSN 0.279 0.322 0.314 0.283 0.336 0.161 0.359 0.125 0.397 0.279 0.349 -0.164 SC -0.104 0.394 -0.114 0.301 -0.002 0.365 -0.078 0.234 0.070 0.462 0.005 -0.351 GSP 0.192 -0.322 0.158 -0.445 0.137 -0.402 0.154 -0.327 0.218 -0.335 0.112 0.379 HD 0.076 0.433 0.152 0.369 0.221 0.347 0.108 0.439 0.137 0.449 0.196 -0.396 † PHT, FLL, FLW, FLA, FLS were not evaluated in Kinston 2014. 142 Clarksville 2013 Clarksville 2014 Queenstown 2013 Queenstown 2014 Kinston 2014 Overall Figure 5.1 Principal component analysis: biplot summarizing the relationship among grain yield components, plant architecture, and spike morphology for the MD01W233-06-1 × SS8641doubled haploid population evaluated in five trials from 2013 to 2014. Traits are grain yield (GYLD), grains per spike (GPS), grain weight per spike (GWPS), spikes per square meter (SPSM), and thousand-grain-weight (TGW), plant height (PHT), flag leaf length (FLL), flag leaf width (FLW), flag leaf area (FLA), flag leaf shape (FLS), spike length (SL), sterile spikelet number per spike (SSN), fertile spikelet number per spike (FSN), total spikelet number per spike (TSN), spikelet compactness (SC), and grain number per spikelet (GSP). PHT, FLL, FLW, FLA, FLS were not evaluated at Kinston 2014. 143 Figure 5.21 Dendrogram of cluster analysis and scatter diagram of the doubled-haploid lines for the first two principal components at Clarksville 2013 (E1). Co m po ne nt 2 ( 20 .2 % ) 144 Figure 5.22 Dendrogram of cluster analysis and scatter diagram of the doubled-haploid lines for the first two principal components at Clarksville 2014 (E2). 145 Figure 5.23 Dendrogram of cluster analysis and scatter diagram of the doubled-haploid lines for the first two principal components at Queenstown 2013 (E3). Co m po ne nt 2 ( 22 % ) 146 Figure 5.24 Dendrogram of cluster analysis and scatter diagram of the doubled-haploid lines for the first two principal components at Queenstown 2014 (E4). Co m po ne nt 2 ( 18 .7 % ) 147 Figure 5.25 Dendrogram of cluster analysis and scatter diagram of the doubled-haploid lines for the first two principal components at Kinston 2014 (E5). -6 -4 -2 0 2 4 6 33 3 5 5 5 3 5 5 3 3 3 3 3 3 3 5 5 33 5 3 3 5 3 3 3 4 5 3 5 1 3 4 5 3 5 5 5 1 3 3 3 4 5 3 3 1 1 34 3 34 4 5 1 11 1 4 4 4 3 4 3 4 1 1 1 1 3 1 1 2 1 2 1 1 1 2 2 2 2 22 1 2 2 1 1 2 2 2 11 1 2 2 2 1 2 1 2 1 2 1 1 1 1 1 1 1 1 Cluster 1 2 3 4 5 -6 -4 -2 0 2 4 6 Component 1 (34.6 %) 148 Figure 5.26 Dendrogram of cluster analysis and scatter diagram of the doubled-haploid lines for the first two principal components based on the average of E1, E2, E3, and E4. -6 -4 -2 0 2 4 6 4 4 4 4 3 4 3 5 4 5 44 3 4 4 3 44 3 5 3 5 4 4 3 3 5 55 3 5 5 5 4 5 4 4 5 4 4 5 2 2 3 3 3 5 5 4 3 4 33 4 2 2 33 3 2 2 1 1 3 1 3 1 2 22 2 1 2 2 3 2 1 2 2 2 2 1 2 2 2 2 11 1 1 2 1 5 2 1 1 1 2 2 21 22 2 1 1 1 2 1 1 1 1 1 1 2 Cluster 1 2 3 4 5 -6 -4 -2 0 2 4 6 Component 1 (33.1 %) 149 Table 5.6 Mean and standard error for five clusters based on seventeen yield related traits evaluated at Clarksville 2013 (E1), Clarksville 2014 (E2), Queenstown 2013 (E3), Queenstown 2014 (E4), Kinston 2014 (E5), and average of five environments. Environments Traits Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 E1 GYLD 639.64 ± 6.40 561.74 ± 4.83 493.13 ± 6.07 484.99 ± 7.49 578.20 ± 8.01 GPS 38.15 ± 0.26 35.19 ± 0.20 40.82 ± 0.26 45.10 ± 0.24 43.81 ± 0.30 GWPS 1.19 ± 0.01 1.08 ± 0.01 1.16 ± 0.01 1.38 ± 0.01 1.37 ± 0.01 SPSM 540.92 ± 6.65 527.62 ± 5.15 428.18 ± 5.67 359.14 ± 6.16 426.97 ± 5.01 TGW 32.34 ± 0.18 31.29 ± 0.16 29.19 ± 0.14 31.31 ± 0.14 32.36 ± 0.18 PHT 87.82 ± 0.41 83.68 ± 0.54 85.28 ± 0.37 87.12 ± 0.45 89.57 ± 0.57 FLL 13.52 ± 0.09 12.45 ± 0.09 14.51 ± 0.13 14.54 ± 0.10 13.59 ± 0.13 FLW 1.44 ± 0.01 1.46 ± 0.01 1.53 ± 0.01 1.54 ± 0.01 1.44 ± 0.01 FLA 15.47 ± 0.17 14.46 ± 0.14 17.55 ± 0.18 17.77 ± 0.13 15.53 ± 0.19 FLS 9.48 ± 0.06 8.61 ± 0.08 9.61 ± 0.12 9.56 ± 0.08 9.54 ± 0.10 SL 6.66 ± 0.03 6.67 ± 0.04 7.02 ± 0.03 7.64 ± 0.04 7.41 ± 0.04 SSN 1.81 ± 0.04 2.18 ± 0.04 1.99 ± 0.03 1.70 ± 0.04 1.40 ± 0.03 FSN 13.38 ± 0.05 13.96 ± 0.07 14.80 ± 0.08 15.84 ± 0.07 14.90 ± 0.04 TSN 15.19 ± 0.05 16.14 ± 0.07 16.80 ± 0.08 17.54 ± 0.07 16.30 ± 0.05 SC 2.29 ± 0.01 2.43 ± 0.01 2.40 ± 0.01 2.31 ± 0.01 2.21 ± 0.01 GSP 2.85 ± 0.02 2.52 ± 0.01 2.76 ± 0.01 2.85 ± 0.01 2.94 ± 0.02 HD 132.22 ± 0.13 133.90 ± 0.11 135.11 ± 0.11 134.47 ± 0.06 131.57 ± 0.11 E2 GYLD 789.07 ± 7.10 888.68 ± 5.20 770.68 ± 6.44 756.12 ± 5.43 797.80 ± 9.01 GPS 32.94 ± 0.18 33.31 ± 0.15 38.17 ± 0.21 38.13 ± 0.22 35.97 ± 0.33 GWPS 0.91 ± 0.01 1.06 ± 0.01 1.08 ± 0.01 1.05 ± 0.01 0.97 ± 0.01 SPSM 882.49 ± 8.33 852.99 ± 6.89 722.89 ± 6.07 729.60 ± 6.08 844.23 ± 9.17 TGW 28.79 ± 0.18 33.14 ± 0.07 29.03 ± 0.16 30.59 ± 0.19 28.30 ± 0.23 PHT 87.62 ± 0.45 95.30 ± 0.49 88.50 ± 0.60 89.37 ± 0.42 88.67 ± 0.29 FLL 14.58 ± 0.09 15.38 ± 0.06 15.28 ± 0.12 16.10 ± 0.07 17.21 ± 0.08 FLW 1.40 ± 0.01 1.42 ± 0.01 1.40 ± 0.01 1.59 ± 0.01 1.39 ± 0.01 FLA 16.21 ± 0.14 17.35 ± 0.11 17.06 ± 0.21 20.33 ± 0.17 18.97 ± 0.18 FLS 10.44 ± 0.07 10.89 ± 0.07 10.94 ± 0.09 10.18 ± 0.06 12.47 ± 0.08 SL 6.95 ± 0.03 7.33 ± 0.04 7.33 ± 0.05 7.56 ± 0.03 7.52 ± 0.05 SSN 2.23 ± 0.03 2.40 ± 0.03 1.84 ± 0.03 2.48 ± 0.04 2.42 ± 0.03 FSN 14.68 ± 0.06 14.35 ± 0.08 15.36 ± 0.06 16.14 ± 0.05 15.97 ± 0.08 TSN 16.91 ± 0.06 16.75 ± 0.07 17.20 ± 0.06 18.62 ± 0.06 18.39 ± 0.07 SC 2.44 ± 0.01 2.29 ± 0.01 2.36 ± 0.01 2.47 ± 0.01 2.46 ± 0.01 GSP 2.09 ± 0.01 2.14 ± 0.01 2.34 ± 0.01 2.19 ± 0.01 2.08 ± 0.02 HD 141.42 ± 0.11 141.50 ± 0.07 140.92 ± 0.12 143.59 ± 0.13 142.00 ± 0.10 150 Table 5.6 Continued Environments Traits Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 E3 GYLD 804.35 ± 5.83 672.72 ± 8.33 612.83 ± 8.42 659.55 ± 8.19 825.29 ± 9.62 GPS 49.36 ± 0.27 41.67 ± 0.24 43.34 ± 0.39 47.65 ± 0.30 39.51 ± 0.27 GWPS 1.61 ± 0.01 1.33 ± 0.01 1.39 ± 0.02 1.51 ± 0.01 1.33 ± 0.01 SPSM 506.02 ± 4.75 507.85 ± 5.63 446.12 ± 6.34 438.93 ± 4.67 629.89 ± 9.94 TGW 32.2 ± 0.17 31.32 ± 0.15 31.92 ± 0.20 31.66 ± 0.16 33.22 ± 0.19 PHT 96.11 ± 0.50 94.86 ± 0.47 104.28 ± 0.41 98.66 ± 0.53 100.31 ± 0.43 FLL 16.87 ± 0.11 17.11 ± 0.09 19.57 ± 0.12 18.96 ± 0.09 16.22 ± 0.10 FLW 1.64 ± 0.01 1.71 ± 0.01 1.59 ± 0.01 1.87 ± 0.01 1.62 ± 0.01 FLA 21.89 ± 0.20 23.21 ± 0.18 24.73 ± 0.26 28.08 ± 0.21 20.78 ± 0.18 FLS 10.37 ± 0.07 10.06 ± 0.06 12.41 ± 0.07 10.22 ± 0.08 10.09 ± 0.07 SL 7.48 ± 0.04 7.20 ± 0.03 7.35 ± 0.04 7.69 ± 0.03 6.70 ± 0.03 SSN 1.11 ± 0.04 1.92 ± 0.04 1.62 ± 0.05 1.84 ± 0.05 1.75 ± 0.04 FSN 15.26 ± 0.05 14.88 ± 0.05 14.99 ± 0.06 16.26 ± 0.05 13.96 ± 0.07 TSN 16.38 ± 0.06 16.80 ± 0.06 16.61 ± 0.04 18.09 ± 0.07 15.71 ± 0.08 SC 2.20 ± 0.01 2.34 ± 0.01 2.27 ± 0.01 2.36 ± 0.01 2.36 ± 0.01 GSP 3.13 ± 0.01 2.66 ± 0.01 2.76 ± 0.02 2.79 ± 0.02 2.66 ± 0.02 HD 124.84 ± 0.21 128.91 ± 0.18 129.41 ± 0.17 130.5 ± 0.21 125.96 ± 0.22 E4 GYLD 657.02 ± 3.15 601.17 ± 3.10 595.94 ± 6.01 553.28 ± 2.13 655.64 ± 4.44 GPS 39.42 ± 0.20 34.50 ± 0.21 39.97 ± 0.24 42.94 ± 0.28 44.83 ± 0.36 GWPS 1.12 ± 0.01 1.00 ± 0.01 1.11 ± 0.01 1.20 ± 0.01 1.32 ± 0.01 SPSM 591.24 ± 5.10 603.86 ± 2.58 539.76 ± 4.95 465.42 ± 3.43 500.91 ± 4.10 TGW 28.97 ± 0.16 30.14 ± 0.20 28.57 ± 0.15 29.02 ± 0.21 30.16 ± 0.17 PHT 76.85 ± 0.34 73.53 ± 0.36 75.55 ± 0.52 78.16 ± 0.34 79.49 ± 0.49 FLL 13.99 ± 0.08 13.93 ± 0.08 15.25 ± 0.10 16.66 ± 0.09 15.59 ± 0.08 FLW 1.32 ± 0.00 1.41 ± 0.01 1.44 ± 0.01 1.61 ± 0.01 1.36 ± 0.01 FLA 14.60 ± 0.09 15.65 ± 0.17 17.47 ± 0.15 21.39 ± 0.22 16.86 ± 0.13 FLS 10.67 ± 0.07 9.92 ± 0.06 10.64 ± 0.09 10.35 ± 0.05 11.55 ± 0.09 SL 6.47 ± 0.03 6.38 ± 0.03 6.84 ± 0.04 7.19 ± 0.03 7.47 ± 0.04 SSN 1.02 ± 0.03 1.51 ± 0.04 1.14 ± 0.03 1.51 ± 0.04 1.13 ± 0.03 FSN 13.58 ± 0.05 13.09 ± 0.05 14.15 ± 0.05 15.21 ± 0.05 15.07 ± 0.07 TSN 14.61 ± 0.06 14.60 ± 0.06 15.29 ± 0.06 16.72 ± 0.04 16.20 ± 0.06 SC 2.26 ± 0.01 2.30 ± 0.01 2.25 ± 0.01 2.33 ± 0.01 2.18 ± 0.01 GSP 2.90 ± 0.01 2.62 ± 0.01 2.81 ± 0.02 2.81 ± 0.01 2.96 ± 0.02 HD 138.46 ± 0.10 140.07 ± 0.13 140.30 ± 0.12 142.25 ± 0.09 138.54 ± 0.12 151 Table 5.6 Continued Environment Traits Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 E5 GYLD 591.65 ± 5.44 585.89 ± 5.01 545.90 ± 4.71 400.65 ± 6.28 544.87 ± 5.99 GPS 40.11 ± 0.22 37.55 ± 0.15 45.84 ± 0.35 40.47 ± 0.20 48.73 ± 0.19 GWPS 1.17 ± 0.01 1.10 ± 0.01 1.20 ± 0.01 1.03 ± 0.01 1.35 ± 0.01 SPSM 508.88 ± 5.51 534.13 ± 3.68 458.08 ± 4.65 388.77 ± 5.29 404.32 ± 3.96 TGW 28.91 ± 0.23 29.26 ± 0.20 25.74 ± 0.14 24.52 ± 0.26 27.27 ± 0.21 SL 7.46 ± 0.04 7.07 ± 0.03 7.76 ± 0.04 7.42 ± 0.03 8.05 ± 0.05 SSN 2.21 ± 0.04 2.96 ± 0.04 2.74 ± 0.04 2.30 ± 0.04 1.82 ± 0.03 FSN 15.40 ± 0.08 15.09 ± 0.05 17.25 ± 0.08 16.15 ± 0.05 16.70 ± 0.08 TSN 17.61 ± 0.09 18.05 ± 0.05 19.99 ± 0.08 18.45 ± 0.05 18.52 ± 0.09 SC 2.37 ± 0.01 2.56 ± 0.01 2.59 ± 0.01 2.50 ± 0.01 2.31 ± 0.01 GSP 2.60 ± 0.01 2.48 ± 0.01 2.65 ± 0.01 2.51 ± 0.02 2.92 ± 0.01 HD 113.25 ± 0.23 116.81 ± 0.12 119.03 ± 0.22 114.95 ± 0.17 112.26 ± 0.32 All five trials GYLD 692.85 ± 5.00 654.56 ± 4.01 677.91 ± 3.08 589.85 ± 5.71 627.68 ± 5.26 GPS 38.68 ± 0.18 37.06 ± 0.19 44.33 ± 0.23 42.53 ± 0.27 40.68 ± 0.30 GWPS 1.15 ± 0.00 1.1 ± 0.01 1.34 ± 0.01 1.22 ± 0.01 1.18 ± 0.01 SPSM 617.91 ± 5.00 612.58 ± 4.18 519.57 ± 3.94 497.52 ± 4.10 547.24 ± 3.53 TGW 30.1 ± 0.18 29.9 ± 0.15 30.56 ± 0.19 29.43 ± 0.14 29.56 ± 0.23 PHT 88.28 ± 0.30 84.37 ± 0.43 89.45 ± 0.46 85.89 ± 0.48 91.36 ± 0.31 FLL 14.65 ± 0.08 14.66 ± 0.07 15.73 ± 0.07 15.8 ± 0.11 16.39 ± 0.10 FLW 1.44 ± 0.01 1.49 ± 0.01 1.47 ± 0.01 1.64 ± 0.01 1.46 ± 0.01 FLA 16.86 ± 0.14 17.43 ± 0.11 18.44 ± 0.12 20.72 ± 0.22 19.12 ± 0.17 FLS 10.22 ± 0.06 9.89 ± 0.06 10.77 ± 0.06 9.7 ± 0.06 11.31 ± 0.08 SL 6.67 ± 0.03 7.03 ± 0.03 7.52 ± 0.03 7.44 ± 0.03 7.36 ± 0.04 SSN 1.73 ± 0.03 2.04 ± 0.03 1.51 ± 0.02 1.93 ± 0.04 2.11 ± 0.03 FSN 14.09 ± 0.04 14.52 ± 0.05 15.31 ± 0.06 15.8 ± 0.06 15.41 ± 0.07 TSN 15.82 ± 0.05 16.56 ± 0.05 16.81 ± 0.05 17.73 ± 0.07 17.51 ± 0.06 SC 2.38 ± 0.01 2.36 ± 0.01 2.24 ± 0.01 2.39 ± 0.01 2.39 ± 0.01 GSP 2.69 ± 0.01 2.49 ± 0.01 2.84 ± 0.01 2.63 ± 0.01 2.57 ± 0.01 HD 130.29 ± 0.14 131.92 ± 0.16 129.42 ± 0.12 133.44 ± 0.16 132.94 ± 0.12 152 a) Phenotype network at 2013 Clarksville, MD (E1). Model fit indices: SRMR=0.04 RMSEA=0.06 NNFI=0.97 CFI=0.98 b) Phenotype network at 2014 Clarksville, MD (E2). Model fit indices: SRMR=0.03 RMSEA=0.05 NNFI=0.98 CFI=0.99 153 c) Phenotype network at 2013 Queenstwon, MD (E3). Model fit indices: SRMR=0.03 RMSEA=0.06 NNFI=0.97 CFI=0.98 d) Phenotype network at 2014 Queenstwon, MD (E4). Model fit indices: SRMR=0.03 RMSEA=0.07 NNFI=0.96 CFI=0.98 154 Figure 5.3 Graphical representaion of the structural equation modeling for the phenotypic network based on data from a) Clarksville 2013, b) Clarksville 2014, c) Queenstown 2013, d) Queenstown 2014, f) Kinston 2014, and g) First four environments averaged. Red arrows indicate negative contribution. Green arrows indicate error covariance. e) Phenotype network at 2014 Kinston, NC (E4). Model fit indices: SRMR=0.08 RMSEA=0.06 NNFI=0.98 CFI=0.99 Plant architecture traits were not evaluated in E4. f) Phenotype network based on E1, E2, E3, and E4. Model fit indices: SRMR=0.04 RMSEA=0.07 NNFI=0.96 CFI=0.98 155 Appendix A. Table A.1 Source of simple sequence repeats (SSRs) on the linkage map constructed in this study SSR Marker Source Reference Xbarc100 USDA-ARS Beltsville Agricultural Research Center (Song et al., 2005) Theor Appl Genet 110: 550–560 Xbarc101 USDA-ARS Beltsville Agricultural Research Center (Song et al., 2005) Theor Appl Genet 110: 550–560 Xbarc10 USDA-ARS Beltsville Agricultural Research Center (Song et al., 2005) Theor Appl Genet 110: 550–560 Xbarc127 USDA-ARS Beltsville Agricultural Research Center (Song et al., 2005) Theor Appl Genet 110: 550–560 Xbarc12 USDA-ARS Beltsville Agricultural Research Center (Song et al., 2005) Theor Appl Genet 110: 550–560 Xbarc147 USDA-ARS Beltsville Agricultural Research Center (Song et al., 2005) Theor Appl Genet 110: 550–560 Xbarc163 USDA-ARS Beltsville Agricultural Research Center (Song et al., 2005) Theor Appl Genet 110: 550–560 Xbarc164 USDA-ARS Beltsville Agricultural Research Center (Song et al., 2005) Theor Appl Genet 110: 550–560 Xbarc170 USDA-ARS Beltsville Agricultural Research Center (Song et al., 2005) Theor Appl Genet 110: 550–560 Xbarc28 USDA-ARS Beltsville Agricultural Research Center (Song et al., 2005) Theor Appl Genet 110: 550–560 Xbarc45 USDA-ARS Beltsville Agricultural Research Center (Song et al., 2005) Theor Appl Genet 110: 550–560 Xbarc59 USDA-ARS Beltsville Agricultural Research Center (Song et al., 2005) Theor Appl Genet 110: 550–560 Xbarc80 USDA-ARS Beltsville Agricultural Research Center (Song et al., 2005) Theor Appl Genet 110: 550–560 Xgdm136 Gatersleben D-genome Microsatellite (Pestsova et al.,2000) Genome 43: 689–697 Xgwm111 Gatersleben wheat microsatellite (Roder et al., 1998) Genetics 149: 2007–2024 Xgwm11 Gatersleben wheat microsatellite (Roder et al., 1998) Genetics 149: 2007–2025 Xgwm261 Gatersleben wheat microsatellite (Roder et al., 1998) Genetics 149: 2007–2026 Xgwm282 Gatersleben wheat microsatellite (Roder et al., 1998) Genetics 149: 2007–2027 Xgwm304 Gatersleben wheat microsatellite (Roder et al., 1998) Genetics 149: 2007–2028 Xgwm319 Gatersleben wheat microsatellite (Roder et al., 1998) Genetics 149: 2007–2029 Xwmc273 Wheat Microsatellite Consortium Somers and Isaac, 2004. SSRs from the Wheat Microsatellite Consortium http://wheat.pw.usda.gov/ggpages/SSR/WMC/ Xwmc496 Wheat Microsatellite Consortium 156 Appendix B. Fi gu re B .1 H ea t m ap o f t he g en et ic li nk ag e m ap b as ed o n re co m bi na tio n fre qu en ci es am on g 85 9 D N A m ar ke rs . M ar ke rs ar e a lig ne d al on g ea ch ch ro m os om e f ro m 1 A to 7 D . 157 F ig ur e B .2 G en om e- w id e d ist rib ut io n of L O D sc or e. M ar ke rs ar e a lig ne d al on g ea ch ch ro m os om e f ro m 1 A to 7 D ac co rd in g to th ei r or de r o n ea ch ch ro m os om e. U ni t o f X -a xi s: cM . 158 Appendix C. Table C.1 Summary of major and possible new QTLs identified in the present study. QTLs detected in multiple environments were indicated by asterisk. QTL Trait Marker interval LOD PVE Additive effect QFlw.cz-2A.2 FLW Xsnp2471-Xsnp2461 13.3 31.2% 0.13 Major QFsn.cz-1A FSN Xsnp1970-Xbarc28 20.6 30.0% 0.65 Major * QGps.cz-1A.1 GPS Xsnp1970-Xbarc28 14.2 26.5% 2.44 Major QGps.cz-1A.2 GPS Xbarc28-Xsnp2005 21.2 44.1% 2.95 Major * QGps.cz-3A.2 GPS Xsnp3049-Xsnp3021 24.1 52.0% -3.20 Major QGws.cz-1A.1 GWPS Xwmc496-Xsnp1970 12.2 28.6% 0.08 Major QGws.cz-1A.2 GWPS Xsnp1970-Xbarc28 11.5 33.2% 0.06 Major * QPht.cz-5B.1 PHT Xsnp4068-Xsnp4012 9.3 20.1% 2.86 Major QSc.cz-5A.3 SC Xgwm304-Xsnp996 27.7 80.2% -0.15 Major * QSc.cz-2B.1 SC Xsnp2773-Xgwm319 9.3 21.6% 0.07 Major * QSc.cz-5A.1 SC Xsnp279-Xsnp3878 12.7 26.7% 0.09 Major QSl.cz-1A SL Xsnp1970-Xbarc28 9.3 22.4% 0.25 Major * QSsm.cz-1A.1 SPSM Xwmc496-Xsnp1970 13.9 30.1% -50.85 Major * QSsm.cz-1A.2 SPSM Xsnp1970-Xbarc28 15.6 22.1% -34.04 Major QSsm.cz-1A.3 SPSM Xbarc28-Xsnp2005 8.3 23.0% -51.60 Major * QSsn.cz-2D.1 SSN Xsnp2862-XPpdD1 16.3 30.0% -0.31 Major QTgw.cz-7A.5 TGW Xsnp4588-Xsnp4620 26.8 71.2% -1.77 Major QTsn.cz-1A TSN Xsnp1970-Xbarc28 13.2 20.0% 0.57 Major QTsn.cz-2D.3 TSN XPpdD1-Xsnp2869 6.5 20.9% -0.41 Major QFla.cz-1A.1 FLA Xsnp1970-Xbarc28 8.3 16.8% 1.36 New QFla.cz-1A.2 FLA Xbarc28-Xsnp2005 4.3 9.1% 0.70 New QFla.cz-1B FLA Xsnp4503-Xsnp2181 4.1 9.4% 0.71 New QFla.cz-2A.1 FLA Xsnp2471-Xsnp2461 13.1 28.7% 4.18 New QFla.cz-2A.2 FLA Xsnp2461-Xsnp2466 6.5 13.4% 1.23 New QFla.cz-2D.1 FLA Xsnp2862-XPpdD1 9.5 20.9% -1.53 New QFla.cz-2D.2 FLA XPpdD1-Xsnp2869 8.7 24.1% -1.10 New * QFla.cz-2D.3 FLA Xsnp2804-Xsnp1766 4.6 9.6% -0.72 New QFla.cz-3B FLA Xsnp3407-Xbarc147 3.8 6.8% 2.03 New QFla.cz-5B.1 FLA Xsnp4140-Xsnp4114 3.3 7.9% -0.63 New QFla.cz-5B.2 FLA Xsnp4083-Xsnp3988 3.2 11.4% -2.47 New QFla.cz-6A FLA Xsnp473-Xsnp4228 3.2 10.1% -0.76 New QFll.cz-1A.1 FLL Xsnp1970-Xbarc28 4.5 10.0% 0.76 New * QFll.cz-2D FLL XPpdD1-Xsnp2869 7.5 20.6% -0.58 New * QGws.cz-5B GWPS Xsnp4130-Xsnp3884 3.6 7.3% -0.04 New QGws.cz-7B GWPS Xsnp4927-Xsnp489 3.1 7.5% 0.03 New Qyld.cz-2A GYLD Xsnp2477-Xsnp2432 4.3 9.9% 25.21 New QYld.cz-5B.2 GYLD Xsnp4011-Xsnp4073 4.5 10.3% 25.67 New QPht.cz-2D.2 PHT Xsnp2795-Xsnp708 8.4 22.0% 2.79 New QSl.cz-5A.1 SL Xsnp3819-Xsnp3789 8.2 13.0% 0.20 New QSl.cz-5A.2 SL Xsnp3789-Xsnp3844 6.2 12.5% 0.18 New QSl.cz-5A.3 SL Xsnp3812-Xsnp3856 4.0 9.7% 0.16 New QSl.cz-5A.4 SL Xsnp3852-Xsnp3843 7.0 13.1% 0.19 New QSl.cz-5A.5 SL Xsnp3849-Xsnp3841 2.8 6.7% 0.13 New QTgw.cz-3A.6 TGW Xsnp2951-Xsnp2971 4.2 6.7% -0.57 New 159 Appendix D. Table D.1 Cluster membership of 124 doubled haploid lines based on data from: Clarksville 2013 (E1), Clarksville 2014 (E2), Queenstown 2013 (E3), Queenstown 2014 (E4), Kinston 2014 (E5), and average of five environments. No. Name E1 E2 E4 E4 E5 five average 1 DH1 1 1 1 1 1 1 2 DH3 2 3 2 3 2 1 3 DH4 2 1 2 2 2 2 4 DH5 3 3 2 3 1 2 5 DH6 1 3 1 1 1 3 6 DH7 2 4 2 2 3 2 7 DH8 5 3 1 3 5 3 8 DH9 3 4 4 4 3 4 9 DH11 5 3 3 5 1 3 10 DH12 4 3 2 3 4 4 11 DH13 3 5 4 4 3 5 12 DH14 2 5 2 5 2 5 13 DH15 1 1 5 1 1 1 14 DH16 1 1 5 3 1 1 15 DH17 5 3 1 5 1 3 16 DH18 4 4 4 4 3 4 17 DH19 1 1 5 2 1 1 18 DH20 3 4 4 4 3 4 19 DH21 1 2 3 3 2 5 20 DH22 4 4 4 5 3 4 21 DH23 1 3 5 1 5 1 22 DH24 1 1 5 3 1 1 23 DH25 5 1 5 2 1 2 24 DH26 2 1 2 3 4 2 25 DH27 1 1 3 3 2 1 26 DH28 1 1 1 1 2 1 27 DH29 2 1 1 2 3 2 28 DH30 1 5 4 3 4 5 29 DH31 3 1 2 3 1 2 30 DH32 3 1 2 2 3 2 31 DH33 3 4 4 4 3 4 32 DH34 5 3 4 5 5 3 33 DH35 2 1 2 2 2 2 34 DH36 4 5 4 3 3 4 35 DH37 3 5 2 3 3 5 36 DH38 5 1 1 3 4 3 37 DH39 2 5 5 1 3 1 38 DH40 5 3 3 3 4 5 39 DH41 1 1 5 3 2 2 40 DH42 3 5 5 3 3 5 41 DH43 1 3 2 1 4 1 42 DH44 5 2 3 3 5 5 43 DH45 1 2 2 2 1 2 44 DH46 4 5 4 3 3 4 45 DH47 1 1 2 1 2 2 46 DH49 5 4 4 3 5 4 47 DH50 1 1 5 2 2 2 48 DH51 3 5 4 3 3 5 49 DH52 1 1 1 1 1 1 50 DH53 2 1 2 3 2 2 51 DH54 4 3 1 3 5 3 52 DH55 2 3 5 1 2 1 53 DH56 2 1 5 1 1 1 54 DH57 2 1 5 3 4 1 55 DH58 1 1 5 1 1 1 56 DH59 1 3 5 1 1 1 57 DH60 1 3 1 3 5 3 58 DH61 2 1 2 2 1 2 59 DH62 1 3 1 1 4 1 60 DH63 5 3 1 5 5 3 61 DH64 5 2 5 3 1 3 62 DH65 5 5 1 5 5 3 160 Table D.1 Continued. No. Name E1 E2 E4 E4 E5 five average 63 DH66 4 4 4 4 3 4 64 DH67 2 1 2 2 1 2 65 DH68 3 1 2 3 4 4 66 DH69 1 3 5 3 5 1 67 DH70 4 4 2 3 3 5 68 DH71 4 3 4 3 3 4 69 DH72 3 4 2 3 3 4 70 DH73 3 4 2 3 3 4 71 DH75 1 2 5 5 1 1 72 DH76 1 1 5 2 2 2 73 DH77 2 5 3 5 1 5 74 DH78 5 3 1 5 5 3 75 DH79 1 3 1 5 1 3 76 DH80 4 3 3 5 1 3 77 DH81 5 4 5 3 4 4 78 DH82 5 1 1 1 1 1 79 DH83 1 2 5 2 2 5 80 DH84 1 2 5 1 1 1 81 DH85 1 1 5 3 1 2 82 DH86 5 1 5 1 1 1 83 DH87 5 3 1 1 5 3 84 DH89 2 1 5 2 2 2 85 DH90 1 3 2 3 1 2 86 DH91 1 3 5 1 1 1 87 DH92 1 1 5 2 1 2 88 DH93 5 5 1 5 5 3 89 DH94 2 1 5 1 2 2 90 DH95 1 2 5 2 1 1 91 DH96 1 2 5 1 1 1 92 DH97 5 1 5 3 1 3 93 DH98 5 3 1 1 5 3 94 DH99 3 3 4 3 3 4 95 DH100 2 3 1 3 3 4 96 DH101 5 3 1 1 5 3 97 DH102 4 4 4 3 3 5 98 DH103 3 3 2 2 3 4 99 DH104 3 4 3 3 3 5 100 DH105 3 3 5 3 2 1 101 DH106 3 4 4 3 3 4 102 DH107 1 1 5 1 1 1 103 DH108 2 1 5 1 2 2 104 DH109 3 1 2 3 3 2 105 DH110 5 3 2 5 5 5 106 DH111 4 3 2 3 5 3 107 DH112 5 3 2 3 4 4 108 DH113 1 3 5 2 1 1 109 DH114 2 3 2 3 1 2 110 DH115 2 1 2 3 2 2 111 DH116 2 1 5 2 1 2 112 DH117 2 1 5 3 1 2 113 DH119 4 4 4 4 1 4 114 DH120 2 5 5 2 2 2 115 DH121 2 1 2 2 3 2 116 DH122 4 1 3 3 3 5 117 DH123 3 5 3 3 1 5 118 DH124 1 5 2 3 3 2 119 DH125 1 1 5 2 2 1 120 DH126 4 3 4 3 3 4 121 DH128 3 5 3 3 3 5 122 DH129 1 3 1 1 1 3 123 DH130 3 1 2 3 1 2 124 DH131 3 4 4 4 3 4 161 Appendix E. Table E.1 Phenotypic data for yield contributing traits evaluated at Clarksville 2013 (E1), Clarksville 2014 (E2), Queenstown 2013 (E3), Queenstown 2014 (E4), and Kinston 2014 (E5). Two replications at each environment. Missing data is indicated by dot. No. Env. Rep. Name GYLD GPS GWPS SPSM TGW PHT FLL FLW FLA FLS SL SSN FSN TSN SC GSP HD 1 E1 1 DH1 658.7 34.7 1.0 646.4 29.6 86.5 10.9 1.2 10.2 9.3 6.2 1.5 13.3 14.8 2.4 2.6 131 2 E1 1 DH3 696.5 35.3 1.1 636.7 32.7 91.2 15.5 1.6 19.7 9.6 6.1 2.0 12.8 14.8 2.4 2.8 134 3 E1 1 DH4 494.4 31.9 1.0 477.2 33.5 78.2 12.9 1.6 16.0 8.3 6.5 2.2 14.3 16.5 2.6 2.2 136 4 E1 1 DH5 499.8 35.1 1.0 483.8 30.3 74.5 15.6 1.6 20.3 9.6 6.9 2.1 13.5 15.6 2.3 2.6 136 5 E1 1 DH6 697.4 40.4 1.3 543.6 32.7 90.0 13.7 1.4 15.2 9.7 6.8 1.3 14.2 15.5 2.3 2.8 129 6 E1 1 DH7 564.1 32.5 1.0 560.1 29.9 81.2 13.4 1.5 16.1 8.9 6.3 2.9 13.9 16.8 2.7 2.3 135 7 E1 1 DH8 640.2 42.3 1.2 520.5 30.5 93.9 14.2 1.5 17.1 9.5 7.3 2.2 14.4 16.6 2.3 2.9 132 8 E1 1 DH9 354.9 39.4 1.1 335.4 29.9 79.6 15.9 1.6 20.4 9.9 7.2 2.3 15.8 18.1 2.5 2.5 133 9 E1 1 DH11 720.7 44.8 1.5 475.4 35.1 110.9 16.1 1.4 18.4 11.2 8.4 1.7 15.0 16.7 2.0 3.0 131 10 E1 1 DH12 416.1 41.3 1.2 341.6 30.7 82.3 13.0 1.4 15.0 9.0 7.1 1.8 14.6 16.4 2.4 2.8 132 11 E1 1 DH13 482.5 42.5 1.2 389.5 28.0 88.5 15.0 1.4 16.8 10.6 7.1 2.0 15.8 17.8 2.5 2.7 137 12 E1 1 DH14 632.4 38.7 1.3 503.1 32.7 87.0 12.6 1.6 15.9 8.1 6.7 2.0 14.7 16.7 2.5 2.6 137 13 E1 1 DH15 640.0 35.3 1.1 579.1 31.8 80.7 12.9 1.4 14.1 9.3 6.6 1.5 13.3 14.8 2.2 2.7 130 14 E1 1 DH16 601.1 39.7 1.2 513.8 31.6 87.5 13.2 1.5 16.0 8.6 6.4 1.8 13.5 15.3 2.4 2.9 133 15 E1 1 DH17 596.6 40.7 1.4 417.2 34.7 87.4 15.2 1.4 16.4 11.3 7.5 1.3 14.0 15.3 2.0 2.9 130 16 E1 1 DH18 424.7 43.8 1.4 307.7 31.0 81.3 13.8 1.7 18.7 8.0 7.4 1.7 16.1 17.8 2.4 2.7 135 17 E1 1 DH19 660.7 35.3 1.2 573.5 33.4 91.7 13.5 1.5 16.2 9.0 6.2 1.9 12.4 14.3 2.3 2.8 133 18 E1 1 DH20 500.7 47.7 1.3 371.2 28.8 73.8 13.7 1.6 17.2 8.7 6.7 1.3 15.0 16.3 2.4 3.2 134 19 E1 1 DH21 583.2 37.1 1.2 488.4 33.1 93.7 15.2 1.5 18.1 10.3 6.9 2.8 13.4 16.2 2.3 2.8 134 20 E1 1 DH22 611.5 39.5 1.2 500.0 33.4 95.3 14.6 1.6 18.4 9.2 7.1 2.5 15.7 18.2 2.6 2.5 134 21 E1 1 DH23 663.0 41.2 1.3 522.5 31.1 87.4 16.1 1.4 17.4 11.8 6.3 1.2 13.2 14.4 2.3 3.1 131 22 E1 1 DH24 748.8 33.4 1.0 744.3 31.0 93.6 12.1 1.5 14.6 7.9 6.0 1.8 12.4 14.2 2.4 2.7 132 23 E1 1 DH25 672.5 44.1 1.3 515.4 33.8 79.2 13.7 1.4 15.4 9.6 7.4 1.9 14.8 16.7 2.3 3.0 130 24 E1 1 DH26 644.8 33.1 1.1 609.4 33.1 79.5 12.7 1.5 15.4 8.4 6.6 2.4 13.7 16.1 2.4 2.4 132 25 E1 1 DH27 586.9 41.1 1.2 479.5 30.5 91.3 15.0 1.4 16.9 10.5 6.3 1.4 12.9 14.3 2.3 3.2 134 26 E1 1 DH28 602.4 38.7 1.2 514.8 32.0 93.5 13.2 1.5 15.9 8.8 6.4 2.2 14.1 16.3 2.6 2.7 133 27 E1 1 DH29 442.6 30.9 1.0 428.0 33.7 70.8 12.1 1.4 13.3 8.9 6.9 1.8 13.6 15.4 2.2 2.3 135 28 E1 1 DH30 601.2 34.1 1.2 520.9 32.8 86.0 15.1 1.6 19.1 9.4 6.5 2.4 13.3 15.7 2.4 2.6 133 29 E1 1 DH31 488.3 36.8 1.0 487.3 29.0 83.2 14.5 1.5 17.1 9.7 6.9 2.0 13.7 15.7 2.3 2.7 136 30 E1 1 DH32 421.5 38.4 1.0 435.5 25.7 81.1 13.9 1.6 17.7 8.6 7.4 1.2 16.4 17.6 2.4 2.3 137 31 E1 1 DH33 415.1 48.6 1.0 400.7 28.9 90.0 16.0 1.7 21.6 9.4 7.1 2.0 16.3 18.3 2.6 3.0 137 32 E1 1 DH34 574.3 45.6 1.3 444.5 30.7 92.2 15.2 1.5 17.7 10.3 7.3 1.2 15.2 16.4 2.3 3.0 132 33 E1 1 DH35 576.1 37.9 1.2 480.1 33.7 85.8 11.4 1.9 17.0 6.1 7.1 1.9 13.6 15.5 2.2 2.8 134 34 E1 1 DH36 418.7 40.8 1.3 330.5 31.5 81.9 14.7 1.5 17.3 10.1 8.1 1.4 17.0 18.4 2.3 2.4 135 35 E1 1 DH37 553.4 42.1 1.1 504.0 29.7 92.2 15.6 1.6 19.4 10.1 7.1 2.4 14.4 16.8 2.4 2.9 134 36 E1 1 DH38 533.9 41.7 1.2 445.7 29.4 82.3 13.8 1.4 15.3 9.8 7.1 1.2 14.4 15.6 2.2 2.9 129 37 E1 1 DH39 576.6 39.7 1.2 499.6 30.1 89.7 10.4 1.1 9.4 9.2 6.1 2.1 15.4 17.5 2.9 2.6 137 38 E1 1 DH40 547.8 37.1 1.2 450.5 32.8 94.0 12.1 1.4 13.4 9.1 7.7 1.1 14.7 15.8 2.1 2.5 132 39 E1 1 DH41 601.8 39.5 1.1 544.1 30.3 86.3 16.6 1.4 18.4 12.0 6.6 1.3 13.5 14.8 2.2 2.9 130 40 E1 1 DH42 571.0 44.6 1.4 417.1 30.7 85.8 16.5 1.4 18.1 11.9 7.5 1.5 16.8 18.3 2.5 2.7 134 41 E1 1 DH43 615.0 34.9 1.0 608.3 31.9 89.7 13.7 1.5 16.6 9.0 6.1 2.3 12.3 14.6 2.4 2.8 134 42 E1 1 DH44 765.9 34.8 1.3 612.2 36.0 106.0 16.0 1.6 20.7 9.8 7.6 1.9 13.9 15.8 2.1 2.5 136 43 E1 1 DH45 641.7 35.9 1.3 505.7 35.3 90.6 11.5 1.5 13.5 7.7 7.1 1.9 13.9 15.8 2.2 2.6 133 44 E1 1 DH46 478.0 41.8 1.1 416.4 29.4 87.4 15.8 1.6 19.4 10.2 7.8 2.3 14.2 16.5 2.1 2.9 134 45 E1 1 DH47 699.0 37.0 1.1 618.6 32.3 86.7 13.8 1.4 15.8 9.6 6.8 2.1 13.5 15.6 2.3 2.7 135 46 E1 1 DH49 599.8 46.2 1.4 435.6 30.9 88.7 14.4 1.5 17.4 9.4 7.6 1.8 14.6 16.4 2.2 3.2 134 47 E1 1 DH50 669.3 34.4 1.1 585.6 34.4 88.7 15.1 1.6 19.5 9.3 6.2 2.1 12.5 14.6 2.3 2.8 131 48 E1 1 DH51 507.0 42.6 1.0 507.5 29.2 90.7 16.5 1.6 21.5 10.0 7.2 2.6 15.1 17.7 2.5 2.8 134 49 E1 1 DH52 748.9 37.9 1.1 709.9 28.6 78.4 13.3 1.4 15.2 9.5 6.5 1.5 14.1 15.6 2.4 2.7 131 50 E1 1 DH53 517.6 38.0 1.1 460.9 29.0 73.7 12.8 1.4 14.9 8.8 6.6 1.4 14.6 16.0 2.4 2.6 134 51 E1 1 DH54 487.7 41.0 1.3 370.0 33.7 77.2 14.7 1.6 18.3 9.6 7.3 1.9 13.8 15.7 2.2 3.0 133 52 E1 1 DH55 409.0 41.3 0.9 431.8 25.7 84.8 12.4 1.8 18.0 6.7 6.4 2.1 13.5 15.6 2.4 3.1 135 53 E1 1 DH56 447.3 34.6 1.0 470.4 29.5 85.3 12.1 1.5 14.8 8.0 6.1 2.1 12.7 14.8 2.4 2.7 132 54 E1 1 DH57 596.6 32.9 1.0 590.6 30.0 78.1 12.8 1.4 14.7 8.9 5.6 1.8 13.1 14.9 2.7 2.5 132 55 E1 1 DH58 595.2 38.0 1.1 528.2 32.5 90.7 14.8 1.5 17.7 9.9 6.4 2.0 12.7 14.7 2.3 3.0 132 56 E1 1 DH59 807.9 33.7 1.0 791.2 29.6 88.0 13.7 1.4 15.4 9.6 6.0 1.3 12.5 13.8 2.3 2.7 131 57 E1 1 DH60 653.8 39.8 1.2 536.8 31.2 81.8 14.3 1.4 16.4 10.0 6.7 2.0 12.9 14.9 2.2 3.1 132 58 E1 1 DH61 533.8 34.2 1.1 496.1 31.6 81.3 12.5 1.7 16.6 7.6 6.9 1.8 13.8 15.6 2.3 2.5 135 59 E1 1 DH62 507.1 41.8 1.2 437.1 28.5 81.9 14.2 1.5 17.4 9.3 6.6 0.7 13.3 14.0 2.1 3.1 132 60 E1 1 DH63 507.0 47.1 1.6 313.6 32.1 84.0 14.1 1.4 15.6 10.1 7.6 1.1 14.9 16.0 2.1 3.2 131 61 E1 1 DH64 586.2 39.4 1.4 428.9 36.3 91.1 14.4 1.5 16.9 9.8 7.4 0.9 13.7 14.6 2.0 2.9 131 62 E1 1 DH65 595.4 43.8 1.3 460.5 29.8 81.6 14.1 1.3 14.7 10.9 7.3 1.1 14.9 16.0 2.2 2.9 130 162 Table E.1 Continued. No. Env. Rep. Name GYLD GPS GWPS SPSM TGW PHT FLL FLW FLA FLS SL SSN FSN TSN SC GSP HD 63 E1 1 DH66 354.2 40.0 1.4 250.2 34.0 87.6 14.8 1.7 19.5 8.9 7.8 2.9 16.5 19.4 2.5 2.4 134 64 E1 1 DH67 560.6 37.2 1.1 511.5 . 84.1 12.0 1.3 12.1 9.4 6.8 1.8 14.1 15.9 2.3 2.6 133 65 E1 1 DH68 455.1 37.5 1.1 428.2 28.5 80.5 15.2 1.6 18.7 9.8 6.7 2.0 13.1 15.1 2.3 2.9 133 66 E1 1 DH69 649.4 38.9 1.1 608.7 30.8 83.4 12.5 1.4 13.6 9.1 6.2 1.5 12.5 14.0 2.3 3.1 132 67 E1 1 DH70 633.5 36.6 1.2 544.2 32.5 89.7 15.5 1.5 18.6 10.3 6.9 2.0 14.2 16.2 2.3 2.6 136 68 E1 1 DH71 573.9 44.8 1.3 427.3 30.7 82.1 14.3 1.5 17.3 9.4 7.7 1.5 15.6 17.1 2.2 2.9 135 69 E1 1 DH72 578.3 43.3 1.2 469.4 30.5 87.0 14.8 1.6 18.4 9.4 7.2 1.6 15.8 17.4 2.4 2.7 137 70 E1 1 DH73 498.6 38.2 1.0 492.2 28.7 80.2 12.7 1.8 18.0 7.1 6.8 2.0 15.0 17.0 2.5 2.5 136 71 E1 1 DH75 614.6 36.6 1.2 500.1 34.3 89.9 12.2 1.3 12.6 9.7 6.8 1.9 12.9 14.8 2.2 2.8 130 72 E1 1 DH76 630.3 36.8 1.2 531.0 30.7 81.9 13.0 1.4 14.3 9.3 6.7 1.5 13.7 15.2 2.3 2.7 134 73 E1 1 DH77 688.1 35.1 1.2 556.7 33.7 89.9 16.1 1.4 18.5 11.1 8.1 2.1 16.0 18.1 2.2 2.2 133 74 E1 1 DH78 535.2 41.0 1.2 459.8 30.9 83.8 12.3 1.3 13.1 9.1 7.2 2.1 14.2 16.3 2.3 2.9 131 75 E1 1 DH79 562.7 42.6 1.3 432.2 30.3 89.5 14.2 1.5 17.1 9.3 7.0 2.0 13.9 15.9 2.3 3.1 133 76 E1 1 DH80 657.8 42.4 1.3 502.1 30.6 90.5 14.2 1.5 17.4 9.4 6.6 1.9 14.9 16.8 2.5 2.8 135 77 E1 1 DH81 400.0 27.2 0.9 433.4 32.6 88.6 11.8 1.5 13.7 8.1 6.6 0.9 16.0 16.9 2.6 1.7 132 78 E1 1 DH82 644.4 36.6 1.0 626.9 31.8 84.9 11.9 1.6 14.9 7.6 6.9 1.9 13.8 15.7 2.3 2.7 130 79 E1 1 DH83 646.3 39.5 1.3 499.9 35.1 99.7 13.8 1.4 15.5 9.8 6.9 2.2 12.6 14.8 2.1 3.1 133 80 E1 1 DH84 669.3 34.7 1.2 568.2 37.4 90.5 11.5 1.2 11.1 9.5 6.8 1.8 12.4 14.2 2.1 2.8 130 81 E1 1 DH85 658.5 32.0 1.1 617.7 31.9 74.4 12.9 1.2 12.3 10.8 6.2 2.4 12.4 14.8 2.4 2.6 133 82 E1 1 DH86 648.8 43.0 1.3 500.2 32.4 92.6 13.3 1.5 15.4 9.2 6.2 1.4 13.9 15.3 2.5 3.1 131 83 E1 1 DH87 435.7 41.7 1.1 383.2 27.8 85.5 14.1 1.3 14.3 12.3 6.6 1.6 14.5 16.1 2.4 2.9 133 84 E1 1 DH89 630.0 34.6 1.1 570.1 32.3 89.7 11.4 1.6 14.1 7.4 6.5 2.5 14.2 16.7 2.6 2.4 134 85 E1 1 DH90 553.3 35.3 1.1 484.9 32.2 82.7 14.7 1.5 17.9 9.5 6.6 2.0 13.1 15.1 2.3 2.7 131 86 E1 1 DH91 668.0 36.0 1.1 604.0 31.5 95.6 14.3 1.6 17.9 9.3 6.6 2.5 14.1 16.6 2.5 2.6 131 87 E1 1 DH92 612.1 37.6 1.2 518.3 32.2 84.9 14.8 1.5 18.1 9.7 6.6 1.7 13.0 14.7 2.2 2.9 132 88 E1 1 DH93 539.2 46.4 1.3 406.0 30.1 76.9 12.3 1.4 13.3 9.1 7.0 1.5 14.6 16.1 2.3 3.2 130 89 E1 1 DH94 698.3 31.1 0.9 759.9 30.9 88.7 10.6 1.3 11.0 8.1 6.8 2.8 13.5 16.3 2.4 2.3 134 90 E1 1 DH95 751.8 35.3 1.3 581.9 36.1 85.9 11.3 1.3 11.5 8.9 7.2 1.8 13.5 15.3 2.1 2.6 130 91 E1 1 DH96 646.4 30.9 1.1 610.4 34.5 92.0 13.6 1.2 13.2 11.3 5.8 2.6 11.0 13.6 2.3 2.8 133 92 E1 1 DH97 515.6 40.8 1.2 418.9 34.6 91.9 11.5 1.5 13.2 7.9 6.8 1.6 14.5 16.1 2.4 2.8 133 93 E1 1 DH98 573.3 41.4 1.4 402.9 34.5 81.1 12.6 1.6 16.2 7.8 7.3 1.0 13.3 14.3 2.0 3.1 130 94 E1 1 DH99 495.0 38.5 1.1 457.9 29.5 80.6 14.5 1.6 18.1 9.2 6.4 1.1 13.7 14.8 2.3 2.8 133 95 E1 1 DH100 624.2 40.8 1.0 611.3 27.9 75.1 11.5 1.4 12.5 8.4 6.7 2.1 14.8 16.9 2.5 2.7 132 96 E1 1 DH101 525.2 44.8 1.3 408.1 29.5 84.1 13.3 1.3 13.4 10.5 7.3 1.0 14.6 15.6 2.1 3.1 130 97 E1 1 DH102 620.0 42.5 1.2 518.4 29.5 89.8 14.5 1.4 16.7 10.0 7.6 2.4 16.0 18.4 2.4 2.7 133 98 E1 1 DH103 461.5 36.1 1.1 436.2 29.7 78.9 13.7 1.7 18.0 8.3 6.6 2.1 13.6 15.7 2.4 2.7 137 99 E1 1 DH104 713.4 41.1 1.2 617.1 27.9 90.3 14.6 1.3 15.7 10.9 6.8 1.9 15.6 17.5 2.6 2.6 136 100 E1 1 DH105 622.2 41.6 1.1 581.5 28.2 81.3 16.4 1.6 20.9 10.3 6.0 2.4 13.5 15.9 2.6 3.1 135 101 E1 1 DH106 448.2 39.5 1.2 372.6 29.8 85.4 14.3 1.6 18.8 8.7 7.0 2.1 15.5 17.6 2.5 2.5 135 102 E1 1 DH107 590.7 37.0 1.1 556.2 . 84.7 11.5 1.4 12.6 8.6 6.8 1.6 13.2 14.8 2.2 2.8 130 103 E1 1 DH108 457.5 33.0 1.0 475.6 29.4 92.2 12.5 1.4 13.7 9.1 6.3 2.4 13.1 15.5 2.5 2.5 132 104 E1 1 DH109 529.5 36.6 1.0 507.7 27.9 80.9 12.8 1.4 14.6 8.9 6.8 2.4 13.5 15.9 2.3 2.7 134 105 E1 1 DH110 361.3 37.3 0.9 393.1 29.4 92.3 13.1 1.5 15.4 9.1 7.7 2.0 14.7 16.7 2.2 2.5 133 106 E1 1 DH111 453.8 46.6 1.3 361.9 28.9 85.5 16.6 1.5 20.0 10.9 7.8 1.8 15.1 16.9 2.2 3.1 133 107 E1 1 DH112 621.1 38.9 1.1 561.6 30.5 87.7 11.7 1.3 11.6 9.5 7.1 2.0 14.3 16.3 2.3 2.7 133 108 E1 1 DH113 557.5 36.8 1.2 446.7 34.2 91.2 12.9 1.4 14.1 9.4 6.2 1.9 12.9 14.8 2.4 2.8 133 109 E1 1 DH114 571.5 36.2 1.1 521.4 31.7 68.0 12.5 1.4 14.4 8.6 7.0 1.3 14.1 15.4 2.2 2.6 133 110 E1 1 DH115 525.9 31.8 0.9 567.3 29.3 87.6 12.8 1.4 14.5 9.2 6.7 2.5 13.0 15.5 2.3 2.4 134 111 E1 1 DH116 525.5 31.4 0.9 605.4 30.8 86.5 12.1 1.4 13.7 8.5 6.5 2.9 12.3 15.2 2.3 2.6 133 112 E1 1 DH117 547.8 32.3 1.0 553.9 33.7 78.8 13.3 1.8 18.4 7.7 6.5 1.5 13.0 14.5 2.2 2.5 133 113 E1 1 DH119 588.6 44.1 1.5 392.1 32.5 92.2 16.7 1.5 19.3 12.9 7.6 2.4 15.3 17.7 2.3 2.9 133 114 E1 1 DH120 650.0 32.7 0.9 689.3 31.7 89.0 14.0 1.4 15.1 10.2 6.4 2.5 13.1 15.6 2.5 2.5 135 115 E1 1 DH121 621.1 36.9 1.2 537.8 29.9 81.5 13.3 1.4 14.5 9.7 6.9 3.0 14.4 17.4 2.5 2.6 137 116 E1 1 DH122 540.4 44.8 0.9 571.2 27.8 88.3 14.7 1.6 18.8 9.2 7.2 1.5 16.0 17.5 2.4 2.8 134 117 E1 1 DH123 405.6 44.0 1.2 339.7 26.8 96.0 13.3 1.2 12.2 11.9 7.7 1.7 15.5 17.2 2.2 2.8 135 118 E1 1 DH124 703.4 37.4 1.2 591.6 30.5 83.2 15.5 1.5 18.0 10.7 7.2 2.2 13.6 15.8 2.2 2.7 134 119 E1 1 DH125 679.9 37.8 1.1 597.5 30.0 79.0 14.4 1.7 18.9 8.7 6.4 1.6 13.3 14.9 2.3 2.8 133 120 E1 1 DH126 488.5 45.8 1.4 357.6 29.6 76.7 12.8 1.5 14.9 8.7 7.5 1.0 16.5 17.5 2.3 2.8 136 121 E1 1 DH128 478.5 42.0 1.1 428.4 27.0 93.1 17.7 1.5 20.5 12.5 6.9 2.2 15.5 17.7 2.6 2.7 136 122 E1 1 DH129 668.1 43.0 1.3 527.3 32.6 91.5 13.2 1.6 16.2 8.7 6.9 2.1 13.6 15.7 2.3 3.2 133 123 E1 1 DH130 620.1 35.3 1.0 599.7 30.0 85.2 13.9 1.5 16.7 9.2 6.8 2.3 13.4 15.7 2.3 2.6 134 124 E1 1 DH131 396.3 38.9 1.3 314.3 33.6 88.0 13.7 1.6 17.7 8.4 7.4 2.5 15.5 18.0 2.4 2.5 137 163 Table E.1 Continued. No. Env. Rep. Name GYLD GPS GWPS SPSM TGW PHT FLL FLW FLA FLS SL SSN FSN TSN SC GSP HD 125 E1 2 DH1 581.7 34.8 1.0 567.5 30.3 84.8 11.4 1.2 10.9 9.5 6.0 1.3 12.3 13.6 2.3 2.8 132 126 E1 2 DH3 428.9 38.1 1.0 409.2 27.5 88.8 12.5 1.4 14.0 8.9 6.4 2.5 13.5 16.0 2.5 2.8 136 127 E1 2 DH4 525.7 36.5 1.2 433.1 32.9 78.7 11.3 1.6 14.2 7.1 7.0 2.0 15.0 17.0 2.4 2.4 133 128 E1 2 DH5 359.3 36.6 0.9 390.1 29.9 80.9 13.9 1.5 16.9 9.0 7.0 2.1 13.3 15.4 2.2 2.8 135 129 E1 2 DH6 559.5 40.3 1.2 460.5 31.4 86.7 13.8 1.4 15.1 10.0 6.8 1.2 13.8 15.0 2.2 2.9 130 130 E1 2 DH7 535.0 35.9 1.1 505.2 31.5 85.1 12.7 1.5 15.0 8.5 6.6 3.3 14.3 17.6 2.7 2.5 136 131 E1 2 DH8 564.3 50.1 1.5 368.1 30.8 91.9 14.7 1.4 16.6 10.3 7.4 1.4 15.5 16.9 2.3 3.2 132 132 E1 2 DH9 484.1 43.7 1.4 338.3 31.0 86.1 14.4 1.5 17.5 9.4 7.7 2.2 15.3 17.5 2.3 2.9 135 133 E1 2 DH11 668.8 43.9 1.5 446.8 35.4 99.3 16.6 1.5 19.2 11.3 8.0 1.6 13.9 15.5 1.9 3.2 131 134 E1 2 DH12 311.6 53.4 1.7 182.9 32.9 84.5 14.2 1.6 17.9 9.0 8.1 1.2 17.7 18.9 2.3 3.0 134 135 E1 2 DH13 515.0 45.5 1.4 378.1 28.5 92.2 15.9 1.5 18.3 11.0 7.0 2.4 15.5 17.9 2.6 2.9 136 136 E1 2 DH14 634.0 32.4 1.1 590.9 32.4 89.4 14.1 1.6 17.6 8.9 6.6 3.2 13.5 16.7 2.5 2.4 135 137 E1 2 DH15 647.9 37.2 1.2 553.3 31.9 82.8 13.6 1.4 15.4 9.5 7.2 1.7 14.2 15.9 2.2 2.6 130 138 E1 2 DH16 589.3 41.6 1.2 493.5 30.8 82.6 12.8 1.4 14.2 9.1 6.2 1.6 13.5 15.1 2.4 3.1 132 139 E1 2 DH17 535.7 45.9 1.5 349.0 34.4 88.8 16.3 1.6 20.9 10.1 8.0 1.4 15.3 16.7 2.1 3.0 130 140 E1 2 DH18 391.5 43.4 1.4 289.6 32.1 85.2 12.2 1.6 15.7 7.5 7.5 1.4 16.5 17.9 2.4 2.6 135 141 E1 2 DH19 372.0 38.5 1.3 286.6 32.1 87.4 12.8 1.5 15.2 8.5 6.6 1.5 13.6 15.1 2.3 2.8 132 142 E1 2 DH20 633.8 38.0 1.3 490.2 29.7 81.8 13.8 1.6 17.6 8.6 7.0 2.0 14.2 16.2 2.3 2.7 134 143 E1 2 DH21 642.3 36.8 1.2 523.0 33.6 90.4 14.3 1.5 16.8 9.7 7.0 2.7 12.9 15.6 2.2 2.8 135 144 E1 2 DH22 510.4 48.9 1.5 347.9 32.5 93.7 13.8 1.5 16.2 9.3 7.2 1.4 15.9 17.3 2.4 3.1 135 145 E1 2 DH23 640.2 45.4 1.4 466.6 31.6 93.7 14.9 1.4 16.3 10.9 6.4 1.1 13.5 14.6 2.3 3.4 130 146 E1 2 DH24 762.1 42.2 1.3 587.1 31.9 88.7 14.7 1.6 19.1 9.0 6.8 1.1 13.9 15.0 2.2 3.0 132 147 E1 2 DH25 662.4 48.3 1.3 508.7 34.2 84.3 14.0 1.5 16.6 9.3 7.8 1.6 15.7 17.3 2.2 3.1 132 148 E1 2 DH26 494.2 38.1 1.1 444.0 32.3 78.0 12.1 1.4 13.3 8.7 6.9 1.6 14.4 16.0 2.3 2.6 133 149 E1 2 DH27 575.2 39.9 1.2 483.8 28.3 92.9 13.5 1.5 15.9 9.1 6.4 2.3 13.4 15.7 2.5 3.0 136 150 E1 2 DH28 588.7 38.7 1.2 493.5 33.0 91.5 15.4 1.6 19.2 9.8 6.3 2.4 13.7 16.1 2.5 2.8 134 151 E1 2 DH29 402.3 37.1 1.2 348.9 31.9 75.9 14.0 1.4 15.5 10.0 7.5 2.2 14.9 17.1 2.3 2.5 133 152 E1 2 DH30 566.5 42.2 1.3 448.2 35.4 93.3 13.9 1.6 17.7 8.6 6.7 2.4 15.2 17.6 2.6 2.8 133 153 E1 2 DH31 466.8 46.8 1.4 340.2 29.1 83.0 12.5 1.5 15.2 8.2 7.6 1.5 15.1 16.6 2.2 3.1 134 154 E1 2 DH32 575.6 37.6 1.0 568.8 27.2 80.2 11.1 1.5 13.5 7.2 7.2 2.0 15.8 17.8 2.5 2.4 138 155 E1 2 DH33 436.3 48.7 1.1 391.7 29.7 90.4 17.1 1.7 23.8 9.8 7.2 2.0 16.2 18.2 2.5 3.0 136 156 E1 2 DH34 637.0 46.8 1.5 425.8 33.5 97.4 14.5 1.5 17.4 9.5 7.2 1.5 15.4 16.9 2.3 3.0 130 157 E1 2 DH35 527.6 36.1 1.2 438.9 34.0 83.5 10.9 1.5 12.9 7.3 7.0 2.2 13.6 15.8 2.3 2.6 134 158 E1 2 DH36 492.1 49.9 1.7 294.3 31.9 83.0 14.6 1.7 19.7 8.5 8.6 1.0 17.5 18.5 2.2 2.8 134 159 E1 2 DH37 555.4 33.6 1.1 516.6 30.5 84.9 16.0 1.5 19.3 10.5 6.9 2.1 13.6 15.7 2.3 2.5 134 160 E1 2 DH38 669.5 43.7 1.4 479.6 31.4 80.4 15.5 1.6 19.0 10.1 7.2 1.0 14.6 15.6 2.2 3.0 130 161 E1 2 DH39 504.3 37.2 1.1 460.2 31.0 89.0 11.7 1.3 12.4 8.8 6.2 2.5 14.8 17.3 2.8 2.5 135 162 E1 2 DH40 497.3 42.8 1.4 351.9 32.9 104.3 13.5 1.3 13.6 10.6 8.4 0.6 16.4 16.9 2.0 2.6 133 163 E1 2 DH41 666.8 41.2 1.2 573.8 30.0 87.7 13.3 1.4 14.7 9.7 7.0 1.1 14.6 15.7 2.2 2.8 135 164 E1 2 DH42 440.2 41.0 1.3 340.2 30.5 87.3 14.7 1.3 15.4 11.3 7.0 1.7 15.6 17.3 2.5 2.6 136 165 E1 2 DH43 622.4 41.6 1.4 458.3 31.8 84.5 12.8 1.4 14.6 8.9 7.0 1.7 14.0 15.7 2.3 3.0 134 166 E1 2 DH44 570.1 41.7 1.4 397.8 34.9 95.4 15.4 1.5 18.3 10.2 7.9 1.7 15.0 16.7 2.1 2.8 133 167 E1 2 DH45 598.4 36.4 1.3 450.9 35.8 89.1 11.4 1.4 12.7 8.1 7.4 2.0 14.4 16.4 2.2 2.5 132 168 E1 2 DH46 340.1 43.0 1.2 277.4 31.4 89.5 17.2 1.7 22.5 10.4 8.2 1.7 14.7 16.4 2.0 2.9 135 169 E1 2 DH47 807.0 36.5 1.2 676.4 32.7 91.7 13.6 1.6 17.3 8.5 7.2 1.5 14.0 15.5 2.2 2.6 133 170 E1 2 DH49 1091.6 48.2 1.6 699.7 32.0 92.8 15.1 1.5 18.5 9.9 7.9 1.9 15.1 17.0 2.2 3.2 134 171 E1 2 DH50 712.9 41.9 1.5 490.0 35.6 96.4 14.3 1.6 17.8 9.1 7.1 1.5 14.1 15.6 2.2 3.0 130 172 E1 2 DH51 480.2 41.2 1.0 461.7 28.8 88.5 15.5 1.5 18.4 10.3 7.2 2.5 13.9 16.4 2.3 3.0 134 173 E1 2 DH52 664.6 39.3 1.2 547.4 29.0 80.9 12.9 1.2 12.8 10.3 6.8 1.2 14.5 15.7 2.3 2.7 132 174 E1 2 DH53 786.5 30.3 1.0 816.8 30.3 73.5 10.5 1.3 11.1 8.0 6.4 2.4 14.7 17.1 2.7 2.1 134 175 E1 2 DH54 368.5 45.5 1.5 244.7 32.7 83.0 16.5 1.6 20.3 10.6 7.7 1.8 14.4 16.2 2.1 3.2 137 176 E1 2 DH55 620.6 35.4 1.0 593.9 28.7 91.8 11.3 1.4 12.3 8.3 6.1 2.6 12.9 15.5 2.5 2.7 134 177 E1 2 DH56 723.0 31.7 1.1 657.9 31.7 90.2 12.3 1.4 13.9 8.7 6.0 2.3 12.5 14.8 2.5 2.5 133 178 E1 2 DH57 508.5 31.8 0.9 535.8 29.3 84.7 14.0 1.5 16.2 9.6 5.8 2.0 13.3 15.3 2.6 2.4 136 179 E1 2 DH58 439.9 43.9 1.2 378.3 29.5 84.7 12.0 1.3 12.8 8.9 6.9 1.8 13.1 14.9 2.2 3.3 133 180 E1 2 DH59 858.9 43.7 1.4 599.0 32.7 83.2 14.3 1.4 15.6 10.3 6.8 1.0 14.2 15.2 2.3 3.1 130 181 E1 2 DH60 659.4 46.4 1.5 447.0 31.9 83.5 14.8 1.3 15.6 11.3 7.2 1.5 14.0 15.5 2.1 3.3 132 182 E1 2 DH61 583.5 35.6 1.1 522.8 31.5 84.8 12.8 1.6 16.3 8.1 6.9 1.4 14.2 15.6 2.3 2.5 136 183 E1 2 DH62 540.5 40.6 1.2 462.4 29.8 87.0 14.2 1.4 15.6 10.3 6.4 1.1 13.3 14.4 2.2 3.1 132 184 E1 2 DH63 631.4 48.3 1.6 398.1 33.2 87.0 15.6 1.6 19.4 10.0 7.8 1.4 15.1 16.5 2.1 3.2 131 185 E1 2 DH64 480.0 43.0 1.6 306.9 36.6 96.1 13.3 1.4 15.1 9.2 7.6 1.2 14.6 15.8 2.1 2.9 132 186 E1 2 DH65 519.6 50.3 1.5 342.7 30.8 85.4 15.9 1.4 17.9 11.1 7.6 1.0 15.5 16.5 2.2 3.2 130 164 Table E.1 Continued. No. Env. Rep. Name GYLD GPS GWPS SPSM TGW PHT FLL FLW FLA FLS SL SSN FSN TSN SC GSP HD 187 E1 2 DH66 396.9 45.6 1.6 245.6 33.8 88.3 13.5 1.7 18.6 7.8 7.9 2.5 16.5 19.0 2.4 2.8 136 188 E1 2 DH67 628.9 36.6 1.2 518.4 32.8 83.1 12.8 1.4 14.4 9.0 7.0 1.6 14.4 16.0 2.3 2.6 131 189 E1 2 DH68 454.5 37.9 1.3 358.4 27.5 85.2 16.3 1.7 21.7 9.8 7.1 2.4 13.6 16.0 2.3 2.8 133 190 E1 2 DH69 487.0 37.4 0.9 532.2 30.1 83.3 12.1 1.2 12.1 9.8 6.0 1.7 12.9 14.6 2.4 2.9 133 191 E1 2 DH70 505.3 42.4 1.5 333.8 32.5 90.7 13.9 1.4 15.6 9.7 7.8 1.7 15.5 17.2 2.2 2.7 134 192 E1 2 DH71 625.7 45.9 1.4 437.9 30.5 90.7 13.4 1.6 16.7 8.6 8.0 1.1 16.3 17.4 2.2 2.8 134 193 E1 2 DH72 564.8 42.7 1.4 417.1 30.9 88.7 13.1 1.5 15.3 8.8 7.4 1.7 16.1 17.8 2.4 2.6 135 194 E1 2 DH73 549.0 43.0 1.3 413.4 29.6 90.0 11.7 1.4 13.4 8.1 7.2 1.7 15.5 17.2 2.4 2.8 136 195 E1 2 DH75 655.8 39.9 1.5 446.7 35.9 89.6 13.4 1.5 16.2 8.9 7.5 1.8 14.2 16.0 2.1 2.8 130 196 E1 2 DH76 721.4 35.4 1.1 637.8 32.8 79.0 13.2 1.4 14.3 9.7 6.4 2.0 12.8 14.8 2.3 2.8 134 197 E1 2 DH77 601.8 30.5 1.1 570.4 33.7 77.7 14.3 1.3 14.5 11.2 7.5 2.6 14.9 17.5 2.3 2.0 133 198 E1 2 DH78 608.0 47.6 1.4 445.4 30.5 89.0 13.8 1.4 15.1 10.0 7.5 1.2 16.6 17.8 2.4 2.9 133 199 E1 2 DH79 644.7 45.2 1.3 511.3 31.8 92.4 13.2 1.4 14.4 9.7 7.1 2.2 14.2 16.4 2.3 3.2 133 200 E1 2 DH80 611.0 46.4 1.4 443.4 30.7 91.0 15.1 1.4 16.5 11.1 6.9 1.6 16.2 17.8 2.6 2.9 135 201 E1 2 DH81 418.2 41.4 1.4 303.7 33.2 87.3 11.8 1.4 13.1 8.5 6.8 1.3 15.8 17.1 2.5 2.6 132 202 E1 2 DH82 563.9 45.0 1.6 357.6 32.8 85.1 9.8 1.4 10.9 7.1 7.8 2.3 16.7 19.0 2.4 2.7 133 203 E1 2 DH83 764.8 38.0 1.3 569.0 35.5 100.6 14.8 1.6 18.4 9.5 7.0 2.3 13.1 15.4 2.2 2.9 133 204 E1 2 DH84 834.4 32.8 1.2 694.1 37.4 87.7 10.2 1.2 9.6 8.6 6.9 2.1 12.6 14.7 2.1 2.6 130 205 E1 2 DH85 738.8 28.6 0.9 863.0 32.4 82.6 14.3 1.5 16.7 9.7 6.0 2.4 12.3 14.7 2.5 2.3 134 206 E1 2 DH86 607.3 53.8 1.7 352.1 32.6 94.7 12.1 1.4 13.1 8.9 6.9 0.7 15.1 15.8 2.3 3.6 132 207 E1 2 DH87 581.1 54.4 1.6 367.8 29.7 88.7 13.2 1.2 12.7 10.9 7.4 0.4 16.3 16.7 2.3 3.3 131 208 E1 2 DH89 624.2 35.2 1.2 520.6 33.9 88.4 12.7 1.5 15.1 8.4 6.5 2.4 13.7 16.1 2.5 2.6 133 209 E1 2 DH90 735.5 33.4 1.0 746.0 34.1 87.6 13.4 1.6 16.9 8.5 6.5 2.0 13.6 15.6 2.4 2.5 130 210 E1 2 DH91 607.4 36.3 1.2 525.9 33.1 95.8 13.7 1.4 15.1 9.8 6.4 2.1 13.0 15.1 2.4 2.8 133 211 E1 2 DH92 450.4 44.1 1.3 357.8 32.9 81.9 15.0 1.5 17.8 10.1 7.1 1.3 14.3 15.6 2.2 3.1 133 212 E1 2 DH93 484.6 43.9 1.2 391.8 30.2 83.6 12.6 1.3 13.2 9.4 7.0 1.5 14.9 16.4 2.3 3.0 130 213 E1 2 DH94 580.9 34.1 1.1 543.9 30.4 91.6 10.6 1.3 11.3 7.8 7.1 2.2 14.7 16.9 2.4 2.3 133 214 E1 2 DH95 528.3 32.9 1.2 452.7 36.3 86.7 14.1 1.5 17.1 9.1 7.2 2.3 13.3 15.6 2.2 2.5 130 215 E1 2 DH96 828.3 38.8 1.4 603.7 37.3 91.2 13.3 1.4 15.1 9.3 6.4 1.9 12.6 14.5 2.3 3.1 133 216 E1 2 DH97 476.8 47.3 1.6 300.6 34.0 88.6 10.9 1.5 13.1 7.2 7.1 1.2 15.2 16.4 2.3 3.1 133 217 E1 2 DH98 566.6 46.1 1.6 351.3 36.0 83.3 12.2 1.6 15.3 7.7 7.6 0.9 14.7 15.6 2.1 3.1 130 218 E1 2 DH99 438.9 44.2 1.3 350.0 29.6 86.7 13.3 1.6 16.6 8.4 7.1 1.4 14.9 16.3 2.3 3.0 135 219 E1 2 DH100 438.7 . . . 28.6 74.8 11.4 1.3 12.3 8.5 . . . . . . 133 220 E1 2 DH101 645.3 42.7 1.3 480.5 31.4 92.8 12.2 1.5 14.5 8.1 7.2 1.1 14.5 15.6 2.2 2.9 132 221 E1 2 DH102 506.9 50.8 1.4 349.8 28.9 90.6 13.8 1.4 15.3 9.9 8.3 1.5 16.6 18.1 2.2 3.1 134 222 E1 2 DH103 416.2 36.4 1.1 367.7 30.8 85.4 12.8 1.6 15.9 8.1 6.6 2.3 13.1 15.4 2.3 2.8 136 223 E1 2 DH104 414.0 36.9 1.0 412.8 27.9 89.6 15.0 1.3 15.9 11.2 6.8 2.0 15.2 17.2 2.5 2.4 135 224 E1 2 DH105 543.5 41.6 1.1 495.9 27.9 81.1 14.2 1.4 16.1 9.9 6.2 2.6 14.4 17.0 2.8 2.9 134 225 E1 2 DH106 579.9 45.2 1.4 407.8 30.6 89.7 11.9 1.6 14.7 7.6 7.3 1.7 15.6 17.3 2.4 2.9 133 226 E1 2 DH107 603.5 39.9 1.2 492.6 31.7 90.8 13.0 1.3 13.6 9.9 7.0 1.5 13.4 14.9 2.1 3.0 132 227 E1 2 DH108 494.4 40.8 1.2 407.6 29.9 90.8 11.9 1.4 13.1 8.6 7.0 2.0 14.9 16.9 2.4 2.7 133 228 E1 2 DH109 595.6 39.3 1.2 496.0 30.1 79.7 14.0 1.4 16.0 9.7 7.1 2.3 13.6 15.9 2.2 2.9 133 229 E1 2 DH110 473.3 46.5 1.4 339.5 31.8 93.8 13.2 1.3 13.8 10.0 8.2 1.5 15.7 17.2 2.1 3.0 132 230 E1 2 DH111 495.5 54.8 1.7 294.1 30.2 89.4 15.7 1.4 17.4 11.3 8.7 1.0 16.9 17.9 2.1 3.2 133 231 E1 2 DH112 504.1 46.6 1.4 361.3 31.2 89.8 12.1 1.4 13.7 8.5 7.7 1.6 15.1 16.7 2.2 3.1 132 232 E1 2 DH113 555.9 39.6 1.3 427.9 34.7 95.2 12.0 1.3 12.1 9.4 6.7 1.7 14.1 15.8 2.3 2.8 132 233 E1 2 DH114 624.2 38.5 1.3 486.5 32.0 72.1 12.7 1.5 15.5 8.2 7.4 1.1 15.5 16.6 2.2 2.5 133 234 E1 2 DH115 509.0 32.7 1.0 503.4 31.6 93.7 10.8 1.3 11.2 8.2 6.8 2.1 12.9 15.0 2.2 2.5 133 235 E1 2 DH116 549.9 31.0 1.0 537.5 34.4 94.0 11.0 1.5 12.8 7.5 6.3 2.7 12.2 14.9 2.4 2.5 131 236 E1 2 DH117 552.4 35.2 1.2 452.0 34.8 79.5 12.6 1.4 14.0 9.0 6.9 1.4 13.5 14.9 2.2 2.6 133 237 E1 2 DH119 502.3 46.4 1.5 333.3 34.3 98.7 15.0 1.6 19.5 9.2 7.6 2.1 15.4 17.5 2.3 3.0 136 238 E1 2 DH120 399.1 36.8 1.1 365.1 28.3 83.9 13.1 1.5 15.1 9.0 6.7 2.2 14.5 16.7 2.5 2.5 135 239 E1 2 DH121 577.2 36.8 1.2 501.9 31.2 83.3 12.9 1.4 14.5 9.0 7.0 2.3 15.1 17.4 2.5 2.4 135 240 E1 2 DH122 347.9 49.3 1.4 242.8 28.7 90.6 14.5 1.4 16.2 10.3 7.5 1.1 16.4 17.5 2.3 3.0 135 241 E1 2 DH123 268.7 36.6 0.9 312.4 . 86.3 14.6 1.3 15.0 11.2 7.0 2.3 13.9 16.2 2.3 2.6 136 242 E1 2 DH124 515.3 33.0 0.9 548.8 29.9 87.3 14.1 1.5 16.4 9.7 7.0 2.2 13.0 15.2 2.2 2.5 135 243 E1 2 DH125 755.9 40.6 1.5 511.5 31.9 83.5 12.4 1.7 16.6 7.4 7.1 1.9 15.0 16.9 2.4 2.7 134 244 E1 2 DH126 396.6 51.5 1.5 265.6 30.9 76.8 12.3 1.6 15.2 7.9 7.9 0.8 17.2 18.0 2.3 3.0 135 245 E1 2 DH128 357.2 45.5 1.2 292.5 26.9 90.4 19.2 1.4 21.0 14.0 7.0 1.9 15.8 17.7 2.5 2.9 137 246 E1 2 DH129 643.8 42.6 1.4 472.7 32.2 91.3 12.7 1.4 13.9 9.2 6.9 1.8 13.4 15.2 2.2 3.2 132 247 E1 2 DH130 615.2 46.7 1.4 432.3 27.3 82.2 12.6 1.4 14.5 8.7 7.5 1.4 14.8 16.2 2.2 3.2 134 248 E1 2 DH131 442.8 38.4 1.3 347.1 34.4 85.7 13.3 1.6 17.1 8.1 6.6 1.7 14.0 15.7 2.4 2.7 136 165 Table E.1 Continued. No. Env. Rep. Name GYLD GPS GWPS SPSM TGW PHT FLL FLW FLA FLS SL SSN FSN TSN SC GSP HD 249 E2 1 DH1 938.1 29.8 0.8 1175.6 26.8 94.3 14.6 1.3 15.5 10.9 6.7 2.3 14.3 16.6 2.5 1.8 141 250 E2 1 DH3 782.8 36.8 1.1 734.3 29.1 92.8 13.6 1.5 15.7 9.3 6.9 1.9 15.2 17.1 2.5 2.2 141 251 E2 1 DH4 798.0 32.7 0.9 866.5 30.3 84.6 13.8 1.5 16.3 9.3 7.2 2.7 16.0 18.7 2.6 1.7 143 252 E2 1 DH5 702.9 36.0 0.8 863.5 26.6 78.7 16.3 1.6 21.1 10.1 7.6 1.9 14.5 16.4 2.2 2.2 143 253 E2 1 DH6 885.5 36.0 0.9 936.1 30.3 97.6 16.4 1.4 17.9 11.9 7.3 2.2 15.5 17.7 2.4 2.0 142 254 E2 1 DH7 865.4 35.6 1.0 866.3 32.0 87.7 14.8 1.5 17.6 9.8 6.9 2.7 15.1 17.8 2.6 2.0 143 255 E2 1 DH8 888.0 38.6 1.1 805.0 30.3 99.8 16.4 1.4 18.7 11.4 7.5 2.3 15.2 17.5 2.3 2.2 140 256 E2 1 DH9 761.4 38.0 1.0 770.7 31.9 87.6 16.1 1.6 20.7 9.9 7.7 3.1 16.5 19.6 2.6 1.9 144 257 E2 1 DH11 817.9 38.4 1.3 651.7 32.3 107.9 15.1 1.3 15.1 11.9 8.3 1.7 15.8 17.5 2.1 2.2 142 258 E2 1 DH12 670.8 35.7 1.0 690.9 29.2 72.9 15.0 1.7 20.3 9.1 6.8 1.8 15.1 16.9 2.5 2.1 143 259 E2 1 DH13 807.1 35.6 0.9 899.8 26.9 89.7 18.9 1.4 21.6 13.1 7.5 2.8 17.0 19.8 2.7 1.8 142 260 E2 1 DH14 823.6 28.8 0.9 898.1 31.5 87.9 16.9 1.4 18.4 12.5 7.1 2.8 15.1 17.9 2.5 1.6 143 261 E2 1 DH15 900.7 29.1 0.8 1120.2 28.9 90.5 16.2 1.4 18.4 11.4 7.0 2.2 14.2 16.4 2.4 1.8 141 262 E2 1 DH16 752.6 36.7 1.0 739.3 28.3 89.3 14.0 1.5 16.6 9.3 7.0 1.7 14.7 16.4 2.4 2.2 142 263 E2 1 DH17 839.2 36.1 1.1 768.5 32.7 89.6 17.8 1.5 20.5 12.2 7.4 2.1 14.4 16.5 2.2 2.2 140 264 E2 1 DH18 793.3 39.1 1.1 730.5 31.1 81.6 17.6 1.9 26.6 9.3 7.6 1.7 15.8 17.5 2.3 2.2 145 265 E2 1 DH19 822.2 31.6 1.0 855.6 32.3 92.6 14.3 1.4 15.8 10.3 6.6 2.2 13.3 15.5 2.3 2.0 140 266 E2 1 DH20 810.2 40.0 1.0 775.3 . 88.0 15.3 1.6 19.4 9.5 7.1 2.5 15.7 18.2 2.6 2.2 144 267 E2 1 DH21 836.3 33.1 1.0 812.7 32.9 91.6 18.1 1.5 20.8 12.5 7.4 3.1 14.6 17.7 2.4 1.9 143 268 E2 1 DH22 849.6 34.1 0.9 923.4 31.8 97.1 16.7 1.5 19.6 11.2 7.6 3.0 15.9 18.9 2.5 1.8 143 269 E2 1 DH23 884.2 33.3 0.9 963.2 28.1 92.5 18.7 1.4 20.1 13.7 6.3 2.4 13.7 16.1 2.5 2.1 141 270 E2 1 DH24 909.7 33.9 0.9 1042.0 28.4 95.3 15.6 1.6 20.0 9.6 6.7 2.1 14.2 16.3 2.4 2.1 141 271 E2 1 DH25 836.0 34.1 0.9 973.3 30.5 83.2 16.0 1.3 17.0 11.9 7.3 2.5 15.2 17.7 2.4 1.9 139 272 E2 1 DH26 862.1 34.5 0.9 956.8 29.1 85.7 17.7 1.4 20.2 12.3 7.2 2.1 15.4 17.5 2.5 2.0 142 273 E2 1 DH27 975.6 30.8 0.9 1142.4 28.9 99.2 17.7 1.4 19.4 12.9 6.2 2.4 13.7 16.1 2.6 1.9 144 274 E2 1 DH28 895.4 36.8 1.0 927.9 29.7 96.9 15.4 1.4 17.0 11.1 7.1 2.2 15.5 17.7 2.5 2.1 143 275 E2 1 DH29 835.9 34.9 0.9 897.9 30.0 79.9 15.3 1.4 17.0 10.9 7.4 2.3 15.1 17.4 2.4 2.0 143 276 E2 1 DH30 876.8 31.6 1.1 780.8 34.1 86.5 18.3 1.4 20.8 12.7 7.0 2.2 15.9 18.1 2.6 1.7 142 277 E2 1 DH31 679.8 32.5 0.7 921.1 26.7 87.2 14.6 1.4 16.7 10.1 7.3 2.5 14.4 16.9 2.3 1.9 141 278 E2 1 DH32 780.3 30.8 0.8 1005.6 26.4 86.9 12.2 1.4 13.5 8.8 6.5 2.9 14.7 17.6 2.7 1.7 145 279 E2 1 DH33 644.6 41.5 0.8 799.7 26.9 96.2 15.8 1.5 19.4 10.3 7.3 3.1 17.1 20.2 2.8 2.0 147 280 E2 1 DH34 837.0 37.3 1.0 798.7 28.8 100.2 16.3 1.4 18.2 11.5 7.6 2.5 15.7 18.2 2.4 2.0 141 281 E2 1 DH35 867.5 25.7 0.8 1135.5 30.9 91.9 15.2 1.5 18.2 10.0 7.1 3.3 13.6 16.9 2.4 1.5 141 282 E2 1 DH36 862.0 33.4 0.9 909.5 29.3 83.5 18.9 1.7 25.7 11.0 7.8 2.5 15.6 18.1 2.3 1.7 142 283 E2 1 DH37 693.3 33.2 0.8 855.9 26.4 90.2 16.0 1.3 16.4 12.4 7.4 2.8 15.7 18.5 2.5 1.8 141 284 E2 1 DH38 805.7 34.2 0.9 883.4 27.3 86.5 16.3 1.4 18.6 11.3 7.2 2.2 15.6 17.8 2.5 1.9 141 285 E2 1 DH39 932.9 36.3 1.1 882.6 29.4 87.4 15.5 1.3 15.7 12.2 6.4 2.3 15.3 17.6 2.7 2.1 140 286 E2 1 DH40 760.5 33.5 1.0 780.0 30.6 100.8 14.9 1.3 15.3 11.5 7.8 2.0 15.8 17.8 2.3 1.9 142 287 E2 1 DH41 873.5 31.3 0.9 922.4 31.6 89.9 16.9 1.4 19.5 11.6 7.1 2.7 15.4 18.1 2.6 1.7 144 288 E2 1 DH42 905.8 35.3 0.9 1031.7 29.7 90.3 18.9 1.4 20.9 13.5 7.5 3.0 16.5 19.5 2.6 1.8 143 289 E2 1 DH43 934.0 36.6 1.1 879.5 32.0 90.6 16.3 1.4 18.5 11.4 7.3 2.5 14.5 17.0 2.3 2.2 142 290 E2 1 DH44 940.9 30.6 0.9 999.9 33.0 109.3 16.5 1.5 20.2 10.7 7.4 3.2 13.9 17.1 2.3 1.8 145 291 E2 1 DH45 927.1 32.1 1.1 868.8 33.2 97.8 14.8 1.5 18.0 9.7 7.8 2.4 15.6 18.0 2.3 1.8 143 292 E2 1 DH46 722.9 31.8 0.6 1147.5 28.8 94.3 17.8 1.5 21.8 11.5 7.7 3.5 14.7 18.2 2.4 1.7 145 293 E2 1 DH47 831.5 34.4 1.0 835.7 31.0 92.8 14.2 1.4 15.7 10.1 7.1 2.4 14.8 17.2 2.4 2.0 142 294 E2 1 DH49 774.5 39.3 1.0 771.5 30.8 91.6 18.7 1.7 25.3 10.9 7.5 2.7 15.3 18.0 2.4 2.2 145 295 E2 1 DH50 889.9 30.6 0.9 989.8 30.3 104.4 14.0 1.4 15.2 10.2 6.9 2.3 15.1 17.4 2.5 1.8 143 296 E2 1 DH51 725.8 38.1 0.8 917.6 26.4 91.4 17.7 1.4 20.2 12.3 7.6 2.8 15.8 18.6 2.4 2.0 144 297 E2 1 DH52 862.1 34.0 0.8 1123.9 25.7 83.0 14.0 1.2 13.7 11.3 6.8 2.1 14.7 16.8 2.5 2.0 141 298 E2 1 DH53 914.3 37.8 1.1 860.9 26.9 76.6 16.6 1.5 19.6 11.2 7.3 1.6 16.2 17.8 2.5 2.1 142 299 E2 1 DH54 881.7 37.6 1.2 723.9 31.9 95.4 19.5 1.6 25.5 11.8 8.6 1.8 15.9 17.7 2.1 2.1 142 300 E2 1 DH55 801.1 34.7 0.9 855.8 26.7 92.2 15.0 1.5 17.4 10.3 6.5 2.3 14.4 16.7 2.6 2.1 139 301 E2 1 DH56 959.7 29.0 0.9 1026.4 32.2 87.3 17.7 1.4 20.2 12.2 6.6 2.6 14.0 16.6 2.5 1.7 143 302 E2 1 DH57 892.0 34.0 1.0 938.9 27.2 93.8 15.6 1.5 18.4 10.4 6.6 2.3 15.7 18.0 2.7 1.9 142 303 E2 1 DH58 683.4 30.5 0.8 898.0 30.5 94.3 13.8 1.4 14.8 10.2 6.4 2.6 12.7 15.3 2.4 2.0 141 304 E2 1 DH59 910.8 37.1 0.9 961.8 26.6 91.7 13.6 1.2 13.4 11.0 6.8 1.7 15.0 16.7 2.5 2.2 140 305 E2 1 DH60 983.9 40.6 1.1 903.4 30.0 90.5 16.4 1.4 18.3 11.6 7.7 2.2 15.1 17.3 2.3 2.3 141 306 E2 1 DH61 870.6 32.5 0.8 1151.6 29.1 89.7 13.5 1.5 15.5 9.3 7.5 1.8 15.6 17.4 2.3 1.9 144 307 E2 1 DH62 838.3 38.7 1.1 753.8 28.2 92.0 17.3 1.4 19.4 12.2 6.8 1.1 14.7 15.8 2.3 2.5 140 308 E2 1 DH63 885.1 40.7 1.2 764.3 29.6 84.0 20.6 1.7 27.7 12.2 8.2 2.4 15.7 18.1 2.2 2.3 142 309 E2 1 DH64 857.5 37.9 1.2 719.4 34.3 100.2 15.6 1.5 17.9 10.7 7.8 2.1 15.2 17.3 2.2 2.2 141 310 E2 1 DH65 826.8 38.9 1.0 863.9 26.8 87.8 18.6 1.3 19.6 14.0 7.9 2.2 17.2 19.4 2.5 2.0 142 166 Table E.1 Continued. No. Env. Rep. Name GYLD GPS GWPS SPSM TGW PHT FLL FLW FLA FLS SL SSN FSN TSN SC GSP HD 311 E2 1 DH66 740.2 34.8 1.0 746.9 34.3 92.6 16.5 1.7 22.2 9.7 7.9 3.4 15.8 19.2 2.4 1.8 145 312 E2 1 DH67 676.6 31.0 0.8 890.2 28.0 85.9 16.4 1.4 18.3 11.6 6.9 2.6 14.6 17.2 2.5 1.8 139 313 E2 1 DH68 600.8 33.7 0.8 717.8 26.2 77.4 15.8 1.5 18.8 10.6 7.3 1.5 15.0 16.5 2.3 2.0 141 314 E2 1 DH69 823.4 37.7 0.9 961.9 30.8 91.3 14.8 1.4 16.5 10.4 6.9 1.8 15.0 16.8 2.4 2.2 141 315 E2 1 DH70 911.0 35.4 1.0 891.3 31.8 90.3 16.8 1.5 20.1 11.2 7.7 2.7 16.5 19.2 2.5 1.8 144 316 E2 1 DH71 707.0 39.8 1.1 653.5 29.2 86.1 16.4 1.5 19.1 11.2 7.9 1.5 15.8 17.3 2.2 2.3 144 317 E2 1 DH72 835.5 36.8 0.8 985.3 30.7 85.1 14.4 1.6 17.8 9.3 7.5 2.7 16.2 18.9 2.5 1.9 144 318 E2 1 DH73 832.5 38.8 0.9 880.0 31.2 91.5 16.4 1.7 21.8 9.7 7.6 2.1 17.1 19.2 2.5 2.0 145 319 E2 1 DH75 901.8 33.2 0.9 971.7 33.5 97.3 18.0 1.4 19.6 13.2 7.3 2.4 15.1 17.5 2.4 1.9 142 320 E2 1 DH76 837.7 30.6 0.8 1035.5 28.7 84.5 17.1 1.4 19.8 11.8 6.8 2.6 14.2 16.8 2.5 1.8 142 321 E2 1 DH77 926.0 32.7 0.9 1007.7 31.9 94.0 18.3 1.4 20.0 13.4 8.5 3.2 16.9 20.1 2.4 1.5 141 322 E2 1 DH78 764.6 35.8 0.9 849.6 28.3 88.0 14.4 1.3 14.7 11.2 7.4 2.5 15.6 18.1 2.5 2.0 140 323 E2 1 DH79 776.5 35.5 1.0 785.1 30.2 92.9 16.9 1.3 17.8 12.7 6.9 2.2 14.4 16.6 2.4 2.1 141 324 E2 1 DH80 873.5 38.9 1.1 766.2 29.2 87.4 17.1 1.4 18.5 12.5 7.1 1.5 15.6 17.1 2.4 2.3 140 325 E2 1 DH81 711.7 37.6 1.1 639.9 30.8 90.9 15.0 1.4 17.4 10.6 7.0 2.0 15.9 17.9 2.6 1.9 141 326 E2 1 DH82 730.8 37.6 1.0 767.6 27.5 91.2 13.5 1.4 14.8 9.8 7.3 1.7 15.4 17.1 2.3 2.2 140 327 E2 1 DH83 986.3 30.4 1.0 971.8 35.8 98.2 16.3 1.7 22.4 9.5 6.8 3.2 13.5 16.7 2.5 1.8 143 328 E2 1 DH84 997.7 29.8 0.9 1111.0 31.0 87.0 16.6 1.4 17.8 12.3 7.0 2.7 12.8 15.5 2.2 1.9 140 329 E2 1 DH85 864.6 31.0 0.8 1026.8 29.5 87.5 15.7 1.4 17.9 11.1 7.1 2.9 14.9 17.8 2.5 1.7 141 330 E2 1 DH86 698.5 31.8 0.9 804.7 29.7 95.9 14.7 1.3 15.6 11.0 6.4 2.1 14.8 16.9 2.6 1.9 142 331 E2 1 DH87 978.5 36.6 0.9 1038.7 25.8 89.6 17.2 1.4 19.1 12.2 6.7 1.8 14.8 16.6 2.5 2.2 139 332 E2 1 DH89 829.1 29.3 0.9 937.9 32.2 93.6 14.8 1.5 17.3 10.0 7.0 2.7 15.1 17.8 2.6 1.6 143 333 E2 1 DH90 867.5 33.6 1.0 882.5 29.6 86.1 16.6 1.5 19.4 11.3 7.4 3.0 15.2 18.2 2.4 1.9 140 334 E2 1 DH91 809.0 36.2 1.0 803.4 29.5 92.8 17.1 1.4 18.3 12.6 7.0 2.5 15.1 17.6 2.5 2.1 141 335 E2 1 DH92 865.5 31.8 0.9 966.0 29.8 84.8 16.8 1.5 19.8 11.2 7.4 2.4 15.7 18.1 2.5 1.8 144 336 E2 1 DH93 877.7 39.8 1.0 853.8 . 86.1 17.7 1.4 19.7 12.6 7.6 2.0 16.7 18.7 2.5 2.1 140 337 E2 1 DH94 849.3 29.6 0.9 962.9 31.4 100.1 12.5 1.4 13.5 9.2 7.2 2.4 15.5 17.9 2.5 1.6 142 338 E2 1 DH95 839.4 35.3 1.0 803.2 33.2 88.1 14.9 1.5 17.6 10.0 7.7 2.0 14.8 16.8 2.2 2.1 140 339 E2 1 DH96 1089.0 31.8 1.1 1030.2 34.5 102.2 14.9 1.3 15.8 11.1 7.1 2.5 13.4 15.9 2.3 2.0 141 340 E2 1 DH97 879.3 34.9 1.0 881.0 32.4 88.0 15.3 1.6 19.3 9.6 7.0 2.0 15.3 17.3 2.5 2.0 141 341 E2 1 DH98 826.9 35.5 1.1 738.3 33.0 92.0 16.9 1.5 19.5 11.6 7.3 2.3 13.6 15.9 2.2 2.2 139 342 E2 1 DH99 751.5 40.5 1.1 703.0 28.7 85.5 14.9 1.6 18.9 9.4 7.5 1.3 16.0 17.3 2.3 2.3 143 343 E2 1 DH100 815.5 42.2 0.9 910.2 25.0 80.2 14.4 1.4 16.4 10.0 7.1 1.9 16.7 18.6 2.6 2.3 140 344 E2 1 DH101 844.9 39.2 1.0 855.2 26.5 94.4 13.4 1.4 14.6 9.8 7.4 1.6 15.7 17.3 2.3 2.3 142 345 E2 1 DH102 822.3 39.7 0.9 880.4 27.7 99.0 15.6 1.5 18.9 10.2 7.7 3.0 16.1 19.1 2.5 2.1 145 346 E2 1 DH103 630.4 32.3 0.9 677.8 30.1 85.2 14.6 1.7 19.5 8.7 7.4 2.1 15.2 17.3 2.4 1.9 146 347 E2 1 DH104 836.4 41.3 1.1 777.3 28.1 98.3 16.5 1.4 18.4 11.7 7.2 2.1 16.3 18.4 2.5 2.3 144 348 E2 1 DH105 740.2 33.0 0.9 850.8 27.5 86.2 15.4 1.4 16.8 11.2 6.2 2.2 14.8 17.0 2.7 1.9 141 349 E2 1 DH106 932.8 40.9 1.1 840.4 29.0 93.4 16.2 1.6 20.2 10.4 8.0 2.3 16.5 18.8 2.3 2.2 142 350 E2 1 DH107 908.7 31.2 0.8 1079.2 29.0 97.2 12.7 1.3 12.9 9.8 7.0 2.3 13.6 15.9 2.3 2.0 140 351 E2 1 DH108 855.0 29.7 0.7 1141.5 27.5 87.0 17.3 1.5 20.2 11.8 6.3 2.7 14.1 16.8 2.7 1.8 141 352 E2 1 DH109 834.3 34.7 0.9 897.1 26.9 91.0 16.2 1.4 18.2 11.5 7.7 2.5 15.5 18.0 2.4 1.9 143 353 E2 1 DH110 804.0 38.7 1.0 802.4 28.0 101.2 13.5 1.4 14.5 10.1 8.7 2.1 16.7 18.8 2.2 2.1 140 354 E2 1 DH111 680.8 38.3 1.0 684.3 27.7 93.3 17.2 1.4 18.8 12.4 8.0 1.7 16.1 17.8 2.2 2.2 140 355 E2 1 DH112 777.1 40.0 1.0 749.4 28.0 91.6 15.6 1.4 17.8 10.9 7.8 2.0 16.6 18.6 2.4 2.1 141 356 E2 1 DH113 821.5 32.3 0.9 885.2 29.7 99.0 16.1 1.4 17.7 11.5 6.6 1.7 14.8 16.5 2.5 1.9 141 357 E2 1 DH114 766.4 38.9 1.0 739.7 28.3 80.0 16.5 1.5 19.6 11.0 7.6 1.3 15.5 16.8 2.2 2.3 142 358 E2 1 DH115 897.9 27.2 0.8 1097.6 30.7 99.1 13.1 1.5 15.2 9.0 7.1 3.2 14.3 17.5 2.5 1.6 141 359 E2 1 DH116 800.6 25.0 0.8 1002.0 33.0 94.2 13.4 1.3 14.0 10.3 6.5 3.1 12.9 16.0 2.5 1.6 142 360 E2 1 DH117 962.3 32.4 1.0 997.2 30.1 87.1 15.7 1.5 18.5 10.6 7.2 1.6 14.3 15.9 2.3 2.0 141 361 E2 1 DH119 811.4 34.4 1.1 752.0 31.8 90.6 17.2 1.6 22.2 10.6 8.0 2.8 15.3 18.1 2.3 1.9 143 362 E2 1 DH120 821.1 28.4 0.7 1197.0 29.9 83.4 18.2 1.3 19.3 13.7 6.8 3.2 14.0 17.2 2.5 1.7 142 363 E2 1 DH121 678.5 30.7 0.8 807.7 30.0 81.8 16.1 1.5 18.7 11.0 7.1 3.6 15.0 18.6 2.6 1.6 142 364 E2 1 DH122 671.7 34.2 0.6 1088.7 15.8 99.1 16.7 1.5 19.7 11.2 6.4 2.5 13.8 16.3 2.6 2.1 146 365 E2 1 DH123 726.4 35.6 0.9 809.9 26.0 94.1 17.5 1.3 17.4 13.9 7.8 2.7 16.1 18.8 2.4 1.9 144 366 E2 1 DH124 849.0 36.5 0.9 907.1 28.3 85.4 17.8 1.4 20.1 12.4 8.0 2.3 15.8 18.1 2.3 2.0 144 367 E2 1 DH125 808.3 32.4 0.9 911.3 28.0 86.5 13.5 1.4 15.1 9.7 7.0 2.3 14.0 16.3 2.3 2.0 140 368 E2 1 DH126 . 39.2 0.9 . 26.2 81.5 14.4 1.5 16.9 9.8 7.3 1.4 15.8 17.2 2.3 2.3 142 369 E2 1 DH128 807.7 29.2 0.7 1115.5 26.7 94.2 19.4 1.3 19.1 15.6 6.7 3.0 14.3 17.3 2.6 1.7 142 370 E2 1 DH129 707.7 36.9 1.0 679.8 28.3 96.2 12.7 1.2 12.5 10.2 6.8 2.2 14.8 17.0 2.5 2.2 140 371 E2 1 DH130 913.9 36.0 1.0 908.5 29.6 89.3 14.8 1.5 17.4 10.0 7.8 2.3 15.3 17.6 2.3 2.0 141 372 E2 1 DH131 686.3 30.9 1.0 721.7 34.7 90.6 18.8 1.7 24.9 11.5 7.8 4.0 15.5 19.5 2.5 1.6 145 167 Table E.1 Continued. No. Env. Rep. Name GYLD GPS GWPS SPSM TGW PHT FLL FLW FLA FLS SL SSN FSN TSN SC GSP HD 373 E2 2 DH1 929.6 38.8 1.0 914.1 26.6 93.1 15.2 1.3 16.1 11.4 7.3 1.4 15.6 17.0 2.3 2.5 141 374 E2 2 DH3 804.7 37.5 1.2 686.6 30.2 92.6 14.5 1.5 17.1 9.7 6.8 1.8 15.0 16.8 2.5 2.5 142 375 E2 2 DH4 693.9 29.3 0.8 898.9 29.4 80.2 15.7 1.6 19.9 9.7 7.0 3.2 15.0 18.2 2.6 1.9 144 376 E2 2 DH5 684.8 40.5 1.1 597.0 27.9 82.7 14.0 1.5 16.1 9.6 8.0 1.4 15.8 17.2 2.2 2.6 141 377 E2 2 DH6 773.9 38.1 1.1 714.6 29.4 90.4 14.1 1.3 14.6 10.8 7.2 1.7 15.3 17.0 2.4 2.5 140 378 E2 2 DH7 718.7 37.0 1.0 689.1 32.5 81.1 15.7 1.5 19.5 10.1 7.1 2.8 15.3 18.1 2.6 2.4 143 379 E2 2 DH8 695.1 41.0 1.2 575.4 27.7 85.8 14.8 1.4 16.0 10.8 7.5 1.7 15.0 16.7 2.2 2.7 141 380 E2 2 DH9 636.5 36.4 1.2 532.6 31.1 83.3 15.7 1.5 19.2 10.3 7.1 2.7 15.1 17.8 2.5 2.4 142 381 E2 2 DH11 977.2 39.4 1.3 742.6 33.6 108.5 15.2 1.2 14.9 12.3 8.7 1.5 16.3 17.8 2.0 2.4 141 382 E2 2 DH12 586.9 38.7 0.9 623.7 27.6 70.2 12.7 1.8 17.5 7.2 6.4 1.2 15.2 16.4 2.6 2.5 142 383 E2 2 DH13 577.5 44.0 1.1 512.9 25.0 82.1 15.0 1.3 15.4 11.6 7.6 1.6 17.5 19.1 2.5 2.5 141 384 E2 2 DH14 878.4 33.7 1.1 805.2 32.3 87.5 17.6 1.5 21.1 11.6 7.5 2.5 15.9 18.4 2.5 2.1 143 385 E2 2 DH15 813.8 31.2 0.9 940.8 28.0 87.3 14.4 1.3 15.2 10.8 7.1 1.9 14.6 16.5 2.3 2.1 141 386 E2 2 DH16 732.6 30.1 0.8 965.2 28.5 87.5 14.8 1.5 17.5 10.0 6.7 3.1 13.3 16.4 2.5 2.3 142 387 E2 2 DH17 730.2 39.1 1.3 567.8 33.1 89.6 16.0 1.4 17.6 11.5 7.7 2.0 15.2 17.2 2.2 2.6 139 388 E2 2 DH18 781.5 40.2 1.2 644.3 30.7 89.4 14.4 1.9 21.7 7.6 7.8 2.4 16.2 18.6 2.4 2.5 146 389 E2 2 DH19 738.5 34.5 1.1 661.8 30.3 78.4 14.5 1.3 15.3 10.8 6.5 1.6 13.3 14.9 2.3 2.6 139 390 E2 2 DH20 739.5 41.9 1.0 704.9 28.8 80.8 16.8 1.6 21.9 10.2 7.4 2.0 16.1 18.1 2.5 2.6 143 391 E2 2 DH21 822.5 31.6 1.0 790.1 31.8 90.6 15.1 1.4 16.3 11.1 7.2 3.2 14.0 17.2 2.4 2.3 142 392 E2 2 DH22 847.0 42.5 1.3 663.8 30.5 101.1 15.4 1.5 17.8 10.6 8.0 1.6 17.5 19.1 2.4 2.4 141 393 E2 2 DH23 820.1 39.4 1.1 765.0 27.0 91.8 17.0 1.3 17.4 13.3 6.6 1.7 14.6 16.3 2.5 2.7 141 394 E2 2 DH24 880.0 37.4 1.1 820.9 28.7 85.3 15.4 1.5 17.9 10.5 7.0 1.3 14.7 16.0 2.3 2.5 141 395 E2 2 DH25 705.3 36.2 0.9 768.3 29.3 78.5 13.5 1.3 13.9 10.3 7.0 2.2 15.3 17.5 2.5 2.4 138 396 E2 2 DH26 815.4 35.5 0.9 951.4 29.4 82.1 14.8 1.5 17.1 10.1 7.0 1.9 14.9 16.8 2.4 2.4 141 397 E2 2 DH27 667.1 34.9 1.0 650.2 30.2 87.2 13.8 1.2 13.4 11.2 6.4 1.8 14.0 15.8 2.5 2.5 141 398 E2 2 DH28 729.9 32.7 1.0 750.2 30.1 87.6 13.4 1.3 13.5 10.6 6.5 2.0 13.7 15.7 2.4 2.4 141 399 E2 2 DH29 816.7 31.0 0.8 970.0 28.7 81.4 12.9 1.4 13.9 9.5 7.1 2.5 15.0 17.5 2.5 2.1 141 400 E2 2 DH30 893.2 31.8 1.1 814.2 35.4 90.9 16.9 1.5 20.4 11.1 7.2 2.5 15.9 18.4 2.6 2.0 143 401 E2 2 DH31 632.8 36.3 1.0 664.8 26.0 82.7 14.9 1.4 16.2 10.9 7.4 1.7 15.1 16.8 2.3 2.4 140 402 E2 2 DH32 863.6 31.2 0.8 1131.8 26.2 87.8 14.0 1.4 15.5 10.0 6.6 3.1 14.8 17.9 2.7 2.1 146 403 E2 2 DH33 731.3 43.5 0.8 878.0 27.3 93.6 18.8 1.7 25.6 11.0 7.7 2.8 17.4 20.2 2.6 2.5 147 404 E2 2 DH34 733.0 42.8 1.2 588.3 28.5 95.3 15.3 1.4 17.0 10.8 7.7 1.5 16.9 18.4 2.4 2.5 141 405 E2 2 DH35 752.0 34.3 1.0 749.7 30.9 88.2 13.4 1.4 14.5 9.9 7.2 2.2 14.7 16.9 2.4 2.3 141 406 E2 2 DH36 867.6 37.0 1.0 844.8 29.3 84.3 17.6 1.7 23.0 10.6 8.4 2.3 16.9 19.2 2.3 2.2 143 407 E2 2 DH37 708.0 40.6 1.0 709.5 25.2 90.3 15.2 1.4 16.5 11.2 7.5 2.2 16.4 18.6 2.5 2.5 141 408 E2 2 DH38 995.7 37.4 1.0 994.7 27.1 82.9 14.8 1.4 16.2 10.8 7.4 2.2 16.2 18.4 2.5 2.3 141 409 E2 2 DH39 1098.1 31.6 0.9 1180.7 28.8 95.7 18.5 1.4 20.7 13.3 6.3 3.0 14.6 17.6 2.8 2.2 140 410 E2 2 DH40 744.0 36.5 1.1 674.6 29.3 100.4 12.8 1.2 12.6 10.4 8.2 2.0 17.0 19.0 2.3 2.1 143 411 E2 2 DH41 696.9 33.4 0.9 760.0 26.8 85.2 13.0 1.2 13.0 10.4 7.0 1.9 15.0 16.9 2.4 2.2 142 412 E2 2 DH42 918.7 43.5 1.3 696.0 29.6 94.3 16.1 1.3 17.1 11.9 7.9 1.4 18.0 19.4 2.4 2.4 142 413 E2 2 DH43 845.4 37.9 1.2 727.6 30.8 81.8 14.3 1.3 14.8 11.0 6.9 2.0 14.4 16.4 2.4 2.6 140 414 E2 2 DH44 815.7 39.0 1.4 604.2 32.6 100.0 15.4 1.5 18.1 10.3 7.9 1.4 15.4 16.8 2.1 2.5 141 415 E2 2 DH45 735.2 34.4 1.2 618.3 33.6 89.1 14.1 1.5 16.8 9.4 7.8 1.5 15.7 17.2 2.2 2.2 140 416 E2 2 DH46 591.8 37.8 0.9 624.2 26.6 89.0 17.0 1.5 20.8 11.0 8.1 2.3 15.4 17.7 2.2 2.5 142 417 E2 2 DH47 837.5 32.3 0.9 916.3 28.9 90.3 14.0 1.4 15.8 9.9 6.9 2.1 14.1 16.2 2.3 2.3 142 418 E2 2 DH49 642.3 36.3 1.1 588.7 31.5 81.6 15.5 1.5 18.6 10.2 7.2 2.2 14.8 17.0 2.4 2.4 141 419 E2 2 DH50 740.6 31.3 1.0 755.7 29.8 92.3 12.8 1.2 12.5 10.5 6.9 1.9 14.9 16.8 2.4 2.1 142 420 E2 2 DH51 717.5 44.4 1.1 666.2 25.5 90.0 16.3 1.4 18.3 11.5 8.0 1.6 16.6 18.2 2.3 2.7 142 421 E2 2 DH52 943.3 35.8 0.8 1257.7 24.3 88.2 14.4 1.3 14.8 11.2 6.9 1.8 15.1 16.9 2.4 2.4 142 422 E2 2 DH53 914.8 36.1 0.9 984.7 28.2 73.6 13.7 1.4 15.2 9.9 6.9 1.8 15.5 17.3 2.5 2.3 141 423 E2 2 DH54 786.2 39.6 1.3 620.5 32.4 84.2 19.3 1.5 22.7 12.9 8.2 1.6 15.0 16.6 2.0 2.6 142 424 E2 2 DH55 732.7 37.4 1.1 691.2 28.0 90.7 14.7 1.4 16.0 10.7 6.5 1.8 14.2 16.0 2.5 2.6 141 425 E2 2 DH56 665.9 35.8 1.0 640.3 28.5 85.3 11.9 1.3 12.4 9.1 7.0 2.1 15.0 17.1 2.4 2.4 141 426 E2 2 DH57 783.2 31.7 0.9 839.5 28.8 88.8 14.3 1.5 16.7 9.6 6.3 1.9 15.1 17.0 2.7 2.1 142 427 E2 2 DH58 736.4 35.7 0.9 776.0 30.3 89.2 14.7 1.4 16.8 10.2 6.8 2.0 14.0 16.0 2.4 2.5 141 428 E2 2 DH59 837.7 42.0 1.1 740.0 27.1 81.3 13.5 1.2 12.6 11.6 6.9 0.8 15.8 16.6 2.4 2.7 140 429 E2 2 DH60 613.1 38.6 1.2 520.9 29.5 81.2 11.8 1.3 11.9 9.4 7.3 2.5 15.0 17.5 2.4 2.6 137 430 E2 2 DH61 649.7 32.0 0.9 692.7 29.8 79.6 12.6 1.4 13.8 9.1 7.1 1.9 14.3 16.2 2.3 2.2 142 431 E2 2 DH62 675.4 36.5 1.0 649.4 28.3 82.9 15.6 1.3 16.5 11.7 6.5 1.9 13.7 15.6 2.4 2.7 139 432 E2 2 DH63 762.6 45.7 1.4 550.6 30.3 90.4 16.4 1.6 20.4 10.5 8.5 1.3 16.7 18.0 2.1 2.7 141 433 E2 2 DH64 903.8 34.4 1.0 864.9 33.1 103.1 16.0 1.5 18.5 10.9 7.7 2.3 15.4 17.7 2.3 2.2 142 434 E2 2 DH65 884.3 43.6 1.2 755.1 26.5 94.8 18.0 1.4 20.1 12.8 8.4 1.8 18.0 19.8 2.4 2.4 142 168 Table E.1 Continued. No. Env. Rep. Name GYLD GPS GWPS SPSM TGW PHT FLL FLW FLA FLS SL SSN FSN TSN SC GSP HD 435 E2 2 DH66 628.1 38.3 1.3 490.3 32.8 83.8 15.6 1.5 18.7 10.3 8.0 2.0 16.6 18.6 2.3 2.3 143 436 E2 2 DH67 574.4 34.3 0.9 658.7 27.0 81.9 14.5 1.2 14.1 11.8 6.7 1.9 15.1 17.0 2.5 2.3 139 437 E2 2 DH68 473.2 32.5 0.9 551.5 27.2 78.7 16.2 1.4 17.6 12.0 6.9 1.7 14.3 16.0 2.3 2.3 141 438 E2 2 DH69 694.3 42.3 1.2 603.2 29.4 80.1 13.8 1.4 14.8 10.2 6.8 1.3 15.5 16.8 2.5 2.7 140 439 E2 2 DH70 788.9 35.5 1.0 795.3 31.3 91.4 14.9 1.5 17.4 10.1 7.7 2.7 16.2 18.9 2.5 2.2 143 440 E2 2 DH71 708.5 39.0 1.1 628.1 29.9 84.1 15.3 1.4 17.2 10.7 7.7 1.6 15.7 17.3 2.3 2.5 141 441 E2 2 DH72 610.0 35.5 1.0 597.4 28.7 84.1 14.2 1.5 17.1 9.4 7.3 1.9 16.2 18.1 2.5 2.2 143 442 E2 2 DH73 599.3 36.9 1.0 594.6 28.1 82.8 14.8 1.5 17.7 9.7 7.2 1.9 15.9 17.8 2.5 2.3 143 443 E2 2 DH75 822.2 32.3 0.9 871.9 32.3 89.2 13.1 1.3 13.0 10.4 6.8 2.4 13.7 16.1 2.4 2.4 141 444 E2 2 DH76 693.5 34.0 1.0 689.4 28.5 76.9 14.3 1.4 16.0 10.1 6.7 2.1 14.3 16.4 2.4 2.4 140 445 E2 2 DH77 827.4 32.6 1.0 823.3 30.3 88.0 18.0 1.3 18.1 14.2 8.0 2.8 15.9 18.7 2.4 2.0 139 446 E2 2 DH78 798.5 40.4 1.1 696.8 30.4 88.7 14.1 1.3 14.5 10.8 7.8 1.5 16.5 18.0 2.3 2.4 139 447 E2 2 DH79 847.6 37.5 1.1 774.8 29.3 96.4 15.2 1.4 16.4 11.1 7.2 2.1 14.9 17.0 2.4 2.5 142 448 E2 2 DH80 797.6 37.2 1.1 723.8 29.1 87.2 15.2 1.3 15.8 11.6 7.0 1.9 15.3 17.2 2.5 2.4 142 449 E2 2 DH81 690.7 35.6 1.1 646.7 30.6 79.2 17.2 1.7 23.6 9.9 7.2 1.1 16.5 17.6 2.4 2.2 141 450 E2 2 DH82 755.0 34.4 0.9 885.1 26.7 88.8 12.6 1.4 14.4 8.9 7.4 2.1 15.0 17.1 2.3 2.3 141 451 E2 2 DH83 733.4 33.3 1.1 652.0 32.4 96.6 13.1 1.3 13.1 10.4 6.6 2.6 13.3 15.9 2.4 2.5 140 452 E2 2 DH84 857.6 35.8 1.1 760.3 33.3 87.7 14.4 1.3 14.8 11.1 7.0 1.6 13.8 15.4 2.2 2.6 140 453 E2 2 DH85 764.3 37.1 1.1 683.7 27.4 79.8 14.8 1.3 15.6 11.3 7.0 1.4 15.1 16.5 2.4 2.5 141 454 E2 2 DH86 852.1 34.1 0.9 901.7 31.5 96.7 15.3 1.4 16.5 11.2 6.6 2.0 15.0 17.0 2.6 2.3 144 455 E2 2 DH87 822.5 43.2 1.3 650.7 27.2 89.7 13.5 1.3 13.5 10.7 7.0 1.0 15.3 16.3 2.3 2.8 138 456 E2 2 DH89 581.9 32.2 1.1 548.9 31.4 82.0 13.1 1.4 14.2 9.5 6.7 2.0 14.6 16.6 2.5 2.2 141 457 E2 2 DH90 676.0 36.9 1.2 575.8 30.1 77.2 14.9 1.3 15.7 11.2 7.5 2.5 15.5 18.0 2.4 2.4 139 458 E2 2 DH91 656.0 37.6 1.1 577.0 30.5 89.8 14.8 1.4 15.9 10.9 6.7 2.2 15.1 17.3 2.6 2.5 140 459 E2 2 DH92 715.0 34.3 0.9 753.5 26.9 81.9 15.9 1.4 17.1 11.8 6.9 1.8 14.8 16.6 2.4 2.3 140 460 E2 2 DH93 663.1 44.5 1.2 535.7 25.6 80.9 14.9 1.3 14.9 11.8 7.5 1.4 16.7 18.1 2.4 2.6 140 461 E2 2 DH94 795.2 28.4 0.9 903.7 30.1 90.3 12.5 1.3 13.3 9.4 6.9 2.3 14.6 16.9 2.5 1.9 140 462 E2 2 DH95 958.7 35.3 1.0 958.7 31.7 91.3 16.0 1.3 16.9 12.0 8.0 2.0 15.3 17.3 2.2 2.3 142 463 E2 2 DH96 971.1 29.2 1.0 944.6 34.6 96.0 14.0 1.3 14.5 10.7 6.7 2.6 12.8 15.4 2.3 2.3 141 464 E2 2 DH97 831.8 38.1 1.1 736.7 33.0 90.1 15.6 1.6 19.2 10.1 7.3 1.4 15.6 17.0 2.3 2.4 141 465 E2 2 DH98 729.1 44.4 1.4 519.3 32.1 84.3 13.6 1.4 14.8 10.0 7.8 1.0 15.2 16.2 2.1 2.9 138 466 E2 2 DH99 704.9 33.8 0.9 771.3 27.7 80.5 16.4 1.5 19.1 11.2 6.8 1.7 14.5 16.2 2.4 2.3 143 467 E2 2 DH100 648.2 48.0 1.2 544.7 25.3 74.6 12.5 1.4 13.4 9.2 7.3 1.1 16.9 18.0 2.5 2.8 140 468 E2 2 DH101 872.9 41.4 1.1 823.5 27.7 91.2 17.8 1.6 22.3 11.2 8.0 1.4 16.3 17.7 2.2 2.5 141 469 E2 2 DH102 650.9 43.5 1.1 589.1 28.3 88.5 15.4 1.6 19.7 9.6 8.1 1.8 17.0 18.8 2.3 2.6 144 470 E2 2 DH103 539.0 32.5 1.0 531.5 30.0 77.5 13.0 1.5 15.7 8.5 7.2 2.1 15.0 17.1 2.4 2.2 145 471 E2 2 DH104 762.3 37.7 1.0 798.2 27.6 95.6 15.6 1.4 17.7 10.9 7.0 2.8 15.5 18.3 2.6 2.4 145 472 E2 2 DH105 659.9 38.0 1.1 618.5 27.2 88.2 15.5 1.4 17.8 10.8 6.8 1.8 16.2 18.0 2.7 2.3 142 473 E2 2 DH106 810.6 42.4 1.2 656.3 29.4 81.7 14.9 1.5 17.6 10.0 7.7 2.1 16.6 18.7 2.4 2.6 141 474 E2 2 DH107 758.2 32.0 0.9 870.4 28.9 90.0 12.4 1.2 12.0 10.2 6.8 2.2 13.7 15.9 2.3 2.3 139 475 E2 2 DH108 672.9 34.7 1.0 689.4 27.0 87.9 15.1 1.4 16.4 11.2 6.6 2.3 14.6 16.9 2.6 2.4 139 476 E2 2 DH109 570.9 30.5 0.8 675.6 27.2 80.0 14.3 1.4 15.3 10.6 7.0 2.6 14.5 17.1 2.5 2.1 140 477 E2 2 DH110 617.6 38.8 1.1 554.4 27.2 90.4 12.2 1.0 9.2 12.8 8.0 1.9 16.1 18.0 2.3 2.4 139 478 E2 2 DH111 642.9 39.2 1.1 568.5 27.9 89.6 15.4 1.3 16.3 11.5 8.0 1.9 15.8 17.7 2.2 2.5 141 479 E2 2 DH112 574.3 38.7 1.1 500.3 27.8 78.7 13.8 1.4 15.1 10.0 7.4 2.2 15.8 18.0 2.4 2.5 140 480 E2 2 DH113 686.5 35.1 1.1 638.0 30.8 84.6 15.3 1.3 15.7 11.8 6.5 1.6 14.2 15.8 2.5 2.5 140 481 E2 2 DH114 773.9 36.5 1.0 765.5 28.4 74.0 14.6 1.5 17.0 9.9 7.3 1.3 15.2 16.5 2.3 2.4 141 482 E2 2 DH115 669.1 31.7 1.0 679.3 29.3 88.7 13.0 1.3 13.9 9.7 7.0 2.4 14.1 16.5 2.4 2.2 140 483 E2 2 DH116 730.3 30.2 0.9 772.8 30.4 91.2 13.3 1.4 14.8 9.4 6.5 2.4 13.1 15.5 2.4 2.3 141 484 E2 2 DH117 666.0 34.1 1.0 663.3 29.8 80.6 13.0 1.4 13.9 9.6 7.3 1.4 14.2 15.6 2.2 2.4 139 485 E2 2 DH119 761.8 39.3 1.3 608.5 31.8 99.4 17.3 1.6 21.9 10.8 8.3 2.4 16.7 19.1 2.3 2.3 143 486 E2 2 DH120 805.8 33.9 0.9 923.0 29.7 87.8 16.0 1.4 17.1 11.8 7.1 2.2 15.7 17.9 2.5 2.2 142 487 E2 2 DH121 759.2 35.5 1.1 712.9 29.0 89.1 13.4 1.4 15.4 9.3 7.2 2.8 15.4 18.2 2.5 2.3 141 488 E2 2 DH122 581.9 35.8 0.9 641.6 27.7 87.9 13.3 1.3 13.8 10.2 6.5 1.6 14.5 16.1 2.5 2.5 142 489 E2 2 DH123 555.1 34.4 0.9 602.1 25.4 91.4 14.1 1.1 12.9 12.2 7.5 2.5 14.9 17.4 2.3 2.3 143 490 E2 2 DH124 626.2 35.3 0.9 722.2 26.0 79.8 15.3 1.3 15.9 11.7 7.8 1.9 15.1 17.0 2.2 2.3 142 491 E2 2 DH125 863.3 31.1 0.9 978.7 28.7 84.7 12.5 1.4 13.8 9.0 6.7 2.7 13.8 16.5 2.5 2.2 141 492 E2 2 DH126 775.2 43.0 1.1 692.7 26.7 82.8 13.8 1.5 16.7 9.0 7.9 1.4 17.0 18.4 2.3 2.5 144 493 E2 2 DH128 731.9 37.1 1.0 721.8 27.4 84.5 17.9 1.3 18.3 13.8 6.6 2.2 14.8 17.0 2.6 2.5 142 494 E2 2 DH129 697.2 37.9 1.2 601.6 29.8 86.7 13.9 1.3 14.1 11.0 6.7 2.2 14.4 16.6 2.5 2.6 140 495 E2 2 DH130 794.7 32.0 0.9 893.9 28.8 83.3 13.6 1.4 14.6 10.0 7.3 2.5 14.8 17.3 2.4 2.2 141 496 E2 2 DH131 689.8 35.9 1.2 576.2 36.6 89.4 16.7 1.8 23.2 9.5 8.1 3.2 16.5 19.7 2.4 2.2 144 169 Table E.1 Continued. No. Env. Rep. Name GYLD GPS GWPS SPSM TGW PHT FLL FLW FLA FLS SL SSN FSN TSN SC GSP HD 497 E3 1 DH1 662.5 47.7 1.5 439.9 31.3 98.3 14.6 1.5 17.0 9.9 7.0 0.4 15.5 15.9 2.3 3.1 126 498 E3 1 DH3 810.1 48.3 1.5 527.1 30.5 99.6 17.7 1.8 25.1 9.9 7.2 1.7 16.1 17.8 2.5 3.0 130 499 E3 1 DH4 794.3 44.1 1.4 561.8 29.9 90.3 18.3 1.9 28.2 9.4 7.1 1.6 15.0 16.6 2.4 2.9 129 500 E3 1 DH5 830.1 43.9 1.3 648.0 31.0 91.8 17.4 1.6 22.6 10.6 7.7 1.8 14.8 16.6 2.1 3.0 129 501 E3 1 DH6 847.7 47.5 1.5 557.0 32.6 99.2 17.5 1.6 22.8 10.6 7.3 1.1 15.4 16.5 2.3 3.1 123 502 E3 1 DH7 491.5 38.6 1.2 408.3 31.0 92.4 17.0 1.7 23.4 9.8 6.6 3.1 14.2 17.3 2.6 2.7 133 503 E3 1 DH8 614.6 51.5 1.6 373.6 30.7 103.1 18.7 1.7 24.5 11.3 7.7 1.5 15.2 16.7 2.2 3.4 127 504 E3 1 DH9 655.1 43.8 1.4 454.6 32.8 97.4 20.0 1.9 29.9 10.7 7.4 2.4 16.2 18.6 2.5 2.7 130 505 E3 1 DH11 611.3 49.4 1.9 330.1 35.1 115.5 19.7 1.6 24.3 12.8 8.6 1.4 15.5 16.9 2.0 3.2 125 506 E3 1 DH12 582.1 42.5 1.4 408.5 32.5 87.0 16.7 1.7 22.9 9.6 7.0 1.5 14.1 15.6 2.2 2.9 128 507 E3 1 DH13 548.3 47.8 1.4 387.4 . 93.9 20.0 1.7 27.2 11.6 7.1 2.2 15.9 18.1 2.6 2.6 130 508 E3 1 DH14 475.6 38.8 1.3 363.6 32.1 95.9 16.6 1.6 20.9 10.7 7.0 3.2 15.3 18.5 2.7 2.1 129 509 E3 1 DH15 774.6 37.5 1.3 597.7 31.9 91.0 14.8 1.5 17.3 10.0 7.1 1.8 13.8 15.6 2.2 2.4 122 510 E3 1 DH16 834.4 42.1 1.5 573.5 33.0 95.1 15.5 1.7 20.5 9.3 6.9 1.5 13.8 15.3 2.2 2.8 124 511 E3 1 DH17 790.0 42.5 1.5 516.3 32.7 98.9 17.8 1.6 22.7 11.0 7.4 1.8 14.3 16.1 2.2 2.6 124 512 E3 1 DH18 605.8 42.7 1.4 435.1 31.4 94.4 18.7 2.2 33.5 8.3 7.5 1.9 15.6 17.5 2.3 2.4 135 513 E3 1 DH19 469.2 41.2 1.4 334.7 34.6 99.2 16.4 1.8 23.3 9.2 6.6 1.5 13.3 14.8 2.3 2.8 127 514 E3 1 DH20 808.3 50.7 1.6 495.6 30.1 91.5 20.4 1.9 31.4 10.5 7.8 1.5 16.6 18.1 2.3 2.8 130 515 E3 1 DH21 651.0 36.8 1.3 512.2 32.3 105.2 18.9 1.6 23.7 11.9 6.8 3.1 13.4 16.5 2.4 2.2 131 516 E3 1 DH22 852.4 51.7 1.7 491.3 32.7 114.2 16.1 1.8 22.9 9.0 7.8 2.0 17.6 19.6 2.5 2.6 129 517 E3 1 DH23 817.3 42.1 1.3 607.2 30.7 103.8 18.6 1.5 22.2 12.3 6.5 1.9 13.8 15.7 2.4 2.7 126 518 E3 1 DH24 450.2 43.8 1.4 319.3 32.2 96.9 16.0 1.6 20.7 9.8 6.7 1.5 14.1 15.6 2.3 2.8 121 519 E3 1 DH25 1044.5 38.0 1.2 880.7 33.1 96.4 16.3 1.7 21.9 9.6 6.9 2.6 14.4 17.0 2.5 2.2 125 520 E3 1 DH26 671.9 44.4 1.4 465.0 31.1 94.5 16.2 1.8 22.5 9.2 7.0 1.8 15.1 16.9 2.4 2.6 128 521 E3 1 DH27 505.0 47.8 1.5 333.3 30.6 103.2 20.5 1.8 28.7 11.7 7.1 1.5 14.7 16.2 2.3 2.9 131 522 E3 1 DH28 804.5 52.1 1.5 532.1 32.2 98.1 17.5 1.6 22.6 10.7 7.1 0.9 15.3 16.2 2.3 3.2 129 523 E3 1 DH29 903.7 46.9 1.5 589.9 31.6 89.2 15.8 1.6 20.2 9.8 7.6 2.1 16.2 18.3 2.4 2.6 129 524 E3 1 DH30 779.4 45.7 1.6 498.0 34.1 99.7 18.0 1.8 25.5 10.1 7.3 2.1 16.3 18.4 2.5 2.5 129 525 E3 1 DH31 583.7 44.6 1.4 409.0 29.7 92.6 18.5 1.7 24.7 10.9 7.6 1.7 15.4 17.1 2.3 2.6 129 526 E3 1 DH32 612.5 42.2 1.2 505.4 25.9 95.7 15.4 1.7 20.5 9.1 7.5 1.7 16.5 18.2 2.4 2.3 134 527 E3 1 DH33 533.0 51.8 1.5 364.3 29.2 104.2 18.9 1.9 29.2 9.7 7.6 2.2 16.8 19.0 2.5 2.7 136 528 E3 1 DH34 629.3 47.2 1.5 412.6 32.1 99.5 18.3 1.7 24.4 10.9 7.4 2.2 15.6 17.8 2.4 2.7 126 529 E3 1 DH35 505.5 32.0 1.1 453.8 . 99.8 17.3 1.9 25.8 9.2 7.1 3.1 14.1 17.2 2.4 1.9 126 530 E3 1 DH36 832.9 48.7 1.6 533.6 31.4 96.7 20.6 2.0 32.3 10.4 9.0 1.1 18.3 19.4 2.2 2.5 130 531 E3 1 DH37 735.2 44.6 1.4 522.9 30.9 100.1 16.3 1.6 20.5 10.3 7.3 2.8 15.6 18.4 2.5 2.4 128 532 E3 1 DH38 824.3 42.3 1.4 568.9 31.5 95.4 17.2 1.6 21.9 10.7 7.5 0.9 15.5 16.4 2.2 2.6 121 533 E3 1 DH39 863.0 44.7 1.5 591.9 31.1 105.4 17.5 1.7 23.7 10.2 6.5 1.7 14.2 15.9 2.5 2.8 126 534 E3 1 DH40 363.4 38.1 1.3 280.6 33.3 107.7 15.6 1.5 18.2 10.7 7.5 0.7 15.2 15.9 2.1 2.4 130 535 E3 1 DH41 651.2 41.5 1.3 500.6 31.9 97.6 17.4 1.7 23.5 10.2 6.7 1.3 13.5 14.8 2.2 2.8 131 536 E3 1 DH42 953.4 38.1 1.1 848.7 31.9 103.4 16.3 1.6 20.2 10.4 7.0 2.9 15.5 18.4 2.6 2.1 130 537 E3 1 DH43 830.6 45.2 1.5 545.7 32.9 96.2 16.6 1.7 22.3 9.8 7.2 1.9 15.3 17.2 2.4 2.6 128 538 E3 1 DH44 720.3 37.6 1.4 531.2 36.0 109.5 21.3 1.8 29.9 12.0 7.7 2.1 14.2 16.3 2.1 2.3 132 539 E3 1 DH45 693.5 36.8 1.3 547.8 35.1 93.8 17.9 1.8 25.8 9.9 6.8 2.1 13.6 15.7 2.3 2.4 128 540 E3 1 DH46 581.9 48.3 1.5 390.0 30.8 99.2 19.8 1.7 26.8 11.5 8.1 1.5 15.5 17.0 2.1 2.8 131 541 E3 1 DH47 888.6 41.3 1.3 667.6 32.9 99.8 17.2 1.7 23.8 9.9 7.1 2.3 14.5 16.8 2.4 2.5 130 542 E3 1 DH49 682.3 55.8 1.8 384.9 31.7 97.5 19.7 1.8 28.6 10.8 7.9 1.3 15.9 17.2 2.2 3.2 130 543 E3 1 DH50 904.9 33.6 1.3 671.8 35.1 100.8 14.3 1.5 17.1 9.5 6.5 2.4 13.4 15.8 2.4 2.1 125 544 E3 1 DH51 632.7 50.0 1.5 434.2 29.9 98.6 20.3 1.8 28.8 11.4 7.9 1.4 15.9 17.3 2.2 2.9 131 545 E3 1 DH52 698.2 48.0 1.5 476.9 29.5 87.9 15.4 1.7 20.7 9.2 7.2 0.8 15.5 16.3 2.3 2.9 127 546 E3 1 DH53 768.8 46.1 1.6 491.6 31.0 83.6 18.7 1.8 27.0 10.3 7.2 0.9 15.2 16.1 2.2 2.9 129 547 E3 1 DH54 858.6 50.5 1.9 456.4 34.9 94.4 18.1 1.9 26.8 9.7 8.0 1.8 14.6 16.4 2.1 3.1 123 548 E3 1 DH55 875.1 40.5 1.2 707.4 28.5 101.3 16.3 1.6 20.2 10.4 6.3 2.4 14.4 16.8 2.7 2.4 129 549 E3 1 DH56 848.5 32.9 1.2 691.0 . 100.4 13.8 1.5 16.1 9.4 5.9 2.3 11.9 14.2 2.4 2.3 125 550 E3 1 DH57 874.6 42.2 1.4 638.8 30.6 98.3 14.3 1.7 19.7 8.2 6.3 2.2 15.2 17.4 2.8 2.4 129 551 E3 1 DH58 689.1 47.6 1.5 475.3 31.1 101.3 15.5 1.5 18.3 10.5 6.9 1.5 14.2 15.7 2.3 3.0 127 552 E3 1 DH59 1071.2 36.6 1.1 951.3 30.6 100.9 14.8 1.6 19.0 9.2 6.5 1.4 13.3 14.7 2.3 2.5 122 553 E3 1 DH60 880.1 54.3 1.7 519.8 31.3 95.7 18.9 1.7 26.2 10.9 8.0 1.4 16.5 17.9 2.2 3.0 126 554 E3 1 DH61 642.1 37.3 1.1 564.2 31.7 93.0 17.3 1.8 24.2 9.7 7.2 2.0 14.6 16.6 2.3 2.3 131 555 E3 1 DH62 771.7 52.6 1.7 462.7 . 103.1 19.5 1.7 26.3 11.4 7.2 0.4 15.5 15.9 2.2 3.3 126 556 E3 1 DH63 666.0 55.4 2.0 331.3 33.6 93.5 16.8 1.8 24.3 9.2 8.5 0.9 15.8 16.7 2.0 3.3 123 557 E3 1 DH64 830.2 42.6 1.6 515.7 36.1 111.8 16.2 1.6 20.3 10.2 7.5 1.6 14.7 16.3 2.2 2.6 123 558 E3 1 DH65 721.8 55.6 1.7 420.4 31.0 91.1 18.8 1.6 23.4 12.0 8.0 0.9 17.0 17.9 2.2 3.1 125 170 Table E.1 Continued. No. Env. Rep. Name GYLD GPS GWPS SPSM TGW PHT FLL FLW FLA FLS SL SSN FSN TSN SC GSP HD 559 E3 1 DH66 652.4 42.7 1.5 421.7 34.8 105.2 18.7 2.1 30.4 9.1 8.1 3.7 17.1 20.8 2.6 2.1 132 560 E3 1 DH67 845.4 45.1 1.5 573.6 32.1 97.4 17.9 1.7 23.8 10.6 7.2 1.1 15.9 17.0 2.4 2.7 125 561 E3 1 DH68 609.3 43.3 1.2 503.1 27.4 97.0 18.3 1.8 25.8 10.2 7.4 1.0 15.3 16.3 2.2 2.7 127 562 E3 1 DH69 946.3 44.2 1.5 612.1 33.0 98.4 16.1 1.7 21.0 9.8 6.9 0.9 14.4 15.3 2.2 2.9 124 563 E3 1 DH70 661.2 46.8 1.3 490.2 31.7 98.6 16.8 1.8 23.8 9.5 8.0 1.8 17.0 18.8 2.3 2.5 132 564 E3 1 DH71 826.0 49.2 1.5 555.1 31.2 99.8 19.0 1.9 28.0 10.3 8.0 1.5 17.1 18.6 2.3 2.6 130 565 E3 1 DH72 835.7 41.7 1.4 617.7 31.8 98.1 14.2 1.7 18.6 8.5 6.9 2.8 15.2 18.0 2.6 2.3 129 566 E3 1 DH73 804.7 50.0 1.6 506.4 30.4 99.6 16.2 1.7 22.2 9.4 7.8 1.3 15.9 17.2 2.2 2.9 130 567 E3 1 DH75 941.7 36.5 1.3 723.8 36.1 103.5 17.1 1.8 24.6 9.5 6.9 1.9 13.5 15.4 2.2 2.3 122 568 E3 1 DH76 916.5 31.1 1.1 867.0 32.4 91.3 18.1 1.6 22.7 11.4 6.3 2.5 13.1 15.6 2.5 2.0 130 569 E3 1 DH77 746.8 37.5 1.3 581.2 33.7 105.3 18.1 1.6 22.3 11.6 7.8 2.6 15.8 18.4 2.4 2.0 127 570 E3 1 DH78 862.2 47.3 1.6 530.9 33.4 92.0 16.6 1.6 21.0 10.5 7.1 1.5 14.8 16.3 2.3 2.9 125 571 E3 1 DH79 670.1 52.0 1.7 399.1 32.0 101.2 15.8 1.5 19.1 10.4 7.5 1.3 15.2 16.5 2.2 3.1 126 572 E3 1 DH80 742.3 48.2 1.5 480.8 31.7 103.9 19.3 1.6 24.6 12.0 7.1 1.4 15.6 17.0 2.4 2.8 129 573 E3 1 DH81 719.8 36.1 1.2 584.8 31.6 101.0 15.3 1.6 19.5 9.5 6.1 1.6 15.4 17.0 2.8 2.1 127 574 E3 1 DH82 793.8 51.3 1.8 436.9 32.2 100.1 15.0 1.6 19.2 9.3 8.1 0.6 16.2 16.8 2.1 3.1 122 575 E3 1 DH83 828.1 40.2 1.4 584.0 35.6 110.5 18.3 1.6 23.2 11.4 7.2 2.6 14.2 16.8 2.3 2.4 126 576 E3 1 DH84 1017.3 34.9 1.3 786.2 37.2 102.4 15.5 1.5 18.2 10.4 7.1 1.7 12.8 14.5 2.0 2.4 122 577 E3 1 DH85 921.5 33.3 1.2 792.1 31.7 90.6 16.2 1.6 20.2 10.2 6.4 2.1 13.0 15.1 2.4 2.2 128 578 E3 1 DH86 787.1 40.3 1.4 571.2 32.0 104.8 17.3 1.6 21.4 11.1 6.1 1.8 14.5 16.3 2.7 2.5 128 579 E3 1 DH87 893.4 47.2 1.4 642.3 28.7 101.3 18.0 1.7 24.0 10.7 7.0 1.0 14.8 15.8 2.3 3.0 121 580 E3 1 DH89 804.8 40.5 1.4 591.8 33.5 99.8 17.2 1.7 22.6 10.3 7.1 2.2 14.8 17.0 2.4 2.4 127 581 E3 1 DH90 768.8 37.2 1.3 580.2 31.0 91.2 15.2 1.7 19.8 9.3 6.7 2.2 13.5 15.7 2.4 2.4 125 582 E3 1 DH91 714.4 42.8 1.4 498.9 31.7 109.0 17.0 1.5 20.5 11.1 7.1 1.9 14.6 16.5 2.4 2.6 125 583 E3 1 DH92 898.9 36.5 1.3 708.3 32.9 97.5 16.8 1.7 22.1 10.3 6.8 1.9 13.4 15.3 2.2 2.4 126 584 E3 1 DH93 888.8 50.2 1.7 523.8 31.1 87.8 16.5 1.8 23.2 9.3 7.8 1.3 15.6 16.9 2.2 2.9 126 585 E3 1 DH94 838.3 37.0 1.2 691.1 31.2 103.2 15.5 1.5 19.1 10.1 6.9 2.0 14.8 16.8 2.4 2.2 130 586 E3 1 DH95 1026.1 37.0 1.3 799.1 36.0 102.0 15.7 1.4 18.0 11.1 7.5 1.7 14.3 16.0 2.1 2.3 121 587 E3 1 DH96 759.0 35.8 1.3 564.3 . 105.1 16.0 1.6 19.6 10.3 6.4 2.4 12.5 14.9 2.3 2.4 128 588 E3 1 DH97 884.6 44.2 1.5 581.2 34.2 109.4 16.1 1.9 23.8 8.7 7.3 1.0 15.8 16.8 2.3 2.6 126 589 E3 1 DH98 712.6 48.5 1.8 404.2 38.0 91.5 16.4 1.6 20.6 10.3 7.8 0.2 15.1 15.3 2.0 3.2 120 590 E3 1 DH99 722.2 48.9 1.6 466.0 32.0 91.5 17.5 2.0 27.7 8.8 7.0 1.5 16.4 17.9 2.6 2.7 130 591 E3 1 DH100 877.3 47.9 1.4 620.9 30.5 80.5 14.6 1.7 19.7 8.6 6.9 1.5 15.3 16.8 2.4 2.8 125 592 E3 1 DH101 891.5 47.0 1.4 626.5 . 99.4 17.6 1.8 24.9 9.9 7.5 1.7 15.5 17.2 2.3 2.7 126 593 E3 1 DH102 669.8 53.4 1.4 462.2 29.2 99.7 20.3 1.8 28.7 11.7 8.1 1.9 16.9 18.8 2.3 2.8 131 594 E3 1 DH103 470.3 42.7 1.4 342.8 30.7 92.4 18.8 2.0 30.2 9.3 7.2 1.9 14.7 16.6 2.3 2.6 132 595 E3 1 DH104 714.7 42.7 1.3 551.0 29.6 104.0 19.8 1.7 26.2 11.9 6.9 1.7 14.6 16.3 2.4 2.6 132 596 E3 1 DH105 760.8 44.3 1.4 541.9 29.7 97.1 18.5 1.6 23.6 11.5 6.3 1.9 14.4 16.3 2.6 2.7 126 597 E3 1 DH106 873.8 48.0 1.5 592.4 31.1 99.6 16.2 1.8 22.7 9.2 7.6 2.0 16.3 18.3 2.4 2.6 128 598 E3 1 DH107 726.8 38.9 1.1 642.6 31.4 98.0 15.8 1.8 22.2 9.0 6.7 1.5 13.2 14.7 2.2 2.6 125 599 E3 1 DH108 488.1 40.1 1.3 367.2 30.5 106.8 17.4 1.6 22.7 10.6 6.4 2.4 14.7 17.1 2.7 2.3 125 600 E3 1 DH109 634.5 43.0 1.3 485.1 30.0 92.0 18.1 1.6 23.0 11.2 7.5 1.7 14.9 16.6 2.2 2.6 129 601 E3 1 DH110 545.4 41.5 1.4 391.8 32.1 109.2 16.0 1.5 19.3 10.5 7.9 2.1 16.0 18.1 2.3 2.3 127 602 E3 1 DH111 517.6 34.3 1.1 452.5 30.5 103.7 18.9 1.6 24.4 11.5 6.8 2.1 13.7 15.8 2.3 2.2 127 603 E3 1 DH112 608.4 41.0 1.3 458.8 30.2 90.2 16.0 1.6 20.4 9.9 7.2 2.5 15.2 17.7 2.5 2.3 128 604 E3 1 DH113 776.2 36.8 1.3 593.9 33.1 106.4 17.2 1.5 21.1 11.1 6.2 2.2 14.2 16.4 2.7 2.2 129 605 E3 1 DH114 622.4 41.2 1.3 492.0 31.7 78.4 17.1 1.7 23.7 9.9 7.3 0.9 14.4 15.3 2.1 2.7 128 606 E3 1 DH115 551.7 37.0 1.1 494.8 30.9 103.9 15.9 1.6 20.5 9.8 7.2 1.9 14.4 16.3 2.3 2.3 127 607 E3 1 DH116 780.0 30.9 1.1 740.0 35.2 103.3 12.9 1.6 16.0 8.4 6.5 3.0 12.4 15.4 2.4 2.0 123 608 E3 1 DH117 827.6 37.4 1.3 619.9 34.3 92.7 13.8 1.7 18.1 8.3 7.0 1.5 14.3 15.8 2.3 2.4 124 609 E3 1 DH119 715.5 44.8 1.5 475.1 32.5 104.0 20.8 2.0 33.0 10.3 7.5 1.8 15.7 17.5 2.3 2.5 128 610 E3 1 DH120 941.8 35.8 1.2 812.6 32.5 100.7 17.4 1.6 21.8 11.0 6.7 2.9 14.4 17.3 2.6 2.1 130 611 E3 1 DH121 737.3 39.8 1.3 547.4 30.0 92.1 16.6 1.7 22.2 9.8 6.7 2.7 13.8 16.5 2.4 2.4 131 612 E3 1 DH122 635.9 52.3 1.6 386.8 30.3 95.3 18.9 1.7 25.0 11.3 7.4 0.7 15.8 16.5 2.2 3.2 131 613 E3 1 DH123 571.2 49.3 1.5 379.6 29.2 103.0 17.5 1.2 16.0 15.2 7.8 1.2 14.9 16.1 2.1 3.1 128 614 E3 1 DH124 744.5 40.7 1.3 558.5 31.0 96.7 18.6 1.6 23.7 11.5 7.6 2.4 15.5 17.9 2.4 2.3 131 615 E3 1 DH125 789.9 38.8 1.4 577.0 33.2 89.8 15.7 1.8 22.0 8.9 6.5 1.5 13.9 15.4 2.4 2.5 128 616 E3 1 DH126 797.2 49.7 1.6 506.2 31.9 87.8 17.9 1.9 26.2 9.7 7.9 1.1 16.9 18.0 2.3 2.8 130 617 E3 1 DH128 567.3 40.2 1.1 510.1 29.4 103.3 21.3 1.7 28.9 12.5 6.8 2.0 15.2 17.2 2.5 2.3 131 618 E3 1 DH129 888.1 49.3 1.6 550.9 30.9 100.8 19.2 1.6 23.8 12.3 7.2 1.6 14.6 16.2 2.3 3.0 127 619 E3 1 DH130 698.6 38.7 1.2 565.7 32.4 96.0 16.2 1.7 21.6 9.6 7.0 2.0 14.3 16.3 2.3 2.4 128 620 E3 1 DH131 659.9 36.4 1.3 520.1 35.2 107.1 17.4 1.9 26.4 9.1 7.7 3.9 15.8 19.7 2.6 1.9 134 171 Table E.1 Continued. No. Env. Rep. Name GYLD GPS GWPS SPSM TGW PHT FLL FLW FLA FLS SL SSN FSN TSN SC GSP HD 621 E3 2 DH1 854.9 45.4 1.4 590.4 30.8 99.1 14.1 1.5 16.2 9.7 6.5 0.6 14.9 15.4 2.4 3.1 124 622 E3 2 DH3 508.4 37.2 1.2 427.6 31.2 106.5 18.6 1.9 28.2 9.8 6.3 1.9 13.7 15.6 2.5 2.7 130 623 E3 2 DH4 763.1 41.2 1.3 607.5 31.8 90.5 16.4 1.7 22.4 9.5 6.6 1.9 14.9 16.8 2.5 2.8 129 624 E3 2 DH5 758.5 40.8 1.2 628.4 31.2 92.6 17.2 1.7 23.0 10.2 7.6 1.7 15.0 16.7 2.2 2.7 130 625 E3 2 DH6 818.4 49.7 1.6 500.2 32.1 99.1 16.4 1.6 20.7 10.3 7.5 0.8 15.6 16.4 2.2 3.2 122 626 E3 2 DH7 780.7 39.2 1.2 643.1 31.3 87.9 17.9 1.8 25.1 10.4 6.5 3.1 14.4 17.5 2.7 2.7 132 627 E3 2 DH8 731.5 47.9 1.4 535.9 31.7 101.8 17.8 1.5 21.6 11.8 7.2 1.8 14.7 16.4 2.3 3.3 127 628 E3 2 DH9 382.1 51.6 1.7 222.9 34.0 95.6 18.1 1.8 25.6 10.1 8.1 2.1 18.0 20.1 2.5 2.9 131 629 E3 2 DH11 548.8 48.1 1.8 310.2 36.1 113.3 19.4 1.5 23.6 12.6 8.6 1.6 15.5 17.1 2.0 3.1 127 630 E3 2 DH12 619.9 47.0 1.5 413.0 32.5 87.8 18.5 2.0 28.8 9.4 7.0 1.1 15.2 16.3 2.3 3.1 128 631 E3 2 DH13 668.9 53.1 1.5 437.2 28.4 97.2 18.3 1.7 25.0 10.6 7.4 1.9 17.1 19.0 2.6 3.0 129 632 E3 2 DH14 608.4 40.0 1.4 446.7 33.4 93.5 18.4 1.7 24.9 10.7 7.0 2.0 14.9 16.9 2.4 2.7 129 633 E3 2 DH15 853.9 38.6 1.3 668.1 33.1 96.3 14.1 1.6 18.2 8.9 7.3 1.0 14.6 15.6 2.2 2.6 122 634 E3 2 DH16 478.5 45.2 1.6 307.1 32.7 94.7 16.5 1.7 22.4 9.6 6.8 1.1 13.9 15.0 2.2 3.2 125 635 E3 2 DH17 707.3 48.0 1.7 410.5 35.2 100.3 16.9 1.6 22.0 10.3 8.1 1.1 15.2 16.3 2.0 3.1 125 636 E3 2 DH18 438.9 43.8 1.3 342.0 . 96.7 19.1 2.2 32.6 8.8 7.8 1.3 16.7 18.0 2.3 2.6 136 637 E3 2 DH19 788.0 41.7 1.5 527.8 34.1 106.5 15.9 1.7 21.6 9.2 6.5 0.9 13.1 14.0 2.2 3.2 127 638 E3 2 DH20 579.4 44.4 1.3 434.0 29.7 89.0 20.6 1.8 29.1 11.5 6.9 1.6 14.5 16.1 2.3 3.1 131 639 E3 2 DH21 777.6 40.3 1.3 577.7 32.7 104.1 19.5 1.7 25.8 11.7 7.2 1.9 13.9 15.8 2.2 2.9 129 640 E3 2 DH22 646.4 51.1 1.6 396.8 33.3 115.2 17.9 1.9 27.1 9.3 7.5 1.7 16.3 18.0 2.4 3.1 129 641 E3 2 DH23 855.6 41.0 1.3 654.6 31.7 104.1 17.6 1.7 23.4 10.5 6.3 1.8 12.7 14.5 2.3 3.2 124 642 E3 2 DH24 603.8 40.5 1.3 452.9 32.3 101.7 16.3 1.6 20.2 10.7 6.9 0.6 14.8 15.4 2.2 2.7 122 643 E3 2 DH25 744.2 44.0 1.3 573.8 32.6 97.3 18.1 1.6 23.4 11.1 7.1 1.8 15.3 17.1 2.4 2.9 126 644 E3 2 DH26 813.8 42.4 1.3 617.9 31.8 85.1 17.9 1.7 23.7 11.0 6.8 1.3 14.2 15.5 2.3 3.0 129 645 E3 2 DH27 754.6 42.3 1.3 581.8 30.2 105.3 20.5 1.7 27.3 12.1 6.6 1.9 14.0 15.9 2.4 3.0 130 646 E3 2 DH28 714.5 45.6 1.4 503.5 32.4 105.5 17.3 1.7 23.1 10.3 6.6 1.7 14.0 15.8 2.4 3.1 129 647 E3 2 DH29 892.5 53.0 1.8 496.4 32.1 82.3 15.7 1.7 20.8 9.4 8.1 1.3 16.2 17.5 2.2 3.2 128 648 E3 2 DH30 594.3 46.6 1.6 360.4 34.1 98.6 18.4 1.8 27.0 10.0 7.2 1.3 15.3 16.6 2.3 3.0 128 649 E3 2 DH31 809.0 42.2 1.3 623.2 30.6 97.4 17.2 1.7 23.2 10.1 7.5 1.9 15.2 17.1 2.3 2.8 131 650 E3 2 DH32 597.0 39.3 1.1 542.2 25.5 91.9 15.2 1.7 20.3 9.0 6.9 1.7 14.9 16.6 2.4 2.6 134 651 E3 2 DH33 510.1 51.6 1.4 354.0 29.6 99.3 18.9 1.9 29.0 9.8 7.2 2.4 16.5 18.9 2.6 3.1 136 652 E3 2 DH34 855.9 51.8 1.7 490.8 32.4 105.2 19.3 1.8 27.6 10.7 7.9 1.1 16.2 17.3 2.2 3.2 125 653 E3 2 DH35 610.7 37.6 1.3 476.7 33.4 101.2 17.5 1.9 25.8 9.4 7.2 1.9 14.1 16.0 2.2 2.7 128 654 E3 2 DH36 764.6 43.4 1.5 502.7 32.0 93.3 18.9 1.9 27.8 10.2 8.2 1.3 16.3 17.6 2.1 2.7 130 655 E3 2 DH37 484.1 50.4 1.5 320.6 31.7 98.6 18.4 1.6 23.1 11.6 7.5 1.7 15.8 17.5 2.3 3.2 128 656 E3 2 DH38 750.2 45.9 1.6 483.4 . 90.7 18.0 1.8 25.1 10.2 7.5 0.9 14.7 15.6 2.1 3.2 121 657 E3 2 DH39 765.8 40.3 1.3 592.3 31.3 104.0 15.7 1.7 21.0 9.4 6.0 1.8 13.8 15.6 2.6 2.9 128 658 E3 2 DH40 419.3 40.9 1.3 318.6 32.8 111.6 17.7 1.6 21.8 11.7 7.6 0.8 15.7 16.5 2.2 2.6 126 659 E3 2 DH41 861.3 41.2 1.3 671.3 31.8 99.6 17.4 1.7 22.8 10.5 6.9 1.5 14.5 16.0 2.3 2.8 130 660 E3 2 DH42 856.9 45.0 1.5 574.4 31.7 102.8 17.1 1.5 20.7 11.2 7.2 1.8 15.8 17.6 2.5 2.8 129 661 E3 2 DH43 673.8 43.4 1.4 471.9 32.1 98.8 17.4 1.6 22.6 10.5 7.0 2.2 14.4 16.6 2.4 3.0 129 662 E3 2 DH44 571.5 40.8 1.4 395.8 35.7 104.4 19.9 1.7 27.6 11.4 7.6 1.8 14.4 16.2 2.1 2.9 131 663 E3 2 DH45 578.9 36.9 1.4 410.9 35.5 96.9 15.2 1.7 20.6 8.8 7.1 2.0 14.4 16.4 2.3 2.6 127 664 E3 2 DH46 592.5 46.8 1.4 430.9 32.4 99.4 20.9 1.9 31.0 11.2 8.2 1.7 15.4 17.1 2.1 3.0 131 665 E3 2 DH47 649.0 45.0 1.4 478.3 33.7 90.4 17.4 1.6 22.7 10.7 7.4 1.6 15.1 16.7 2.3 2.9 130 666 E3 2 DH49 628.5 46.7 1.5 420.1 32.9 98.2 19.4 1.7 26.3 11.4 7.5 1.9 14.8 16.7 2.2 3.1 131 667 E3 2 DH50 641.7 41.5 1.6 411.9 37.8 108.0 15.7 1.6 19.5 10.0 6.8 1.5 13.8 15.3 2.2 3.0 125 668 E3 2 DH51 649.8 44.2 1.3 506.4 29.8 98.5 19.8 1.7 26.4 11.7 7.6 2.3 15.4 17.8 2.4 2.8 130 669 E3 2 DH52 847.6 45.9 1.4 621.9 31.8 88.4 14.6 1.4 16.7 10.3 7.0 1.3 14.4 15.7 2.2 3.2 128 670 E3 2 DH53 882.1 41.1 1.3 670.8 30.1 85.1 16.5 1.8 23.9 9.1 6.7 1.7 14.5 16.2 2.4 2.9 130 671 E3 2 DH54 720.2 47.1 1.7 425.9 35.9 100.4 16.0 1.9 23.6 8.6 7.6 1.1 14.5 15.6 2.0 3.2 124 672 E3 2 DH55 926.0 39.6 1.2 801.0 29.8 105.6 16.0 1.6 20.2 10.0 6.4 1.9 14.3 16.2 2.5 2.8 127 673 E3 2 DH56 984.3 35.4 1.2 847.8 35.1 101.4 13.9 1.5 16.8 9.1 6.1 1.6 13.0 14.6 2.4 2.7 125 674 E3 2 DH57 788.3 40.5 1.3 600.4 31.5 95.8 15.2 1.6 19.6 9.3 6.0 1.3 14.0 15.3 2.5 2.9 128 675 E3 2 DH58 880.2 38.0 1.3 694.7 33.2 101.2 16.0 1.5 19.0 10.6 6.3 2.3 12.8 15.1 2.4 3.0 126 676 E3 2 DH59 932.2 42.8 1.4 659.8 33.2 95.5 16.1 1.5 19.6 10.5 6.5 0.6 13.3 13.9 2.1 3.2 123 677 E3 2 DH60 809.4 49.9 1.6 518.2 . 102.0 17.4 1.7 23.0 10.6 7.6 1.5 14.6 16.1 2.1 3.4 127 678 E3 2 DH61 502.1 37.4 1.2 416.3 32.1 90.8 17.0 1.5 20.5 11.4 7.3 2.1 14.5 16.6 2.3 2.6 130 679 E3 2 DH62 746.0 39.8 1.2 597.8 32.6 93.5 19.4 1.7 25.5 11.8 6.4 1.6 12.9 14.5 2.3 3.1 127 680 E3 2 DH63 833.0 51.8 1.8 450.7 34.6 91.0 17.1 1.5 20.4 11.6 8.3 0.8 15.5 16.3 2.0 3.3 124 681 E3 2 DH64 923.7 42.7 1.5 603.7 38.6 109.5 16.7 1.7 22.3 9.9 7.5 1.6 14.9 16.5 2.2 2.9 126 682 E3 2 DH65 808.8 58.2 1.8 444.6 30.7 99.0 17.7 1.6 22.3 11.1 8.2 0.4 17.0 17.4 2.1 3.4 124 172 Table E.1 Continued. No. Env. Rep. Name GYLD GPS GWPS SPSM TGW PHT FLL FLW FLA FLS SL SSN FSN TSN SC GSP HD 683 E3 2 DH66 510.4 42.2 1.4 355.2 35.4 102.0 20.4 2.2 35.1 9.4 7.9 2.2 15.7 17.9 2.3 2.7 133 684 E3 2 DH67 818.6 40.5 1.4 568.5 33.4 99.0 17.9 1.7 23.4 10.8 6.8 1.8 14.9 16.7 2.5 2.7 126 685 E3 2 DH68 570.4 38.9 1.2 460.4 28.3 93.1 18.7 1.7 25.0 11.1 7.0 2.0 14.0 16.0 2.3 2.8 127 686 E3 2 DH69 820.1 41.5 1.4 578.4 32.7 93.5 15.9 1.6 20.0 10.0 6.3 0.9 13.4 14.3 2.3 3.1 125 687 E3 2 DH70 687.0 43.1 1.1 607.9 32.3 99.8 17.2 1.7 23.6 9.9 7.4 2.0 15.8 17.8 2.4 2.7 131 688 E3 2 DH71 481.2 47.1 1.5 331.0 31.6 93.3 20.2 1.9 29.9 10.9 7.6 1.0 15.3 16.3 2.2 3.1 131 689 E3 2 DH72 824.6 43.9 1.4 569.8 32.4 96.9 15.7 1.7 21.7 9.0 7.5 1.7 16.0 17.7 2.4 2.7 130 690 E3 2 DH73 692.3 40.4 1.3 529.7 31.4 94.9 16.8 1.8 23.9 9.4 6.6 1.9 14.5 16.4 2.5 2.8 130 691 E3 2 DH75 877.8 37.0 1.3 685.7 35.5 96.7 15.5 1.9 23.5 8.1 6.9 1.6 13.7 15.3 2.2 2.7 121 692 E3 2 DH76 983.3 34.0 1.1 873.3 32.6 92.7 17.3 1.7 22.5 10.5 6.4 2.0 12.8 14.8 2.3 2.6 129 693 E3 2 DH77 550.0 40.8 1.3 426.3 32.6 102.9 24.4 1.6 30.0 15.8 7.2 1.7 15.0 16.7 2.3 2.7 127 694 E3 2 DH78 895.1 52.2 1.8 503.1 35.5 102.6 16.7 1.6 21.3 10.4 7.6 0.8 16.2 17.0 2.2 3.2 124 695 E3 2 DH79 974.9 46.5 1.6 620.5 . 104.5 16.7 1.5 19.8 11.0 7.0 1.8 14.3 16.1 2.3 3.2 126 696 E3 2 DH80 748.9 51.7 1.6 458.1 31.7 100.4 19.1 1.6 24.2 12.0 7.0 1.0 16.1 17.1 2.4 3.2 130 697 E3 2 DH81 593.1 41.4 1.3 454.5 32.6 90.2 17.8 1.8 25.5 9.9 6.3 0.7 14.5 15.2 2.4 2.9 128 698 E3 2 DH82 705.3 41.9 1.4 491.5 33.5 101.1 14.8 1.6 18.6 9.4 7.4 1.1 14.1 15.2 2.1 3.0 125 699 E3 2 DH83 847.2 46.9 1.6 516.3 34.9 95.0 17.9 1.6 22.3 11.4 7.4 1.7 14.6 16.3 2.2 3.2 127 700 E3 2 DH84 1031.6 37.0 1.4 752.4 37.6 97.4 13.8 1.4 15.1 10.1 7.2 1.5 13.0 14.5 2.0 2.8 122 701 E3 2 DH85 841.7 35.1 1.2 695.6 32.6 91.8 16.3 1.5 19.7 10.7 6.3 1.8 12.8 14.6 2.3 2.7 129 702 E3 2 DH86 795.7 42.4 1.4 556.0 33.3 100.4 18.0 1.5 21.6 11.9 6.2 1.4 14.0 15.4 2.5 3.0 125 703 E3 2 DH87 993.9 52.8 1.5 648.3 29.4 97.6 16.8 1.5 20.1 11.1 7.4 0.1 15.9 16.0 2.2 3.3 122 704 E3 2 DH89 751.7 35.4 1.2 615.7 33.1 102.6 17.1 1.7 22.8 10.2 6.5 2.3 14.0 16.3 2.5 2.5 127 705 E3 2 DH90 531.0 42.5 1.4 375.8 32.1 90.7 15.0 1.6 19.2 9.3 7.2 1.9 14.5 16.4 2.3 2.9 125 706 E3 2 DH91 738.6 42.0 1.4 523.4 32.9 101.5 16.7 1.5 19.8 11.2 6.4 1.8 14.0 15.8 2.5 3.0 128 707 E3 2 DH92 822.9 41.2 1.4 599.8 32.2 94.3 17.7 1.7 23.4 10.6 7.1 1.2 14.0 15.2 2.2 2.9 127 708 E3 2 DH93 966.8 58.1 1.9 520.1 31.2 91.5 15.2 1.6 19.1 9.6 7.5 0.9 15.9 16.8 2.2 3.6 124 709 E3 2 DH94 913.9 40.7 1.3 696.6 32.0 103.6 14.3 1.6 18.2 8.9 7.1 1.5 14.9 16.3 2.3 2.7 129 710 E3 2 DH95 984.6 36.4 1.2 791.5 36.6 96.7 15.0 1.6 19.1 9.8 7.4 1.7 13.9 15.6 2.1 2.6 122 711 E3 2 DH96 905.2 39.2 1.4 629.0 37.9 104.4 17.5 1.5 21.3 11.3 6.5 1.7 12.7 14.4 2.2 3.1 126 712 E3 2 DH97 714.0 39.5 1.3 534.1 35.4 105.4 15.7 1.8 22.5 8.7 6.9 1.5 14.9 16.4 2.4 2.6 126 713 E3 2 DH98 878.0 48.7 1.8 487.5 37.9 88.6 17.8 1.8 25.2 10.0 8.2 0.3 15.0 15.3 1.9 3.2 119 714 E3 2 DH99 657.1 49.9 1.5 435.1 31.1 91.5 17.9 1.9 27.4 9.3 7.2 0.9 16.2 17.1 2.4 3.1 131 715 E3 2 DH100 681.8 56.0 1.6 430.9 29.1 86.2 15.4 1.6 19.4 9.8 7.1 1.3 16.0 17.3 2.5 3.5 126 716 E3 2 DH101 685.4 53.2 1.6 434.6 30.7 96.9 17.5 1.7 23.6 10.2 7.7 0.5 16.0 16.5 2.1 3.3 124 717 E3 2 DH102 772.5 50.6 1.4 538.7 28.4 104.5 17.8 1.7 23.9 10.5 8.2 1.9 17.8 19.7 2.4 2.8 130 718 E3 2 DH103 552.8 37.5 1.2 453.8 30.0 88.0 17.7 1.9 26.0 9.5 6.6 2.3 13.8 16.1 2.4 2.7 132 719 E3 2 DH104 608.5 45.8 1.4 436.2 29.8 100.8 19.4 1.5 22.8 13.1 7.0 1.2 15.9 17.1 2.4 2.9 131 720 E3 2 DH105 964.3 42.5 1.3 721.2 30.4 102.7 17.4 1.6 22.6 10.7 6.2 2.2 14.3 16.5 2.7 3.0 127 721 E3 2 DH106 696.4 52.2 1.6 422.3 31.0 96.1 17.4 1.8 25.5 9.4 7.7 1.6 16.0 17.6 2.3 3.3 128 722 E3 2 DH107 848.7 37.8 1.2 702.0 30.6 103.3 15.6 1.5 18.9 10.2 6.7 1.7 13.4 15.1 2.3 2.8 125 723 E3 2 DH108 659.8 38.4 1.2 536.4 30.5 105.6 14.9 1.6 18.5 9.5 6.4 1.9 14.0 15.9 2.5 2.7 124 724 E3 2 DH109 724.0 40.3 1.2 588.6 29.1 95.5 17.1 1.6 21.6 10.7 7.4 1.9 14.7 16.6 2.3 2.7 128 725 E3 2 DH110 749.9 45.9 1.5 497.6 . 101.7 16.4 1.6 21.1 10.1 7.9 1.3 14.9 16.2 2.0 3.1 127 726 E3 2 DH111 696.0 50.1 1.6 436.9 30.1 104.5 19.2 1.7 25.6 11.5 8.1 1.2 15.9 17.1 2.1 3.1 127 727 E3 2 DH112 479.4 43.2 1.4 353.8 31.9 98.1 16.0 1.6 20.7 9.9 7.2 2.1 15.1 17.1 2.3 2.9 127 728 E3 2 DH113 856.8 46.8 1.7 499.0 36.5 113.5 16.2 1.5 19.1 11.2 7.1 1.7 14.7 16.4 2.3 3.2 127 729 E3 2 DH114 797.9 40.6 1.3 634.3 31.9 87.3 16.5 1.6 21.4 10.2 7.3 1.4 14.6 16.0 2.2 2.8 126 730 E3 2 DH115 544.1 37.0 1.1 508.0 32.3 101.0 16.5 1.6 20.5 10.6 7.0 2.0 13.7 15.7 2.2 2.7 127 731 E3 2 DH116 836.0 34.3 1.2 702.5 35.8 101.6 15.5 1.6 19.3 9.9 6.6 2.4 12.5 14.9 2.3 2.7 126 732 E3 2 DH117 932.5 34.0 1.2 775.8 35.4 91.8 14.8 1.7 19.5 9.0 7.1 1.2 14.0 15.2 2.2 2.4 124 733 E3 2 DH119 698.4 46.6 1.6 429.5 31.7 106.3 19.4 1.8 28.5 10.6 7.8 1.8 16.3 18.1 2.3 2.9 128 734 E3 2 DH120 863.9 38.5 1.2 691.7 33.3 94.7 17.9 1.6 22.9 11.1 6.6 2.4 14.2 16.6 2.5 2.7 129 735 E3 2 DH121 608.3 42.2 1.4 444.6 31.8 97.9 15.1 1.7 20.9 8.7 7.0 2.7 14.7 17.4 2.5 2.9 131 736 E3 2 DH122 620.7 46.0 1.3 477.5 31.5 97.1 19.4 1.5 22.8 13.5 7.2 1.3 15.8 17.1 2.4 2.9 131 737 E3 2 DH123 623.2 40.3 1.2 510.4 28.4 99.8 19.2 1.5 22.9 12.8 7.5 1.6 14.4 16.0 2.1 2.8 127 738 E3 2 DH124 637.1 40.1 1.4 466.8 31.4 90.3 19.5 1.7 26.0 11.7 7.7 1.9 14.9 16.8 2.2 2.7 131 739 E3 2 DH125 857.7 43.2 1.4 602.3 33.8 93.7 17.1 1.8 24.4 9.6 6.7 0.9 14.0 14.9 2.2 3.1 127 740 E3 2 DH126 794.6 43.7 1.4 583.8 31.1 85.8 18.1 1.7 24.1 11.2 7.5 1.2 16.3 17.5 2.3 2.7 130 741 E3 2 DH128 429.9 36.6 1.0 445.1 29.8 98.4 21.2 1.6 27.4 13.0 6.7 2.4 14.2 16.6 2.5 2.6 131 742 E3 2 DH129 854.7 46.5 1.5 568.3 32.8 104.2 16.4 1.5 19.9 10.8 7.2 1.8 14.7 16.5 2.3 3.2 126 743 E3 2 DH130 929.4 44.6 1.4 660.1 31.2 94.3 17.5 1.6 22.4 10.9 8.0 1.3 16.3 17.6 2.2 2.7 126 744 E3 2 DH131 402.2 41.3 1.4 283.0 34.3 99.1 18.9 2.1 31.0 9.2 7.8 3.0 15.8 18.8 2.4 2.6 132 173 Table E.1 Continued. No. Env. Rep. Name GYLD GPS GWPS SPSM TGW PHT FLL FLW FLA FLS SL SSN FSN TSN SC GSP HD 745 E4 1 DH1 671.4 37.0 1.0 671.4 28.8 72.4 12.5 1.3 12.4 9.9 6.0 0.5 12.7 13.2 2.2 2.9 139 746 E4 1 DH3 523.2 36.3 1.0 519.0 28.5 76.0 16.8 1.5 19.6 11.5 6.0 1.2 12.7 13.9 2.3 2.8 142 747 E4 1 DH4 601.8 27.9 0.8 753.2 30.0 71.8 13.9 1.6 17.6 8.6 6.4 2.3 13.1 15.4 2.5 2.1 142 748 E4 1 DH5 633.9 38.6 1.0 608.9 29.0 75.1 15.1 1.5 17.9 10.1 7.5 1.4 13.7 15.1 2.0 2.8 142 749 E4 1 DH6 665.9 33.9 1.0 667.9 31.1 79.3 13.8 1.2 13.1 11.5 6.6 1.6 13.5 15.1 2.3 2.5 136 750 E4 1 DH7 554.1 34.0 0.9 592.6 30.0 67.4 15.4 1.5 18.3 10.3 6.1 2.2 12.7 14.9 2.4 2.7 142 751 E4 1 DH8 671.1 37.7 1.0 669.1 30.0 84.0 15.7 1.5 18.7 10.4 6.6 1.6 12.9 14.5 2.2 2.9 139 752 E4 1 DH9 572.6 38.9 1.1 529.2 31.0 75.7 15.2 1.5 17.8 10.3 6.6 2.0 14.1 16.1 2.4 2.7 142 753 E4 1 DH11 744.5 46.4 1.5 485.0 32.3 95.9 14.9 1.3 15.4 11.4 8.7 1.3 15.9 17.2 2.0 2.9 139 754 E4 1 DH12 538.7 37.9 1.0 527.1 28.4 62.0 11.5 1.7 15.6 6.7 5.7 0.6 14.1 14.7 2.6 2.7 141 755 E4 1 DH13 635.0 44.3 1.2 530.5 27.0 81.4 15.9 1.4 17.5 11.4 6.9 1.3 14.8 16.1 2.3 3.0 140 756 E4 1 DH14 677.1 42.3 1.4 491.8 31.2 81.3 15.1 1.5 18.4 9.8 7.3 0.9 15.4 16.3 2.2 2.8 139 757 E4 1 DH15 696.0 39.2 1.1 653.5 28.4 76.3 13.2 1.2 13.1 10.6 6.9 0.8 14.6 15.4 2.2 2.7 137 758 E4 1 DH16 489.9 35.4 0.9 544.3 28.9 70.1 16.1 1.3 17.1 12.0 6.2 1.5 13.2 14.7 2.4 2.7 139 759 E4 1 DH17 705.5 38.1 1.2 594.8 33.4 80.6 15.3 1.3 15.3 12.3 6.9 2.0 13.4 15.4 2.2 2.8 136 760 E4 1 DH18 621.3 45.5 1.3 475.8 29.1 78.5 17.4 1.9 25.6 9.4 7.7 1.1 16.2 17.3 2.2 2.8 142 761 E4 1 DH19 617.6 35.5 1.0 597.3 31.5 72.1 13.9 1.5 17.0 9.0 6.3 1.2 12.9 14.1 2.2 2.7 138 762 E4 1 DH20 483.6 40.9 1.1 448.2 26.2 73.9 18.1 1.6 22.2 11.7 6.4 1.3 14.1 15.4 2.4 2.9 143 763 E4 1 DH21 746.5 36.9 1.1 668.9 29.2 85.2 15.7 1.4 18.0 10.9 7.0 2.2 13.9 16.1 2.3 2.6 141 764 E4 1 DH22 696.7 40.9 1.2 581.5 29.6 87.7 14.8 1.5 17.5 9.9 6.8 0.9 14.5 15.4 2.3 2.8 141 765 E4 1 DH23 648.3 38.5 1.0 667.7 25.9 78.8 14.9 1.1 13.0 13.6 6.1 1.6 13.3 14.9 2.5 2.9 138 766 E4 1 DH24 615.4 34.6 0.9 672.6 27.1 74.4 16.1 1.6 20.9 9.8 6.1 1.2 12.8 14.0 2.3 2.7 138 767 E4 1 DH25 639.5 39.9 1.1 599.3 30.1 71.9 15.6 1.4 17.9 10.9 6.9 1.5 14.1 15.6 2.3 2.8 139 768 E4 1 DH26 707.5 40.8 1.1 643.8 27.9 73.2 13.6 1.4 14.6 10.0 6.6 0.2 14.3 14.5 2.2 2.9 139 769 E4 1 DH27 571.6 33.4 1.0 591.7 28.2 74.5 17.4 1.4 19.2 12.4 6.0 2.1 12.8 14.9 2.5 2.6 142 770 E4 1 DH28 621.1 38.6 1.1 558.5 28.9 80.5 15.5 1.4 17.0 11.1 6.2 1.4 13.5 14.9 2.4 2.8 141 771 E4 1 DH29 683.6 35.8 1.0 673.5 29.2 69.1 12.6 1.4 14.3 8.8 6.6 1.4 13.1 14.5 2.2 2.7 141 772 E4 1 DH30 568.5 32.5 1.0 544.5 31.6 73.8 17.1 1.5 19.9 11.7 6.8 1.6 13.9 15.5 2.3 2.3 142 773 E4 1 DH31 562.7 40.5 1.1 524.9 26.2 75.8 14.4 1.4 15.5 10.6 7.0 1.3 14.4 15.7 2.2 2.8 140 774 E4 1 DH32 570.6 34.2 0.9 657.3 24.5 75.5 15.3 1.4 17.7 10.6 6.1 1.9 13.1 15.0 2.5 2.6 143 775 E4 1 DH33 506.9 43.1 0.9 591.5 26.3 84.1 15.6 1.7 21.0 9.2 7.0 2.1 15.9 18.0 2.6 2.7 144 776 E4 1 DH34 765.8 48.6 1.4 544.7 28.1 83.4 18.3 1.5 22.3 11.9 7.8 1.0 16.0 17.0 2.2 3.0 140 777 E4 1 DH35 595.9 30.2 0.9 678.6 29.3 71.0 13.9 1.5 16.1 9.6 6.0 1.6 11.7 13.3 2.2 2.6 139 778 E4 1 DH36 741.5 37.5 1.1 679.7 28.6 79.2 16.0 1.6 20.2 10.1 7.3 1.3 14.4 15.7 2.1 2.6 141 779 E4 1 DH37 590.1 39.9 1.0 597.8 25.8 79.0 17.7 1.4 19.8 12.5 6.6 1.5 13.5 15.0 2.3 3.0 142 780 E4 1 DH38 553.7 41.5 1.2 471.2 29.9 76.0 14.1 1.4 15.1 10.4 7.1 0.6 16.0 16.6 2.4 2.6 138 781 E4 1 DH39 658.6 39.8 1.1 605.3 27.6 72.7 16.4 1.4 18.1 11.9 5.6 0.8 13.3 14.1 2.5 3.0 139 782 E4 1 DH40 542.7 33.6 1.0 546.5 28.3 87.5 12.9 1.3 13.5 10.0 6.8 1.1 13.6 14.7 2.2 2.5 140 783 E4 1 DH41 660.5 37.4 1.0 683.0 28.2 85.3 14.3 1.4 15.5 10.4 6.6 1.2 14.2 15.4 2.3 2.6 140 784 E4 1 DH42 637.9 43.5 1.3 492.9 29.5 80.3 15.6 1.3 16.4 11.7 6.9 0.3 15.3 15.6 2.3 2.8 139 785 E4 1 DH43 769.0 41.6 1.2 647.3 30.6 71.8 14.6 1.3 15.2 11.1 6.5 1.0 13.2 14.2 2.2 3.1 139 786 E4 1 DH44 724.6 32.6 1.1 685.5 33.8 89.4 17.5 1.5 21.5 11.3 7.0 2.1 12.8 14.9 2.2 2.5 142 787 E4 1 DH45 603.6 26.8 0.9 653.3 34.2 78.8 13.1 1.4 14.4 9.5 6.6 2.1 13.0 15.1 2.3 2.0 139 788 E4 1 DH46 579.0 40.7 1.1 538.1 29.0 85.9 14.9 1.5 17.4 10.1 7.9 1.4 15.1 16.5 2.1 2.7 141 789 E4 1 DH47 726.0 41.1 1.1 639.1 29.4 82.8 12.9 1.4 14.3 9.2 6.8 1.2 13.4 14.6 2.2 3.1 141 790 E4 1 DH49 634.1 37.3 1.0 624.8 29.5 83.2 15.7 1.5 19.0 10.3 7.0 2.2 14.1 16.3 2.4 2.6 142 791 E4 1 DH50 596.1 35.6 1.0 589.7 28.7 81.8 12.9 1.3 13.7 9.7 6.5 1.0 13.8 14.8 2.3 2.6 141 792 E4 1 DH51 602.5 42.6 1.0 601.3 26.3 77.4 15.1 1.4 16.9 10.7 6.7 1.7 13.5 15.2 2.3 3.1 141 793 E4 1 DH52 725.7 39.9 0.9 774.5 23.8 72.4 14.8 1.3 15.9 11.1 6.6 0.5 14.8 15.3 2.3 2.7 138 794 E4 1 DH53 647.2 37.8 1.0 618.8 27.9 70.4 13.7 1.4 15.1 9.9 6.8 0.8 14.5 15.3 2.3 2.6 139 795 E4 1 DH54 663.5 45.6 1.5 448.6 31.6 76.6 15.6 1.6 19.5 9.9 7.8 0.6 14.8 15.4 2.0 3.1 139 796 E4 1 DH55 695.3 40.0 1.1 624.7 27.9 81.4 13.4 1.3 14.2 10.1 6.3 1.1 13.6 14.7 2.3 2.9 138 797 E4 1 DH56 638.6 39.5 1.2 544.9 30.4 76.6 13.3 1.3 14.0 9.9 6.3 1.0 13.6 14.6 2.3 2.9 139 798 E4 1 DH57 577.7 36.9 1.0 565.2 27.7 66.4 14.0 1.3 14.1 11.1 5.9 0.5 13.8 14.3 2.4 2.7 140 799 E4 1 DH58 723.0 39.0 1.1 679.5 27.9 78.1 12.8 1.3 13.0 9.9 6.4 1.3 12.6 13.9 2.2 3.1 138 800 E4 1 DH59 720.6 36.5 1.0 743.7 28.0 73.4 14.6 1.3 15.5 11.0 5.8 1.0 13.4 14.4 2.5 2.7 139 801 E4 1 DH60 681.5 39.6 1.1 595.2 30.6 75.8 14.3 1.4 15.8 10.3 6.7 1.5 12.8 14.3 2.1 3.1 139 802 E4 1 DH61 640.7 32.0 0.9 701.0 27.8 74.9 13.1 1.5 15.1 8.9 6.8 1.7 13.2 14.9 2.2 2.4 141 803 E4 1 DH62 666.2 42.1 1.2 577.3 29.0 74.2 14.9 1.4 15.9 11.1 6.1 0.4 12.9 13.3 2.2 3.3 138 804 E4 1 DH63 638.5 49.9 1.5 436.1 29.5 81.0 15.8 1.6 20.2 9.7 8.2 1.3 15.5 16.8 2.1 3.2 139 805 E4 1 DH64 739.1 35.9 1.1 655.8 32.4 84.3 15.8 1.4 18.1 10.9 6.9 1.5 13.8 15.3 2.2 2.6 139 806 E4 1 DH65 529.2 44.6 1.1 475.9 27.6 74.9 17.3 1.4 18.9 12.6 7.4 1.1 15.4 16.5 2.2 2.9 139 174 Table E.1 Continued. No. Env. Rep. Name GYLD GPS GWPS SPSM TGW PHT FLL FLW FLA FLS SL SSN FSN TSN SC GSP HD 807 E4 1 DH66 439.7 34.0 1.1 399.3 31.3 74.1 17.4 1.7 22.9 10.4 7.0 2.0 14.0 16.0 2.3 2.4 142 808 E4 1 DH67 588.6 35.7 1.0 586.3 31.4 71.5 14.3 1.4 16.0 10.4 6.1 0.9 13.5 14.4 2.3 2.6 138 809 E4 1 DH68 438.6 38.0 1.0 455.9 25.4 75.9 16.0 1.6 20.5 10.0 6.7 0.8 14.1 14.9 2.2 2.6 139 810 E4 1 DH69 642.1 42.4 1.0 652.6 28.6 69.9 15.0 1.5 17.8 10.0 6.0 0.8 13.6 14.4 2.4 3.1 140 811 E4 1 DH70 512.3 35.3 1.0 527.1 29.5 77.8 16.0 1.4 17.4 11.6 6.7 1.9 13.7 15.6 2.3 2.6 142 812 E4 1 DH71 683.9 39.6 1.1 605.2 30.2 76.9 14.0 1.5 16.3 9.5 7.5 0.9 14.9 15.8 2.1 2.6 141 813 E4 1 DH72 575.8 37.7 0.9 632.8 26.2 76.8 14.4 1.5 17.2 9.6 6.8 1.5 14.4 15.9 2.4 2.6 142 814 E4 1 DH73 479.5 34.2 0.9 524.1 26.6 72.7 16.4 1.4 18.7 11.5 6.4 1.6 13.7 15.3 2.4 2.5 142 815 E4 1 DH75 634.8 44.7 1.3 474.5 33.3 82.9 14.6 1.3 15.2 11.4 7.7 1.1 15.6 16.7 2.2 2.9 137 816 E4 1 DH76 659.1 34.1 1.0 690.2 29.3 72.1 14.3 1.4 15.9 10.2 6.2 1.2 13.0 14.2 2.3 2.6 139 817 E4 1 DH77 573.3 34.4 1.1 545.5 30.2 70.3 20.1 1.3 21.4 14.9 7.3 1.7 13.8 15.5 2.1 2.5 140 818 E4 1 DH78 659.7 40.0 1.2 566.8 30.4 75.5 17.9 1.3 19.0 13.3 7.1 1.7 14.3 16.0 2.3 2.8 137 819 E4 1 DH79 761.2 49.7 1.5 523.5 31.0 84.4 16.0 1.3 17.2 12.0 7.3 1.0 14.5 15.5 2.1 3.4 139 820 E4 1 DH80 733.9 52.6 1.4 511.8 28.2 82.4 15.6 1.4 17.2 11.2 7.0 0.3 15.4 15.7 2.2 3.4 139 821 E4 1 DH81 520.4 35.8 1.0 508.7 30.3 66.0 15.2 1.7 20.4 9.1 6.6 1.1 14.1 15.2 2.3 2.5 140 822 E4 1 DH82 659.4 34.0 0.9 740.8 26.6 77.1 12.9 1.4 14.2 9.3 6.4 1.7 12.5 14.2 2.2 2.7 139 823 E4 1 DH83 619.2 35.1 1.2 532.9 33.4 83.8 14.6 1.3 15.5 11.0 6.1 1.9 12.4 14.3 2.3 2.8 141 824 E4 1 DH84 747.9 36.0 1.1 661.3 30.7 81.0 11.6 1.2 11.3 9.5 6.7 1.4 13.2 14.6 2.2 2.7 136 825 E4 1 DH85 665.8 35.7 1.0 683.6 28.4 72.4 13.8 1.4 15.2 9.9 6.6 1.1 13.6 14.7 2.2 2.6 139 826 E4 1 DH86 635.8 39.8 1.1 572.2 29.2 79.4 13.2 1.3 13.2 10.4 6.1 0.8 13.8 14.6 2.4 2.9 139 827 E4 1 DH87 742.9 44.4 1.1 651.1 26.3 81.4 15.7 1.4 17.6 11.3 6.6 0.6 14.7 15.3 2.3 3.0 137 828 E4 1 DH89 587.0 37.6 1.1 526.0 30.8 77.3 13.4 1.3 14.2 10.1 6.5 1.4 14.7 16.1 2.5 2.6 139 829 E4 1 DH90 545.1 35.3 1.0 534.4 30.0 74.5 14.8 1.4 15.8 11.1 6.7 2.3 13.8 16.1 2.4 2.5 137 830 E4 1 DH91 559.8 40.4 1.1 497.6 28.8 82.1 15.2 1.3 15.6 11.9 6.3 1.4 13.9 15.3 2.4 2.9 139 831 E4 1 DH92 663.3 36.4 0.9 711.7 27.9 76.3 13.4 1.3 14.2 10.1 6.3 1.1 13.1 14.2 2.3 2.8 140 832 E4 1 DH93 635.5 50.9 1.3 486.6 27.3 72.6 16.5 1.4 18.6 11.5 7.5 0.6 16.1 16.7 2.2 3.2 139 833 E4 1 DH94 748.6 35.1 1.0 736.1 29.8 84.4 12.3 1.3 13.2 9.1 6.6 1.3 14.0 15.3 2.3 2.5 138 834 E4 1 DH95 674.1 35.4 1.1 630.0 32.3 73.5 15.6 1.5 18.8 10.4 7.2 1.6 14.0 15.6 2.2 2.5 138 835 E4 1 DH96 605.7 32.7 1.1 568.2 33.0 72.9 14.8 1.3 15.4 11.4 6.3 1.1 11.7 12.8 2.0 2.8 138 836 E4 1 DH97 672.5 36.9 1.2 580.8 31.6 79.0 14.7 1.6 18.7 9.2 6.3 1.1 13.4 14.5 2.3 2.7 139 837 E4 1 DH98 726.1 42.4 1.3 578.1 32.0 80.6 14.2 1.4 15.4 10.4 7.3 0.9 14.4 15.3 2.1 2.9 136 838 E4 1 DH99 671.3 39.2 1.1 601.0 29.5 77.2 16.2 1.5 19.2 10.9 6.6 0.6 13.8 14.4 2.2 2.8 141 839 E4 1 DH100 685.6 48.0 1.2 594.6 26.8 71.5 15.3 1.5 18.1 10.2 6.8 0.6 15.1 15.7 2.3 3.2 139 840 E4 1 DH101 696.2 44.8 1.1 634.7 27.7 79.2 13.7 1.4 15.3 9.7 7.2 0.5 14.8 15.3 2.1 3.0 138 841 E4 1 DH102 562.4 44.5 1.0 576.9 25.2 79.6 16.8 1.5 19.5 11.5 7.4 1.0 14.7 15.7 2.1 3.0 142 842 E4 1 DH103 535.9 33.4 0.9 623.9 27.8 77.8 15.8 1.7 21.1 9.3 6.9 2.2 13.7 15.9 2.3 2.4 142 843 E4 1 DH104 576.8 39.6 1.0 575.1 25.2 79.9 18.1 1.4 19.9 13.1 6.1 1.3 13.2 14.5 2.4 3.0 142 844 E4 1 DH105 687.7 39.6 1.0 665.7 28.2 77.2 16.0 1.4 17.3 11.7 5.8 1.4 13.3 14.7 2.6 3.0 140 845 E4 1 DH106 655.2 42.8 1.1 582.9 28.7 76.6 14.8 1.5 18.1 9.7 7.1 1.3 14.8 16.1 2.3 2.9 139 846 E4 1 DH107 612.6 36.0 1.0 609.6 28.2 75.7 13.0 1.3 13.1 10.2 6.5 0.8 12.8 13.6 2.1 2.8 139 847 E4 1 DH108 585.9 36.3 1.0 602.8 27.4 76.4 15.6 1.3 15.5 12.3 5.9 1.1 12.6 13.7 2.3 2.9 139 848 E4 1 DH109 . 38.0 1.1 . 26.8 72.1 15.5 1.4 17.6 10.7 7.0 1.2 14.1 15.3 2.2 2.7 139 849 E4 1 DH110 677.7 41.1 1.2 570.4 28.6 78.5 13.7 1.2 13.1 11.3 7.6 0.8 14.4 15.2 2.0 2.9 137 850 E4 1 DH111 583.4 43.8 1.1 529.4 27.0 79.0 16.1 1.4 17.6 11.7 7.2 1.1 14.1 15.2 2.1 3.1 139 851 E4 1 DH112 589.1 45.0 1.3 460.9 30.7 75.9 15.3 1.3 16.1 11.7 7.0 0.9 14.7 15.6 2.2 3.1 138 852 E4 1 DH113 692.1 33.2 0.9 752.3 32.8 80.2 13.5 1.4 15.1 9.6 5.9 1.6 12.9 14.5 2.5 2.5 139 853 E4 1 DH114 509.3 34.6 0.9 576.8 27.2 62.7 13.8 1.5 16.6 9.1 6.5 0.7 13.2 13.9 2.1 2.6 140 854 E4 1 DH115 576.7 36.8 1.1 537.4 29.0 74.1 13.1 1.3 13.0 10.5 6.7 1.4 13.6 15.0 2.2 2.6 139 855 E4 1 DH116 515.0 31.2 0.9 554.3 30.8 72.7 13.6 1.3 14.1 10.4 6.1 1.7 12.1 13.8 2.3 2.6 140 856 E4 1 DH117 495.7 33.0 1.0 505.8 30.3 64.8 13.1 1.4 14.3 9.6 6.5 0.9 12.8 13.7 2.1 2.6 138 857 E4 1 DH119 517.3 39.9 1.2 445.6 28.9 81.3 16.7 1.5 20.4 10.8 7.2 1.8 15.1 16.9 2.3 2.6 142 858 E4 1 DH120 581.5 35.1 1.0 589.2 30.4 70.1 14.9 1.3 15.9 11.0 6.0 1.1 12.8 13.9 2.3 2.7 141 859 E4 1 DH121 738.8 36.2 1.1 697.7 29.4 77.9 15.2 1.5 17.6 10.5 6.8 1.8 13.6 15.4 2.3 2.7 141 860 E4 1 DH122 672.2 39.3 1.0 674.4 26.6 84.7 15.2 1.4 16.6 11.0 6.4 1.7 14.1 15.8 2.5 2.8 142 861 E4 1 DH123 610.7 44.2 1.1 544.8 26.3 81.9 15.5 1.2 14.2 13.5 7.2 0.9 14.4 15.3 2.1 3.1 142 862 E4 1 DH124 677.6 37.1 1.0 646.6 28.0 72.4 16.3 1.4 18.0 11.8 7.6 1.2 14.2 15.4 2.0 2.6 141 863 E4 1 DH125 677.6 39.7 1.2 576.7 30.1 70.2 13.6 1.5 16.2 9.1 6.7 1.1 13.5 14.6 2.2 2.9 140 864 E4 1 DH126 637.4 41.3 1.1 601.8 27.4 72.4 15.2 1.6 18.6 9.8 7.4 1.1 15.4 16.5 2.2 2.7 142 865 E4 1 DH128 594.2 40.7 1.0 585.4 27.0 81.8 17.7 1.3 18.6 13.4 6.2 1.5 13.8 15.3 2.5 2.9 140 866 E4 1 DH129 593.2 40.9 1.2 510.5 29.3 79.5 13.9 1.4 15.1 10.3 6.6 1.5 13.3 14.8 2.3 3.1 140 867 E4 1 DH130 691.5 37.5 1.0 670.7 28.3 76.3 14.2 1.6 17.7 9.1 6.8 1.4 13.0 14.4 2.1 2.9 139 868 E4 1 DH131 574.1 36.0 1.1 515.3 32.8 80.1 17.1 1.7 22.5 10.3 7.3 2.5 14.3 16.8 2.3 2.5 142 175 Table E.1 Continued. No. Env. Rep. Name GYLD GPS GWPS SPSM TGW PHT FLL FLW FLA FLS SL SSN FSN TSN SC GSP HD 869 E4 2 DH1 591.6 44.5 1.2 486.9 29.0 72.6 13.4 1.3 13.8 10.2 6.4 0.2 13.9 14.1 2.2 3.2 139 870 E4 2 DH3 584.3 41.6 1.2 491.4 27.5 77.0 15.7 1.5 18.9 10.4 6.2 0.6 13.6 14.2 2.3 3.1 141 871 E4 2 DH4 495.7 31.8 0.9 555.8 29.0 70.9 12.6 1.5 15.3 8.3 6.1 1.5 13.1 14.6 2.4 2.4 141 872 E4 2 DH5 541.2 42.7 1.2 442.0 27.3 71.5 13.8 1.5 16.9 9.0 7.5 0.7 14.2 14.9 2.0 3.0 141 873 E4 2 DH6 614.4 41.7 1.2 504.5 30.5 75.8 16.1 1.4 17.4 11.9 6.6 1.2 14.1 15.3 2.3 3.0 137 874 E4 2 DH7 575.8 35.3 0.9 616.5 28.6 72.6 15.8 1.6 20.4 9.8 6.5 2.7 14.3 17.0 2.6 2.5 143 875 E4 2 DH8 697.5 45.5 1.2 561.1 28.5 77.4 16.8 1.4 18.8 11.9 7.1 0.6 13.8 14.4 2.0 3.3 140 876 E4 2 DH9 557.6 44.4 1.4 403.2 30.6 75.0 13.9 1.5 16.2 9.5 7.2 1.5 15.8 17.3 2.4 2.8 142 877 E4 2 DH11 577.7 38.2 1.3 451.7 32.9 85.5 17.5 1.4 19.1 12.8 7.7 2.0 14.2 16.2 2.1 2.7 137 878 E4 2 DH12 379.3 39.4 1.1 355.8 27.4 52.7 12.4 1.6 15.6 7.9 6.1 0.6 13.7 14.3 2.4 2.9 142 879 E4 2 DH13 539.5 46.0 1.2 459.6 25.8 77.1 17.8 1.6 22.0 11.4 7.0 1.3 15.1 16.4 2.4 3.0 142 880 E4 2 DH14 703.3 40.9 1.3 555.5 30.2 78.0 15.2 1.5 17.9 10.3 7.3 1.3 15.3 16.6 2.3 2.7 140 881 E4 2 DH15 592.8 38.2 1.1 545.3 27.6 75.6 12.6 1.3 12.7 10.0 6.8 0.7 14.5 15.2 2.3 2.6 137 882 E4 2 DH16 580.0 52.4 1.5 398.1 28.7 74.2 15.4 1.5 17.7 10.5 7.4 0.4 16.0 16.4 2.2 3.3 137 883 E4 2 DH17 665.4 44.5 1.5 453.0 32.8 76.1 13.4 1.2 13.1 10.9 7.2 0.9 14.4 15.3 2.1 3.1 137 884 E4 2 DH18 553.9 48.1 1.4 406.0 28.7 74.0 18.1 1.9 26.8 9.7 7.7 0.6 16.1 16.7 2.2 3.0 142 885 E4 2 DH19 558.4 37.4 1.1 500.8 31.4 73.2 12.7 1.5 14.5 8.7 6.4 1.1 12.7 13.8 2.2 2.9 137 886 E4 2 DH20 607.5 48.6 1.3 464.1 26.7 74.7 15.5 1.6 19.6 9.7 7.1 1.0 15.5 16.5 2.3 3.1 142 887 E4 2 DH21 612.6 38.6 1.2 500.5 29.6 78.7 16.9 1.5 19.5 11.5 7.1 2.1 13.9 16.0 2.3 2.8 142 888 E4 2 DH22 621.2 46.8 1.4 457.5 30.3 86.8 15.0 1.5 18.0 9.9 7.3 1.3 15.9 17.2 2.3 2.9 142 889 E4 2 DH23 656.7 46.8 1.3 497.5 26.9 79.9 16.3 1.3 16.9 12.5 6.8 0.6 14.9 15.5 2.3 3.1 140 890 E4 2 DH24 679.4 44.0 1.2 558.2 27.2 79.5 13.2 1.5 16.1 8.6 6.7 0.5 14.2 14.7 2.2 3.1 139 891 E4 2 DH25 588.4 36.8 1.0 561.4 32.0 69.7 14.1 1.4 15.4 10.3 6.4 1.8 12.8 14.6 2.3 2.8 139 892 E4 2 DH26 578.6 40.7 1.1 518.0 27.7 77.0 17.1 1.5 20.0 11.6 7.0 1.4 14.7 16.1 2.3 2.8 140 893 E4 2 DH27 566.0 40.2 1.1 503.1 27.4 75.1 15.6 1.4 16.9 11.4 6.1 1.3 13.2 14.5 2.4 3.0 142 894 E4 2 DH28 680.7 42.2 1.3 523.2 29.7 81.4 13.9 1.3 15.0 10.4 6.5 1.1 13.6 14.7 2.3 3.1 140 895 E4 2 DH29 626.3 35.5 1.0 599.9 29.2 69.0 15.2 1.4 16.3 11.2 7.0 1.2 13.2 14.4 2.1 2.7 140 896 E4 2 DH30 704.1 38.0 1.3 556.6 31.2 78.0 17.4 1.4 19.9 12.1 6.9 0.9 15.2 16.1 2.3 2.5 140 897 E4 2 DH31 495.4 43.4 1.1 434.2 28.3 74.9 15.0 1.5 18.2 9.8 7.3 1.4 14.4 15.8 2.2 3.0 142 898 E4 2 DH32 520.6 37.4 0.9 564.0 23.5 78.2 12.5 1.4 13.5 9.2 6.4 1.2 14.4 15.6 2.4 2.6 143 899 E4 2 DH33 573.3 45.5 1.1 509.6 26.8 85.2 16.3 1.6 20.9 10.1 6.8 1.5 15.8 17.3 2.5 2.9 145 900 E4 2 DH34 667.0 47.8 1.3 522.3 28.3 83.8 13.3 1.3 13.5 10.4 7.3 0.9 15.9 16.8 2.3 2.9 139 901 E4 2 DH35 552.5 36.8 1.1 482.1 31.2 72.6 14.0 1.5 16.2 9.6 6.9 0.8 13.6 14.4 2.1 2.7 138 902 E4 2 DH36 652.8 39.9 1.2 558.4 30.3 75.1 15.7 1.6 20.0 9.8 7.5 1.0 14.8 15.8 2.1 2.7 141 903 E4 2 DH37 550.2 44.7 1.2 478.0 29.4 78.7 15.4 1.2 15.0 12.6 6.8 1.1 14.4 15.5 2.3 3.1 140 904 E4 2 DH38 622.8 43.2 1.3 488.4 28.9 73.2 15.9 1.4 17.4 11.5 7.5 0.7 16.3 17.0 2.3 2.6 138 905 E4 2 DH39 611.7 38.3 1.2 511.4 29.7 71.1 12.8 1.2 12.3 10.7 5.5 0.9 12.7 13.6 2.5 3.0 139 906 E4 2 DH40 524.8 35.3 1.0 504.1 29.7 81.8 16.4 1.5 19.2 11.0 7.1 0.8 14.0 14.8 2.1 2.5 142 907 E4 2 DH41 720.2 41.3 1.1 631.8 28.9 83.1 16.0 1.5 19.6 10.4 7.2 1.1 14.6 15.7 2.2 2.8 142 908 E4 2 DH42 602.7 38.6 1.1 537.7 28.7 78.3 16.2 1.4 17.7 11.7 6.5 1.1 13.9 15.0 2.3 2.8 140 909 E4 2 DH43 649.7 31.9 0.9 686.8 29.9 72.7 14.6 1.3 15.1 11.2 5.8 1.6 11.8 13.4 2.3 2.7 139 910 E4 2 DH44 751.4 35.2 1.2 641.7 31.8 86.8 17.1 1.4 19.5 11.8 7.2 1.3 13.3 14.6 2.0 2.6 142 911 E4 2 DH45 593.1 30.0 1.0 578.0 35.2 75.3 12.8 1.3 13.4 9.7 6.3 1.7 12.1 13.8 2.2 2.5 139 912 E4 2 DH46 528.6 37.2 1.1 501.5 29.5 80.7 17.0 1.5 19.8 11.6 7.3 1.6 13.8 15.4 2.1 2.7 141 913 E4 2 DH47 690.0 43.2 1.2 558.3 29.5 79.4 12.8 1.4 13.8 9.4 6.9 0.9 14.7 15.6 2.3 2.9 141 914 E4 2 DH49 585.8 40.3 1.1 524.5 31.1 80.4 15.2 1.3 16.2 11.4 6.7 1.4 13.8 15.2 2.3 2.9 141 915 E4 2 DH50 578.8 31.9 1.0 603.5 29.3 81.9 12.9 1.4 13.8 9.5 6.1 1.3 12.7 14.0 2.3 2.5 140 916 E4 2 DH51 564.7 43.4 1.0 575.7 27.9 78.9 15.9 1.5 18.3 10.9 6.9 2.4 14.9 17.3 2.5 2.9 142 917 E4 2 DH52 720.3 38.2 0.9 759.0 25.2 67.7 13.8 1.3 13.7 10.9 6.3 0.8 13.7 14.5 2.3 2.8 139 918 E4 2 DH53 534.4 41.2 1.1 469.6 29.6 65.0 13.4 1.4 14.5 10.0 6.7 0.7 14.8 15.5 2.3 2.8 141 919 E4 2 DH54 570.0 35.8 1.1 501.8 31.2 72.8 16.8 1.4 19.2 11.7 7.1 1.7 13.3 15.0 2.1 2.7 139 920 E4 2 DH55 596.8 43.4 1.2 495.7 27.7 78.4 12.9 1.3 13.6 9.8 6.5 0.9 14.7 15.6 2.4 3.0 139 921 E4 2 DH56 632.9 40.3 1.3 494.9 30.2 76.6 14.1 1.4 16.3 9.8 6.6 1.2 13.4 14.6 2.2 3.0 140 922 E4 2 DH57 551.5 38.6 1.1 486.8 28.5 74.1 12.8 1.4 14.2 9.2 6.0 0.7 13.9 14.6 2.4 2.8 139 923 E4 2 DH58 632.5 41.0 1.1 570.4 28.5 77.6 15.9 1.4 18.2 11.1 6.7 1.4 13.3 14.7 2.2 3.1 139 924 E4 2 DH59 757.5 38.0 1.1 678.7 29.3 71.8 13.9 1.3 14.6 10.5 6.4 1.4 14.0 15.4 2.4 2.7 139 925 E4 2 DH60 607.4 43.3 1.3 458.0 30.6 70.4 16.2 1.5 19.2 11.1 6.9 1.0 13.5 14.5 2.1 3.2 140 926 E4 2 DH61 605.5 38.4 1.1 560.1 28.1 75.8 15.6 1.5 18.8 10.2 7.3 1.2 14.6 15.8 2.2 2.6 142 927 E4 2 DH62 514.4 40.6 1.2 440.4 28.9 64.5 14.6 1.3 14.8 11.5 6.0 0.2 12.5 12.7 2.1 3.2 138 928 E4 2 DH63 539.9 52.6 1.5 351.9 29.2 78.3 15.5 1.4 17.0 11.3 8.5 0.8 16.2 17.0 2.0 3.2 139 929 E4 2 DH64 764.0 42.2 1.4 563.8 33.8 85.8 17.3 1.5 20.8 11.4 7.4 1.1 14.5 15.6 2.1 2.9 138 930 E4 2 DH65 587.2 55.8 1.5 389.7 27.2 74.8 16.8 1.3 17.9 12.6 8.3 0.7 17.0 17.7 2.1 3.3 138 176 Table E.1 Continued. No. Env. Rep. Name GYLD GPS GWPS SPSM TGW PHT FLL FLW FLA FLS SL SSN FSN TSN SC GSP HD 931 E4 2 DH66 600.6 41.2 1.2 494.7 32.6 76.2 16.5 1.6 21.4 10.1 7.6 1.9 15.4 17.3 2.3 2.7 143 932 E4 2 DH67 586.4 35.7 1.1 544.0 31.7 68.7 13.0 1.2 12.5 10.7 6.3 0.9 13.6 14.5 2.3 2.6 138 933 E4 2 DH68 389.7 36.4 0.9 435.9 25.0 72.8 16.5 1.5 20.1 10.8 6.6 0.8 14.0 14.8 2.2 2.6 139 934 E4 2 DH69 585.2 43.2 1.2 484.4 29.7 64.9 14.2 1.4 15.2 10.5 6.6 0.9 14.3 15.2 2.3 3.0 140 935 E4 2 DH70 582.2 34.7 1.1 548.8 30.2 77.9 15.4 1.4 16.9 11.2 6.8 1.8 14.4 16.2 2.4 2.4 142 936 E4 2 DH71 531.8 36.0 1.1 479.1 30.7 72.6 14.9 1.4 16.9 10.5 6.6 0.9 12.9 13.8 2.1 2.7 142 937 E4 2 DH72 578.4 41.6 1.1 515.1 27.0 77.1 15.5 1.5 19.1 10.0 7.2 1.2 15.6 16.8 2.3 2.7 142 938 E4 2 DH73 637.7 43.9 1.3 508.2 27.3 78.9 13.7 1.5 16.2 9.2 7.2 1.1 15.4 16.5 2.3 2.8 141 939 E4 2 DH75 725.4 41.6 1.3 551.2 33.2 81.8 15.3 1.4 17.1 10.9 7.5 1.2 14.9 16.1 2.2 2.8 137 940 E4 2 DH76 531.9 38.2 1.1 493.0 29.0 68.4 14.0 1.4 15.5 10.0 6.4 0.8 13.1 13.9 2.2 2.9 140 941 E4 2 DH77 599.9 40.6 1.3 478.8 31.8 72.3 14.2 1.2 13.0 12.2 7.9 1.3 15.8 17.1 2.2 2.6 140 942 E4 2 DH78 736.2 44.9 1.3 572.1 31.7 73.1 16.0 1.3 16.1 12.6 7.3 1.1 15.1 16.2 2.2 3.0 137 943 E4 2 DH79 643.6 39.1 1.1 563.6 30.7 79.4 14.2 1.3 14.7 10.8 6.5 2.0 12.8 14.8 2.3 3.0 137 944 E4 2 DH80 660.4 45.3 1.4 484.5 29.6 77.9 15.2 1.3 16.2 11.4 6.7 0.8 14.1 14.9 2.2 3.2 139 945 E4 2 DH81 525.3 39.9 1.2 444.4 30.4 72.2 14.6 1.6 18.4 9.5 6.8 0.5 14.9 15.4 2.3 2.7 140 946 E4 2 DH82 567.5 40.6 1.2 490.1 29.0 74.2 13.7 1.4 14.8 10.0 6.9 0.9 14.2 15.1 2.2 2.8 138 947 E4 2 DH83 615.3 27.2 0.9 681.4 33.5 77.7 13.8 1.2 13.3 11.3 5.5 2.7 10.5 13.2 2.4 2.6 141 948 E4 2 DH84 617.8 38.8 1.3 488.8 31.5 75.9 12.9 1.2 11.9 11.1 7.2 0.9 13.4 14.3 2.0 2.9 136 949 E4 2 DH85 662.2 40.1 1.2 560.7 28.9 66.6 17.7 1.5 21.2 11.8 6.5 0.7 13.6 14.3 2.2 2.9 140 950 E4 2 DH86 647.5 41.5 1.1 582.8 30.3 81.4 13.9 1.3 14.1 10.9 6.1 0.9 13.8 14.7 2.4 3.0 140 951 E4 2 DH87 581.6 40.3 1.1 512.8 29.0 71.0 13.4 1.3 13.9 10.3 6.2 0.7 13.1 13.8 2.2 3.1 139 952 E4 2 DH89 588.1 33.1 1.0 578.8 31.8 78.6 13.1 1.3 14.0 9.7 6.0 1.4 12.6 14.0 2.3 2.6 141 953 E4 2 DH90 488.7 43.9 1.3 364.5 29.4 71.0 13.6 1.2 12.7 11.7 7.5 1.3 15.8 17.1 2.3 2.7 137 954 E4 2 DH91 671.3 39.2 1.1 588.3 29.8 84.0 14.3 1.3 15.2 10.7 6.4 1.2 14.1 15.3 2.4 2.8 139 955 E4 2 DH92 518.8 31.4 0.9 560.9 27.7 66.6 13.4 1.3 13.6 10.5 6.0 1.7 12.0 13.7 2.3 2.6 140 956 E4 2 DH93 559.0 49.5 1.3 423.2 26.8 69.1 14.3 1.3 14.8 11.0 7.4 0.7 15.6 16.3 2.2 3.2 139 957 E4 2 DH94 662.9 38.9 1.1 579.4 29.8 79.9 13.3 1.3 13.3 10.6 6.9 1.1 15.1 16.2 2.3 2.6 139 958 E4 2 DH95 587.8 32.8 1.0 579.7 32.6 71.4 12.3 1.3 12.8 9.6 6.5 1.5 12.4 13.9 2.2 2.6 139 959 E4 2 DH96 719.5 35.4 1.2 601.1 33.0 77.2 14.6 1.4 16.0 10.6 6.5 1.5 12.5 14.0 2.2 2.8 136 960 E4 2 DH97 604.5 45.7 1.4 436.2 31.0 77.3 14.8 1.6 18.8 9.2 7.1 0.1 15.6 15.7 2.2 2.9 139 961 E4 2 DH98 566.3 32.4 1.1 518.6 33.0 67.9 13.8 1.3 13.9 10.9 6.4 1.3 12.2 13.5 2.1 2.7 137 962 E4 2 DH99 672.8 40.4 1.2 570.1 28.8 71.9 13.7 1.5 15.8 9.4 6.6 0.4 13.7 14.1 2.2 2.9 141 963 E4 2 DH100 475.9 45.9 1.3 378.3 26.8 65.9 11.4 1.2 11.2 9.3 7.1 1.0 15.2 16.2 2.3 3.0 138 964 E4 2 DH101 559.2 39.9 1.1 508.8 27.3 71.5 12.4 1.3 13.2 9.2 6.6 0.6 13.2 13.8 2.1 3.0 138 965 E4 2 DH102 510.9 42.8 1.1 454.1 26.8 74.0 14.8 1.4 16.3 10.7 7.1 1.3 13.7 15.0 2.1 3.1 142 966 E4 2 DH103 560.1 37.0 1.1 518.1 28.8 75.1 16.5 1.6 21.4 10.1 7.0 1.6 13.9 15.6 2.2 2.6 142 967 E4 2 DH104 691.0 45.5 1.2 583.1 27.4 83.9 16.2 1.4 18.2 11.4 6.9 1.3 15.0 16.3 2.4 3.0 142 968 E4 2 DH105 649.9 45.6 1.2 553.6 26.6 76.1 16.6 1.5 19.7 11.1 6.2 1.2 14.5 15.7 2.5 3.1 140 969 E4 2 DH106 561.9 47.3 1.3 428.9 27.8 74.3 13.7 1.6 17.4 8.6 6.9 0.8 14.6 15.4 2.2 3.2 140 970 E4 2 DH107 679.5 38.3 1.1 594.4 27.0 78.7 14.5 1.4 16.2 10.3 7.0 0.8 13.8 14.6 2.1 2.8 139 971 E4 2 DH108 725.8 42.6 1.3 572.9 28.4 85.2 14.3 1.2 13.9 11.7 7.0 1.4 15.0 16.4 2.3 2.8 137 972 E4 2 DH109 479.3 41.9 1.1 453.9 26.2 72.0 15.9 1.5 19.0 10.5 7.7 1.2 14.9 16.1 2.1 2.8 141 973 E4 2 DH110 638.3 43.3 1.3 481.7 29.7 77.4 14.9 1.2 14.0 12.9 7.4 0.8 14.6 15.4 2.1 3.0 137 974 E4 2 DH111 584.5 38.9 1.1 526.1 30.0 79.5 15.4 1.3 16.2 11.5 6.9 1.5 13.3 14.8 2.1 2.9 139 975 E4 2 DH112 566.3 38.7 1.1 497.2 30.2 73.5 12.1 1.3 12.9 9.0 6.6 1.5 13.6 15.1 2.3 2.8 139 976 E4 2 DH113 575.7 33.2 1.0 555.7 31.8 72.9 11.9 1.2 11.3 10.0 5.7 1.5 12.4 13.9 2.4 2.7 139 977 E4 2 DH114 526.9 40.5 1.2 447.7 27.2 63.2 13.1 1.5 15.1 9.1 7.2 0.6 14.3 14.9 2.1 2.8 140 978 E4 2 DH115 534.1 36.4 1.1 484.2 29.4 73.0 14.6 1.3 14.8 11.4 6.8 1.4 13.8 15.2 2.2 2.6 138 979 E4 2 DH116 657.9 33.1 1.0 650.7 32.1 78.7 12.6 1.3 13.1 9.7 6.3 1.7 12.7 14.4 2.3 2.6 139 980 E4 2 DH117 483.4 38.8 1.2 393.0 30.4 59.9 16.0 1.4 17.7 11.4 7.1 0.5 13.4 13.9 2.0 2.9 139 981 E4 2 DH119 589.3 49.1 1.5 390.3 29.1 86.8 18.7 1.7 25.8 10.7 7.7 0.6 16.0 16.6 2.2 3.1 141 982 E4 2 DH120 655.7 36.2 1.1 614.0 29.8 58.9 14.4 1.3 15.0 11.0 6.2 1.0 12.9 13.9 2.2 2.8 139 983 E4 2 DH121 614.8 38.2 1.1 548.4 29.3 71.8 15.0 1.5 17.3 10.3 6.9 1.3 13.8 15.1 2.2 2.8 141 984 E4 2 DH122 578.0 40.9 1.1 547.4 25.7 82.8 14.6 1.4 16.1 10.5 6.6 1.2 14.9 16.1 2.4 2.7 142 985 E4 2 DH123 541.9 41.6 1.1 506.5 26.3 80.5 14.9 1.3 15.3 11.7 7.1 1.3 14.3 15.6 2.2 2.9 140 986 E4 2 DH124 674.8 42.8 1.2 549.9 28.0 77.5 16.0 1.4 18.0 11.3 7.7 1.3 14.6 15.9 2.1 2.9 143 987 E4 2 DH125 628.6 35.5 1.0 656.1 29.8 69.7 12.9 1.4 14.7 9.0 5.9 1.3 12.6 13.9 2.4 2.8 139 988 E4 2 DH126 647.9 44.9 1.2 533.2 28.2 71.8 15.2 1.6 19.0 9.6 7.8 0.6 16.2 16.8 2.2 2.8 142 989 E4 2 DH128 574.6 46.0 1.2 480.0 25.4 71.6 16.5 1.5 19.3 11.3 6.4 1.4 14.0 15.4 2.4 3.3 142 990 E4 2 DH129 684.4 44.0 1.3 535.1 30.1 83.8 14.5 1.3 15.4 10.8 6.9 1.5 14.0 15.5 2.2 3.1 138 991 E4 2 DH130 575.3 42.5 1.2 480.2 27.2 73.1 14.5 1.5 17.1 9.7 7.3 0.9 14.5 15.4 2.1 2.9 140 992 E4 2 DH131 480.2 41.5 1.3 383.9 31.9 72.5 16.3 1.5 19.6 10.8 7.6 1.7 15.1 16.8 2.2 2.7 142 177 Table E.1 Continued. No. Env. Rep. Name GYLD GPS GWPS SPSM TGW PHT FLL FLW FLA FLS SL SSN FSN TSN SC GSP HD 993 E5 1 DH1 439.4 36.8 0.9 490.9 25.5 . . . . . 6.6 1.6 14.6 16.2 2.4 2.5 113 994 E5 1 DH3 593.4 37.9 1.0 584.0 29.0 . . . . . 6.9 2.8 14.9 17.7 2.6 2.5 119 995 E5 1 DH4 454.2 38.0 0.9 532.3 28.0 . . . . . 7.4 3.1 16.0 19.1 2.6 2.3 117 996 E5 1 DH5 601.4 33.7 0.8 737.9 24.5 . . . . . 7.4 3.5 14.2 17.7 2.4 2.4 116 997 E5 1 DH6 508.2 42.3 1.1 462.0 27.9 . . . . . 7.3 2.2 16.2 18.4 2.5 2.6 110 998 E5 1 DH7 459.0 41.9 1.1 426.1 25.3 . . . . . 6.9 3.3 16.6 19.9 2.9 2.5 123 999 E5 1 DH8 439.9 50.7 1.3 338.1 27.2 . . . . . 7.9 1.9 16.8 18.7 2.4 3.0 115 1000 E5 1 DH9 404.7 57.0 1.5 274.9 24.8 . . . . . 8.3 2.3 19.8 22.1 2.7 2.9 119 1001 E5 1 DH11 598.9 39.8 1.4 440.0 33.2 . . . . . 8.6 2.7 15.7 18.4 2.1 2.5 114 1002 E5 1 DH12 288.8 43.3 1.0 285.7 21.7 . . . . . 7.3 1.9 16.3 18.2 2.5 2.7 115 1003 E5 1 DH13 582.9 50.7 1.3 455.4 24.5 . . . . . 8.2 2.3 19.2 21.5 2.6 2.6 116 1004 E5 1 DH14 639.1 38.1 1.2 515.4 31.7 . . . . . 7.4 2.9 15.9 18.8 2.5 2.4 116 1005 E5 1 DH15 604.2 40.9 1.1 542.4 27.8 . . . . . 7.5 1.8 15.4 17.2 2.3 2.6 111 1006 E5 1 DH16 537.1 37.5 1.0 541.5 27.8 . . . . . 7.1 2.6 14.3 16.9 2.4 2.6 111 1007 E5 1 DH17 486.5 37.9 1.2 421.5 30.8 . . . . . 7.4 2.8 14.4 17.2 2.3 2.6 112 1008 E5 1 DH18 525.9 47.1 1.2 443.0 27.5 . . . . . 8.0 2.8 17.5 20.3 2.5 2.7 123 1009 E5 1 DH19 634.5 36.5 1.1 582.7 29.7 . . . . . 6.8 2.8 14.1 16.9 2.5 2.6 113 1010 E5 1 DH20 545.9 51.0 1.2 437.1 25.0 . . . . . 7.3 2.4 17.1 19.5 2.7 3.0 119 1011 E5 1 DH21 664.7 40.3 1.2 554.4 31.2 . . . . . 7.5 3.7 15.1 18.8 2.5 2.6 118 1012 E5 1 DH22 597.5 46.7 1.2 516.4 26.3 . . . . . 8.2 2.3 18.2 20.5 2.5 2.6 116 1013 E5 1 DH23 517.6 47.0 1.2 433.1 24.8 . . . . . 7.0 1.8 15.6 17.4 2.5 3.0 111 1014 E5 1 DH24 622.5 40.1 1.1 560.8 28.8 . . . . . 6.9 2.5 14.3 16.8 2.4 2.8 111 1015 E5 1 DH25 558.5 40.1 1.0 535.5 25.0 . . . . . 7.5 2.5 15.7 18.2 2.4 2.5 112 1016 E5 1 DH26 461.1 47.0 1.1 408.0 22.2 . . . . . 7.6 2.1 16.3 18.4 2.4 2.9 115 1017 E5 1 DH27 542.5 40.9 1.1 482.6 29.0 . . . . . 6.3 3.3 14.2 17.5 2.8 2.9 120 1018 E5 1 DH28 461.1 40.6 1.1 428.1 30.7 . . . . . 6.9 3.0 14.7 17.7 2.6 2.7 117 1019 E5 1 DH29 442.9 41.8 1.1 419.4 25.3 . . . . . 7.9 2.4 17.0 19.4 2.5 2.5 117 1020 E5 1 DH30 324.8 40.2 1.1 298.2 26.8 . . . . . 7.7 2.4 16.6 19.0 2.5 2.4 116 1021 E5 1 DH31 493.6 46.6 1.1 460.4 24.8 . . . . . 8.4 1.9 17.0 18.9 2.3 2.7 116 1022 E5 1 DH32 559.0 42.0 0.9 592.8 23.0 . . . . . 7.6 2.4 17.9 20.3 2.7 2.3 123 1023 E5 1 DH33 599.8 59.3 1.2 496.9 24.3 . . . . . 7.9 1.9 20.2 22.1 2.8 2.9 123 1024 E5 1 DH34 688.2 45.9 1.3 515.1 29.6 . . . . . 7.7 1.8 16.2 18.0 2.3 2.8 110 1025 E5 1 DH35 548.0 35.7 1.1 517.5 30.3 . . . . . 7.4 2.8 15.0 17.8 2.4 2.4 115 1026 E5 1 DH36 525.9 40.2 1.1 466.2 26.4 . . . . . 8.2 2.7 16.7 19.4 2.4 2.4 117 1027 E5 1 DH37 464.2 44.1 1.1 437.5 24.2 . . . . . 7.8 2.5 17.1 19.6 2.5 2.6 117 1028 E5 1 DH38 492.9 41.1 1.0 508.7 23.8 . . . . . 7.6 2.2 16.2 18.4 2.4 2.5 110 1029 E5 1 DH39 596.8 42.5 1.2 493.2 25.3 . . . . . 6.2 2.0 16.4 18.4 3.0 2.6 116 1030 E5 1 DH40 368.8 39.6 0.9 391.1 25.6 . . . . . 8.1 2.5 17.2 19.7 2.4 2.3 118 1031 E5 1 DH41 648.9 37.7 1.0 659.4 25.9 . . . . . 7.0 2.9 14.6 17.5 2.5 2.6 118 1032 E5 1 DH42 554.6 46.3 1.2 450.9 26.7 . . . . . 7.7 2.4 17.7 20.1 2.6 2.6 117 1033 E5 1 DH43 381.2 47.8 1.2 314.7 23.1 . . . . . 7.4 2.2 16.5 18.7 2.5 2.9 116 1034 E5 1 DH44 574.9 47.0 1.4 396.8 29.0 . . . . . 8.5 2.2 17.2 19.4 2.3 2.7 121 1035 E5 1 DH45 504.1 40.4 1.3 386.3 34.5 . . . . . 7.9 2.1 16.8 18.9 2.4 2.4 115 1036 E5 1 DH46 506.4 46.4 1.1 448.2 23.7 . . . . . 8.7 3.3 17.9 21.2 2.4 2.6 119 1037 E5 1 DH47 482.3 37.9 1.0 503.4 24.5 . . . . . 7.1 3.5 14.6 18.1 2.5 2.6 119 1038 E5 1 DH49 457.2 52.4 1.4 337.4 27.1 . . . . . 8.0 2.2 16.6 18.8 2.4 3.1 120 1039 E5 1 DH50 512.9 36.1 1.0 498.9 28.1 . . . . . 7.2 2.8 15.7 18.5 2.6 2.3 117 1040 E5 1 DH51 535.3 46.4 1.1 492.4 24.0 . . . . . 7.9 3.3 16.7 20.0 2.5 2.8 120 1041 E5 1 DH52 702.4 39.4 1.0 706.0 26.5 . . . . . 7.1 1.9 15.3 17.2 2.4 2.6 114 1042 E5 1 DH53 612.7 37.2 1.1 568.9 27.8 . . . . . 6.7 2.2 15.8 18.0 2.7 2.4 116 1043 E5 1 DH54 612.7 47.9 1.6 392.7 32.9 . . . . . 8.3 1.9 15.7 17.6 2.1 3.0 110 1044 E5 1 DH55 573.7 43.4 1.2 484.1 27.8 . . . . . 7.2 2.2 15.9 18.1 2.5 2.7 116 1045 E5 1 DH56 643.6 37.4 1.2 554.8 29.4 . . . . . 6.9 2.2 14.2 16.4 2.4 2.6 114 1046 E5 1 DH57 402.5 36.1 0.9 467.5 22.8 . . . . . 6.7 2.4 15.8 18.2 2.7 2.4 115 1047 E5 1 DH58 584.9 37.9 1.1 554.9 28.4 . . . . . 6.8 2.3 13.7 16.0 2.4 2.8 114 1048 E5 1 DH59 581.6 39.1 1.0 592.9 24.4 . . . . . 6.5 1.7 14.1 15.8 2.4 2.7 109 1049 E5 1 DH60 568.4 48.2 1.4 406.6 27.9 . . . . . 8.0 2.2 15.9 18.1 2.3 3.0 110 1050 E5 1 DH61 595.6 42.1 1.2 503.1 27.0 . . . . . 7.4 2.2 15.8 18.0 2.4 2.7 120 1051 E5 1 DH62 228.7 41.4 0.7 308.6 18.3 . . . . . 7.0 1.6 15.3 16.9 2.4 2.7 115 1052 E5 1 DH63 547.1 54.9 1.5 361.9 28.1 . . . . . 8.6 1.6 17.2 18.8 2.2 3.2 110 1053 E5 1 DH64 590.2 38.7 1.2 504.8 33.1 . . . . . 7.9 2.0 15.8 17.8 2.3 2.4 112 1054 E5 1 DH65 428.8 50.4 1.2 369.7 24.0 . . . . . 8.6 1.9 18.7 20.6 2.4 2.7 110 178 Table E.1 Continued. No. Env. Rep. Name GYLD GPS GWPS SPSM TGW PHT FLL FLW FLA FLS SL SSN FSN TSN SC GSP HD 1055 E5 1 DH66 382.9 47.7 1.2 321.8 24.4 . . . . . 8.1 3.1 18.4 21.5 2.6 2.6 121 1056 E5 1 DH67 492.9 41.1 1.1 467.2 27.9 . . . . . 7.2 1.9 15.8 17.7 2.5 2.6 114 1057 E5 1 DH68 322.2 39.9 0.9 340.2 24.3 . . . . . 7.6 2.0 15.6 17.6 2.3 2.5 115 1058 E5 1 DH69 468.5 47.4 1.0 463.0 22.6 . . . . . 7.1 1.5 15.8 17.3 2.5 3.0 113 1059 E5 1 DH70 554.6 47.3 1.3 411.7 27.5 . . . . . 7.7 2.8 17.3 20.1 2.6 2.7 120 1060 E5 1 DH71 529.4 50.5 1.3 400.4 27.8 . . . . . 8.5 2.1 17.8 19.9 2.3 2.8 119 1061 E5 1 DH72 491.2 48.9 1.2 401.0 25.0 . . . . . 8.1 2.7 17.4 20.1 2.5 2.8 118 1062 E5 1 DH73 514.6 48.7 1.2 426.3 24.2 . . . . . 7.8 2.4 16.9 19.3 2.5 2.9 118 1063 E5 1 DH75 730.8 43.6 1.3 543.8 31.7 . . . . . 7.8 1.6 15.7 17.3 2.2 2.8 110 1064 E5 1 DH76 621.8 40.7 1.2 500.2 29.2 . . . . . 7.0 2.1 15.6 17.7 2.5 2.6 117 1065 E5 1 DH77 632.3 39.3 1.2 513.2 30.6 . . . . . 8.6 2.8 17.2 20.0 2.3 2.3 114 1066 E5 1 DH78 602.8 48.5 1.4 422.7 29.5 . . . . . 8.1 1.5 17.2 18.7 2.3 2.8 113 1067 E5 1 DH79 583.5 40.7 1.2 468.7 29.6 . . . . . 7.5 2.8 15.7 18.5 2.5 2.6 115 1068 E5 1 DH80 559.8 45.9 1.3 423.8 28.2 . . . . . 7.3 2.1 17.5 19.6 2.7 2.6 116 1069 E5 1 DH81 404.2 39.7 1.0 387.5 27.0 . . . . . 7.4 1.8 17.3 19.1 2.6 2.3 115 1070 E5 1 DH82 559.8 39.3 1.0 573.0 26.0 . . . . . 7.4 2.3 15.0 17.3 2.4 2.6 109 1071 E5 1 DH83 678.2 35.0 1.2 560.5 33.8 . . . . . 7.1 3.9 14.1 18.0 2.6 2.4 116 1072 E5 1 DH84 688.7 39.8 1.3 547.0 31.0 . . . . . 7.7 1.8 15.2 17.0 2.2 2.6 109 1073 E5 1 DH85 606.0 40.9 1.2 501.2 30.2 . . . . . 7.3 2.6 15.6 18.2 2.5 2.6 115 1074 E5 1 DH86 587.6 41.5 1.2 508.8 29.2 . . . . . 6.7 2.0 15.4 17.4 2.6 2.7 114 1075 E5 1 DH87 485.9 51.4 1.3 380.5 25.5 . . . . . 7.3 0.9 16.9 17.8 2.4 3.0 109 1076 E5 1 DH89 529.4 35.5 1.1 495.9 31.7 . . . . . 7.0 2.9 15.1 18.0 2.6 2.5 116 1077 E5 1 DH90 481.7 38.7 1.2 414.6 29.7 . . . . . 7.5 3.0 15.7 18.7 2.5 2.4 108 1078 E5 1 DH91 428.3 41.9 1.2 349.6 29.7 . . . . . 7.2 2.7 15.7 18.4 2.6 2.8 115 1079 E5 1 DH92 508.2 40.0 1.1 468.8 28.9 . . . . . 7.5 1.9 15.6 17.5 2.3 2.6 115 1080 E5 1 DH93 454.7 53.4 1.3 350.9 22.6 . . . . . 8.2 1.3 18.0 19.3 2.3 3.0 110 1081 E5 1 DH94 563.2 40.3 1.2 485.9 28.9 . . . . . 7.6 2.6 16.2 18.8 2.5 2.5 117 1082 E5 1 DH95 527.1 37.7 0.9 593.6 24.5 . . . . . 7.7 2.3 15.0 17.3 2.2 2.5 108 1083 E5 1 DH96 837.8 40.1 1.5 572.7 35.2 . . . . . 7.2 2.2 14.1 16.3 2.3 2.8 111 1084 E5 1 DH97 581.0 46.8 1.4 410.3 31.8 . . . . . 7.9 1.5 17.7 19.2 2.4 2.6 114 1085 E5 1 DH98 545.9 50.2 1.4 389.9 28.0 . . . . . 8.2 1.4 15.5 16.9 2.1 3.2 107 1086 E5 1 DH99 501.6 47.7 1.2 427.3 26.5 . . . . . 7.3 1.9 17.4 19.3 2.6 2.7 120 1087 E5 1 DH100 534.1 48.0 1.1 472.6 25.4 . . . . . 7.5 3.0 17.2 20.2 2.7 2.8 114 1088 E5 1 DH101 550.6 48.8 1.3 421.2 27.3 . . . . . 7.9 1.9 16.4 18.3 2.3 3.0 114 1089 E5 1 DH102 661.6 54.3 1.3 516.5 24.4 . . . . . 8.7 2.6 19.0 21.6 2.5 2.8 121 1090 E5 1 DH103 584.0 42.5 1.3 456.2 28.7 . . . . . 7.8 2.4 16.4 18.8 2.4 2.6 123 1091 E5 1 DH104 753.5 48.8 1.2 613.1 25.8 . . . . . 7.1 2.8 17.7 20.5 2.9 2.7 120 1092 E5 1 DH105 721.1 40.3 1.1 652.5 27.7 . . . . . 6.7 3.0 15.3 18.3 2.7 2.6 116 1093 E5 1 DH106 575.0 38.7 1.1 525.6 28.8 . . . . . 7.8 2.8 15.9 18.7 2.4 2.4 116 1094 E5 1 DH107 537.5 36.9 1.1 506.1 27.9 . . . . . 7.4 2.5 14.5 17.0 2.3 2.5 113 1095 E5 1 DH108 473.7 36.2 1.0 483.4 28.4 . . . . . 6.7 3.0 15.3 18.3 2.7 2.4 116 1096 E5 1 DH109 502.9 40.2 1.1 455.1 27.8 . . . . . 8.2 2.9 16.5 19.4 2.4 2.4 117 1097 E5 1 DH110 432.1 46.1 1.3 331.9 29.7 . . . . . 8.8 2.0 17.9 19.9 2.3 2.6 113 1098 E5 1 DH111 444.7 48.8 1.4 328.7 28.3 . . . . . 8.8 2.2 17.8 20.0 2.3 2.7 114 1099 E5 1 DH112 373.7 40.3 1.1 328.1 27.7 . . . . . 7.2 3.2 15.8 19.0 2.7 2.5 117 1100 E5 1 DH113 545.3 39.7 1.2 442.9 29.4 . . . . . 6.7 2.3 14.5 16.8 2.5 2.7 115 1101 E5 1 DH114 552.5 43.6 1.2 453.2 26.2 . . . . . 8.1 1.5 16.7 18.2 2.3 2.6 115 1102 E5 1 DH115 494.7 39.9 1.1 447.3 29.4 . . . . . 7.5 2.3 15.6 17.9 2.4 2.5 115 1103 E5 1 DH116 663.2 34.7 1.1 615.2 31.5 . . . . . 7.0 2.8 13.7 16.5 2.3 2.5 114 1104 E5 1 DH117 535.3 40.4 1.1 484.2 26.3 . . . . . 7.9 1.2 15.7 16.9 2.2 2.6 113 1105 E5 1 DH119 651.1 46.2 1.4 481.9 31.9 . . . . . 8.8 2.3 17.6 19.9 2.3 2.6 115 1106 E5 1 DH120 620.3 38.7 1.0 605.8 27.3 . . . . . 6.6 3.2 15.1 18.3 2.8 2.6 119 1107 E5 1 DH121 527.5 38.2 1.1 475.3 28.3 . . . . . 7.2 4.2 15.0 19.2 2.7 2.5 120 1108 E5 1 DH122 535.0 48.5 1.1 472.6 23.1 . . . . . 7.4 1.9 17.1 19.0 2.6 2.9 120 1109 E5 1 DH123 609.8 39.5 1.1 565.7 25.5 . . . . . 7.3 2.6 15.5 18.1 2.5 2.5 117 1110 E5 1 DH124 679.2 46.1 1.2 552.2 25.4 . . . . . 8.2 2.7 16.7 19.4 2.4 2.8 119 1111 E5 1 DH125 774.4 36.4 1.2 670.5 31.2 . . . . . 7.0 2.9 14.8 17.7 2.5 2.5 116 1112 E5 1 DH126 729.7 45.4 1.2 597.6 25.1 . . . . . 8.2 2.0 18.0 20.0 2.5 2.5 119 1113 E5 1 DH128 504.7 43.9 1.1 476.1 23.8 . . . . . 7.2 3.0 17.3 20.3 2.8 2.6 119 1114 E5 1 DH129 651.8 41.0 1.3 503.3 30.2 . . . . . 7.5 3.0 15.8 18.8 2.5 2.6 115 1115 E5 1 DH130 596.5 37.4 1.0 588.3 26.8 . . . . . 7.9 2.6 15.4 18.0 2.3 2.5 115 1116 E5 1 DH131 508.2 42.6 1.2 409.5 29.3 . . . . . 8.1 3.4 17.4 20.8 2.6 2.4 121 179 Table E.1 Continued. No. Env. Rep. Name GYLD GPS GWPS SPSM TGW PHT FLL FLW FLA FLS SL SSN FSN TSN SC GSP HD 1117 E5 2 DH1 433.6 37.4 0.9 458.3 24.3 . . . . . 6.7 1.8 14.1 15.9 2.4 2.6 113 1118 E5 2 DH3 596.5 36.2 1.1 565.4 28.3 . . . . . 6.6 2.9 13.9 16.8 2.5 2.6 120 1119 E5 2 DH4 505.9 37.2 1.1 479.0 27.8 . . . . . 7.3 2.7 16.1 18.8 2.6 2.3 117 1120 E5 2 DH5 678.2 40.0 1.1 593.8 27.2 . . . . . 8.0 2.4 15.1 17.5 2.2 2.6 117 1121 E5 2 DH6 727.2 42.6 1.2 585.9 29.2 . . . . . 7.2 1.9 15.9 17.8 2.5 2.7 109 1122 E5 2 DH7 637.6 45.6 1.2 547.3 25.2 . . . . . 6.9 3.2 16.7 19.9 2.9 2.7 123 1123 E5 2 DH8 606.0 47.5 1.3 451.5 27.8 . . . . . 7.9 2.2 16.1 18.3 2.3 2.9 116 1124 E5 2 DH9 451.0 42.7 1.1 408.5 24.4 . . . . . 7.6 3.9 16.9 20.8 2.8 2.5 120 1125 E5 2 DH11 608.1 38.7 1.3 458.9 34.5 . . . . . 8.3 2.6 15.4 18.0 2.2 2.5 114 1126 E5 2 DH12 314.5 40.9 1.1 292.8 21.9 . . . . . 7.3 2.5 16.6 19.1 2.6 2.5 115 1127 E5 2 DH13 620.3 46.5 1.3 480.1 25.1 . . . . . 7.8 3.4 18.0 21.4 2.7 2.6 116 1128 E5 2 DH14 668.4 39.3 1.3 509.9 33.8 . . . . . 7.8 3.6 16.1 19.7 2.5 2.4 117 1129 E5 2 DH15 660.2 37.7 1.1 603.5 27.7 . . . . . 7.5 2.0 15.1 17.1 2.3 2.5 112 1130 E5 2 DH16 713.0 40.7 1.2 604.2 29.3 . . . . . 7.0 1.8 15.2 17.0 2.4 2.7 112 1131 E5 2 DH17 657.9 42.2 1.4 467.9 32.2 . . . . . 8.0 2.0 16.1 18.1 2.3 2.6 112 1132 E5 2 DH18 538.1 48.0 1.3 401.6 27.3 . . . . . 8.4 2.2 18.2 20.4 2.4 2.6 123 1133 E5 2 DH19 605.3 37.4 1.1 533.7 31.1 . . . . . 6.9 2.2 14.3 16.5 2.4 2.6 114 1134 E5 2 DH20 541.8 43.7 1.2 470.8 26.2 . . . . . 7.0 3.2 16.2 19.4 2.8 2.7 119 1135 E5 2 DH21 538.1 33.4 1.1 511.5 30.8 . . . . . 7.1 4.0 14.1 18.1 2.5 2.4 118 1136 E5 2 DH22 469.0 46.4 1.3 361.0 28.7 . . . . . 7.8 2.9 17.5 20.4 2.6 2.6 117 1137 E5 2 DH23 484.8 44.6 1.2 415.4 26.0 . . . . . 6.9 2.0 15.1 17.1 2.5 3.0 112 1138 E5 2 DH24 635.4 39.4 1.2 545.8 28.8 . . . . . 7.1 2.1 15.0 17.1 2.4 2.6 112 1139 E5 2 DH25 607.7 42.8 1.2 504.3 25.5 . . . . . 7.8 3.2 16.3 19.5 2.5 2.6 112 1140 E5 2 DH26 531.6 40.2 1.0 520.6 23.7 . . . . . 7.1 2.6 15.2 17.8 2.5 2.6 115 1141 E5 2 DH27 791.0 41.9 1.2 642.6 27.4 . . . . . 6.5 3.1 14.6 17.7 2.7 2.9 119 1142 E5 2 DH28 658.7 38.9 1.2 542.6 30.4 . . . . . 6.8 2.8 15.0 17.8 2.6 2.6 117 1143 E5 2 DH29 595.6 39.3 1.2 515.3 25.1 . . . . . 7.7 3.5 15.9 19.4 2.5 2.5 117 1144 E5 2 DH30 460.6 34.4 1.1 433.3 30.2 . . . . . 7.3 3.2 15.9 19.1 2.6 2.2 116 1145 E5 2 DH31 542.1 40.4 1.1 511.4 26.2 . . . . . 7.7 2.6 15.6 18.2 2.3 2.6 116 1146 E5 2 DH32 550.7 38.8 0.9 622.3 23.6 . . . . . 7.1 2.5 16.4 18.9 2.7 2.4 123 1147 E5 2 DH33 617.5 56.5 1.3 489.7 22.2 . . . . . 7.8 2.3 20.1 22.4 2.9 2.8 123 1148 E5 2 DH34 705.3 54.0 1.6 445.5 30.0 . . . . . 8.3 1.4 17.5 18.9 2.3 3.1 110 1149 E5 2 DH35 576.3 34.3 1.1 543.6 30.6 . . . . . 7.6 3.0 14.9 17.9 2.4 2.3 115 1150 E5 2 DH36 657.2 42.5 1.2 566.1 27.3 . . . . . 8.6 2.9 16.9 19.8 2.3 2.5 117 1151 E5 2 DH37 580.8 45.2 1.2 500.3 26.5 . . . . . 8.0 2.7 17.1 19.8 2.5 2.6 116 1152 E5 2 DH38 519.8 43.9 1.1 473.9 23.2 . . . . . 7.9 1.9 16.4 18.3 2.3 2.7 109 1153 E5 2 DH39 578.9 40.3 1.3 457.7 29.3 . . . . . 6.3 2.1 16.5 18.6 3.0 2.4 116 1154 E5 2 DH40 421.5 36.8 1.0 404.6 28.1 . . . . . 8.2 2.0 17.1 19.1 2.3 2.2 117 1155 E5 2 DH41 542.7 37.7 1.1 504.4 29.7 . . . . . 7.1 2.6 14.5 17.1 2.4 2.6 118 1156 E5 2 DH42 459.0 45.9 1.3 364.0 26.8 . . . . . 7.3 2.3 17.8 20.1 2.8 2.6 116 1157 E5 2 DH43 443.1 40.3 1.2 380.4 27.4 . . . . . 7.0 2.8 15.2 18.0 2.6 2.6 116 1158 E5 2 DH44 621.9 43.0 1.4 453.0 31.4 . . . . . 8.1 2.9 15.4 18.3 2.3 2.8 120 1159 E5 2 DH45 586.9 36.8 1.4 431.2 35.5 . . . . . 8.1 2.8 16.6 19.4 2.4 2.2 115 1160 E5 2 DH46 545.5 46.3 1.1 476.0 26.8 . . . . . 8.6 3.6 16.8 20.4 2.4 2.8 120 1161 E5 2 DH47 598.2 37.6 1.0 589.9 23.9 . . . . . 7.2 3.7 14.6 18.3 2.5 2.6 119 1162 E5 2 DH49 622.5 47.4 1.3 472.7 28.5 . . . . . 7.9 3.0 16.4 19.4 2.5 2.9 120 1163 E5 2 DH50 618.9 33.7 1.0 596.2 31.4 . . . . . 7.2 3.5 14.8 18.3 2.5 2.3 116 1164 E5 2 DH51 548.6 45.1 1.0 538.9 23.7 . . . . . 7.6 3.4 15.9 19.3 2.6 2.8 119 1165 E5 2 DH52 585.6 37.2 1.0 600.0 25.4 . . . . . 7.1 2.2 15.4 17.6 2.5 2.4 114 1166 E5 2 DH53 555.8 39.5 1.1 507.6 28.1 . . . . . 7.2 2.7 15.6 18.3 2.6 2.5 116 1167 E5 2 DH54 619.4 47.0 1.5 409.1 30.4 . . . . . 7.9 2.0 15.2 17.2 2.2 3.1 110 1168 E5 2 DH55 630.1 37.7 1.1 582.4 27.8 . . . . . 7.0 2.5 15.3 17.8 2.5 2.5 115 1169 E5 2 DH56 699.2 36.2 1.1 608.5 31.0 . . . . . 6.9 2.4 14.2 16.6 2.4 2.5 113 1170 E5 2 DH57 426.4 42.1 1.0 412.4 21.1 . . . . . 7.1 2.0 16.4 18.4 2.6 2.6 114 1171 E5 2 DH58 617.9 37.9 1.1 541.1 29.8 . . . . . 6.9 2.7 13.7 16.4 2.4 2.8 114 1172 E5 2 DH59 654.9 45.4 1.2 560.2 27.0 . . . . . 7.0 0.9 15.8 16.7 2.4 2.9 109 1173 E5 2 DH60 568.4 51.0 1.5 375.7 27.1 . . . . . 8.0 1.8 16.4 18.2 2.3 3.1 110 1174 E5 2 DH61 567.0 41.6 1.2 485.8 28.2 . . . . . 7.6 2.0 15.4 17.4 2.3 2.7 119 1175 E5 2 DH62 272.2 40.2 0.9 307.5 20.0 . . . . . 7.3 1.8 15.6 17.4 2.4 2.6 114 1176 E5 2 DH63 382.9 49.6 1.3 285.5 27.2 . . . . . 8.2 1.8 16.1 17.9 2.2 3.1 110 1177 E5 2 DH64 519.4 40.1 1.2 434.6 30.6 . . . . . 7.8 1.7 15.6 17.3 2.2 2.6 113 1178 E5 2 DH65 440.4 49.2 1.2 364.6 23.5 . . . . . 8.4 1.7 18.5 20.2 2.4 2.7 110 180 Table E.1 Continued. No. Env. Rep. Name GYLD GPS GWPS SPSM TGW PHT FLL FLW FLA FLS SL SSN FSN TSN SC GSP HD 1179 E5 2 DH66 451.0 46.5 1.3 334.4 26.9 . . . . . 8.0 3.5 17.9 21.4 2.7 2.6 121 1180 E5 2 DH67 540.0 41.6 1.2 447.0 29.9 . . . . . 7.5 2.0 16.2 18.2 2.4 2.6 114 1181 E5 2 DH68 495.9 39.3 1.0 479.6 24.1 . . . . . 7.8 2.5 15.7 18.2 2.3 2.5 116 1182 E5 2 DH69 652.6 53.3 1.4 480.2 24.3 . . . . . 7.3 0.9 16.5 17.4 2.4 3.2 113 1183 E5 2 DH70 708.5 49.4 1.5 468.6 26.9 . . . . . 8.1 2.3 18.3 20.6 2.6 2.7 119 1184 E5 2 DH71 620.8 47.9 1.3 477.9 23.3 . . . . . 8.2 2.4 17.2 19.6 2.4 2.8 119 1185 E5 2 DH72 485.3 43.4 1.2 420.9 25.1 . . . . . 7.5 2.9 16.3 19.2 2.6 2.6 118 1186 E5 2 DH73 523.5 44.7 1.2 429.8 25.0 . . . . . 7.4 2.6 16.4 19.0 2.6 2.7 118 1187 E5 2 DH75 735.1 37.4 1.2 601.1 30.8 . . . . . 7.2 2.2 14.6 16.8 2.3 2.6 110 1188 E5 2 DH76 629.2 35.5 1.1 592.5 28.6 . . . . . 6.9 2.6 14.6 17.2 2.5 2.4 116 1189 E5 2 DH77 623.9 38.7 1.2 499.6 27.7 . . . . . 8.3 2.7 16.8 19.5 2.3 2.3 114 1190 E5 2 DH78 717.5 43.1 1.3 561.8 29.3 . . . . . 8.0 2.2 16.3 18.5 2.3 2.6 111 1191 E5 2 DH79 635.4 45.3 1.4 461.7 29.5 . . . . . 7.7 2.0 16.3 18.3 2.4 2.8 115 1192 E5 2 DH80 549.9 45.6 1.3 425.3 27.5 . . . . . 7.3 1.4 16.8 18.2 2.5 2.7 116 1193 E5 2 DH81 399.8 38.1 1.1 362.5 27.5 . . . . . 7.5 1.8 16.6 18.4 2.5 2.3 114 1194 E5 2 DH82 507.6 39.0 1.0 487.2 24.8 . . . . . 7.5 1.4 15.5 16.9 2.3 2.5 108 1195 E5 2 DH83 500.6 36.6 1.3 396.0 33.5 . . . . . 7.2 3.6 14.2 17.8 2.5 2.6 116 1196 E5 2 DH84 532.8 32.2 1.0 511.8 32.4 . . . . . 7.1 2.6 13.7 16.3 2.3 2.3 108 1197 E5 2 DH85 528.8 39.7 1.2 435.6 30.1 . . . . . 7.5 2.3 15.8 18.1 2.4 2.5 115 1198 E5 2 DH86 571.1 44.6 1.4 415.3 29.5 . . . . . 6.9 1.3 15.8 17.1 2.5 2.8 114 1199 E5 2 DH87 491.8 41.5 1.2 412.5 26.2 . . . . . 7.3 1.7 15.8 17.5 2.4 2.6 108 1200 E5 2 DH89 586.2 37.0 1.2 484.9 32.4 . . . . . 7.2 2.5 14.9 17.4 2.4 2.5 115 1201 E5 2 DH90 645.1 46.9 1.5 443.1 28.6 . . . . . 7.9 2.3 16.0 18.3 2.3 2.9 108 1202 E5 2 DH91 561.1 41.6 1.2 472.3 28.3 . . . . . 7.3 2.4 15.5 17.9 2.4 2.7 115 1203 E5 2 DH92 614.1 41.6 1.2 520.4 29.0 . . . . . 7.5 1.5 15.3 16.8 2.3 2.7 114 1204 E5 2 DH93 502.9 49.8 1.4 361.8 23.1 . . . . . 8.1 1.9 17.3 19.2 2.4 2.9 109 1205 E5 2 DH94 521.1 32.9 1.0 521.6 29.7 . . . . . 7.1 3.5 15.8 19.3 2.7 2.1 117 1206 E5 2 DH95 545.9 33.7 1.0 559.9 27.0 . . . . . 7.2 2.4 14.1 16.5 2.3 2.4 108 1207 E5 2 DH96 729.9 37.3 1.3 557.6 34.3 . . . . . 7.1 2.1 13.8 15.9 2.2 2.7 112 1208 E5 2 DH97 581.0 41.0 1.2 472.3 31.8 . . . . . 7.4 2.1 16.0 18.1 2.4 2.6 114 1209 E5 2 DH98 551.8 46.1 1.3 424.8 25.8 . . . . . 8.1 1.3 15.6 16.9 2.1 3.0 107 1210 E5 2 DH99 597.0 42.6 1.1 538.8 25.9 . . . . . 7.4 2.0 16.1 18.1 2.4 2.6 120 1211 E5 2 DH100 567.8 55.2 1.4 407.6 22.6 . . . . . 7.7 1.8 17.6 19.4 2.5 3.1 113 1212 E5 2 DH101 553.3 53.1 1.4 403.5 27.2 . . . . . 8.2 1.3 17.5 18.8 2.3 3.0 113 1213 E5 2 DH102 556.6 48.0 1.2 470.5 23.8 . . . . . 8.3 2.9 17.9 20.8 2.5 2.7 121 1214 E5 2 DH103 487.3 36.1 1.1 441.0 30.4 . . . . . 7.6 3.3 15.2 18.5 2.4 2.4 123 1215 E5 2 DH104 538.1 47.8 1.4 398.0 26.0 . . . . . 7.2 2.6 17.8 20.4 2.9 2.7 120 1216 E5 2 DH105 527.5 36.7 1.1 465.6 28.2 . . . . . 6.6 3.3 14.5 17.8 2.7 2.5 116 1217 E5 2 DH106 465.9 47.0 1.2 377.4 28.3 . . . . . 8.3 2.2 17.6 19.8 2.4 2.7 116 1218 E5 2 DH107 535.0 38.7 1.2 446.2 28.6 . . . . . 7.2 1.9 14.3 16.2 2.3 2.7 113 1219 E5 2 DH108 519.8 37.1 1.1 484.5 28.4 . . . . . 7.0 2.4 15.9 18.3 2.6 2.3 116 1220 E5 2 DH109 526.9 41.3 1.1 477.7 23.4 . . . . . 8.0 2.7 16.1 18.8 2.4 2.6 116 1221 E5 2 DH110 573.0 47.3 1.5 389.0 29.2 . . . . . 9.2 2.0 18.2 20.2 2.2 2.6 113 1222 E5 2 DH111 611.2 50.5 1.5 400.0 27.1 . . . . . 9.1 1.6 18.4 20.0 2.2 2.7 115 1223 E5 2 DH112 480.1 37.7 1.1 447.0 29.4 . . . . . 7.3 3.1 15.8 18.9 2.6 2.4 116 1224 E5 2 DH113 555.2 41.0 1.3 440.6 29.6 . . . . . 6.9 2.4 15.2 17.6 2.6 2.7 115 1225 E5 2 DH114 571.7 39.7 1.1 516.5 26.5 . . . . . 7.9 2.2 15.9 18.1 2.3 2.5 115 1226 E5 2 DH115 507.6 32.0 1.0 510.7 30.6 . . . . . 7.2 2.6 14.8 17.4 2.4 2.2 115 1227 E5 2 DH116 656.4 34.8 1.1 592.4 29.8 . . . . . 7.1 2.5 14.0 16.5 2.3 2.5 114 1228 E5 2 DH117 548.0 39.8 1.1 497.3 25.6 . . . . . 7.5 1.1 15.2 16.3 2.2 2.6 112 1229 E5 2 DH119 639.1 40.4 1.2 516.7 30.6 . . . . . 8.5 3.1 16.4 19.5 2.3 2.5 115 1230 E5 2 DH120 727.2 37.1 1.1 682.8 24.8 . . . . . 6.7 2.8 14.9 17.7 2.6 2.5 117 1231 E5 2 DH121 560.5 45.6 1.3 431.8 26.9 . . . . . 7.9 3.9 16.9 20.8 2.6 2.7 119 1232 E5 2 DH122 494.8 54.0 1.3 390.2 23.3 . . . . . 7.5 2.2 18.0 20.2 2.7 3.0 121 1233 E5 2 DH123 520.4 42.7 1.2 428.4 26.6 . . . . . 7.3 2.2 16.0 18.2 2.5 2.7 117 1234 E5 2 DH124 573.0 43.1 1.2 461.0 26.0 . . . . . 8.0 2.8 16.0 18.8 2.4 2.7 119 1235 E5 2 DH125 597.5 37.8 1.2 489.0 32.2 . . . . . 7.2 2.7 15.2 17.9 2.5 2.5 115 1236 E5 2 DH126 520.0 39.4 1.1 476.2 26.9 . . . . . 7.6 3.3 16.3 19.6 2.6 2.4 118 1237 E5 2 DH128 487.0 47.6 1.3 376.4 24.9 . . . . . 7.2 2.6 17.6 20.2 2.8 2.7 119 1238 E5 2 DH129 606.3 44.4 1.4 422.2 31.4 . . . . . 7.6 2.2 16.0 18.2 2.4 2.8 115 1239 E5 2 DH130 630.1 42.9 1.3 470.2 27.9 . . . . . 7.9 2.3 15.7 18.0 2.3 2.7 116 1240 E5 2 DH131 503.5 41.8 1.3 394.3 28.8 . . . . . 8.0 3.6 17.1 20.7 2.6 2.4 121 181 Table E.2 Average phenotypic data for yield contributing traits evaluated at greenhouse 2012 and 2013. Three replications in 2012 and four in 2013. Missing data is indicated by dot. Name Greenhouse 2012 Greenhouse 2013 PHT FLL FLW FLA FLS PHT FLL FLW FLA FLS DH1 73.7 23.6 1.7 32.5 13.6 78 24.1 2 37.7 12.2 DH3 76.1 27 2.1 45.8 12.6 80.2 27.3 2.6 56.1 10.5 DH4 64.9 20.7 1.9 31.5 10.8 65.8 24.7 2.2 43.5 11.1 DH5 68.9 22.5 1.9 33.2 12 75.9 26.4 2.2 45.9 12.1 DH6 79 22.5 1.9 34.2 11.7 78.6 23.7 2 38.3 11.6 DH7 66.1 17 1.9 25.3 9 71.3 28.7 2.4 53.3 12.2 DH8 72.4 18.4 1.7 24.9 10.9 78.3 28.5 2.1 46.6 13.8 DH9 72.9 17.2 2 27.8 8.5 73.9 27.7 2.5 55.4 11 DH11 91.9 21.5 1.8 31.1 11.7 86.8 29.8 2.1 50.3 14 DH12 51.9 18.8 2.1 30.5 9.2 54.8 21.6 2.2 37.2 10 DH13 65.9 18 1.8 25.3 10.1 72 27.1 2 43.1 13.5 DH14 61.8 19.1 1.9 27.9 10.3 71.9 25.9 2.3 46.5 11.4 DH15 63.7 20 1.6 25.2 12.5 70.1 24 1.7 33.4 13.7 DH16 66.9 20.2 1.7 27.3 12 76.1 27 2.1 45.7 12.6 DH17 68.7 26.6 1.6 32.6 17.5 81.7 28.5 2 45.2 14.2 DH18 73.6 16.9 2 26.1 8.6 71.8 30.5 2.6 62.4 11.8 DH19 79.9 15.6 1.9 23.7 8.3 78.4 28 2.3 51.4 12 DH20 70.8 18.1 1.8 26.4 9.9 73.5 29.3 2.5 59 11.5 DH21 81.3 18.6 1.8 26.1 10.5 82.8 28.9 2.4 54.4 12.1 DH22 82.3 15.1 1.6 19.3 9.4 83.4 23.4 1.9 37 11.8 DH23 76.6 21.6 1.6 26.3 13.9 78.9 27.8 2 42.8 14.3 DH24 65.2 20.3 1.9 30.6 10.7 60.5 22.3 2 36.4 11 DH25 73.4 21.5 1.6 26.6 14.5 72.6 25.6 1.9 39.5 13.2 DH26 63.3 14.9 1.5 18.3 9.6 66.2 21.9 1.8 32 12.1 DH27 78.5 15.5 1.5 18.3 10.5 79 30.3 2.4 58.4 12.4 DH28 81.5 19.7 1.6 24.1 12.7 81.4 23.7 1.8 34.2 13.1 DH29 68.7 23 2.1 37.4 11.2 69.8 25.8 2.2 45.3 11.6 DH30 74.6 25.1 1.8 35.2 14.2 78.8 26.6 2.1 44.2 12.9 DH31 74.6 19.1 2.3 34.7 8.3 76.8 23.3 2.2 40.7 10.5 DH32 68.3 15.6 1.8 21.7 8.9 70.8 25.2 2.2 43.8 11.6 DH33 79.7 20.6 1.9 30.9 11 78.6 29.7 2.4 55.8 12.5 DH34 79 17 1.9 25.5 8.9 74.7 26.5 1.9 40.5 13.8 DH35 79.5 14.5 2 22.9 7.3 74.8 20.9 2.1 34.3 10.1 DH36 75 21.1 2 34.1 10.4 76.7 27.2 2.5 53.8 10.9 DH37 77 21.2 2 33.5 10.6 76.9 27.8 1.9 40.2 15.4 DH38 66 20.6 1.5 24.5 13.7 77.1 24.7 1.9 38 12.9 DH39 76.4 13.6 1.5 16.4 8.9 74.8 22.4 1.9 33.6 11.8 DH40 78.5 13.7 1.6 17 8.7 84.2 22.2 1.8 32.8 12.1 DH41 79.2 17.4 1.6 21.9 11.1 84.5 26.4 2 41.8 13.3 DH42 72.3 18.4 1.5 21.8 12.3 77.1 26.2 2.1 42.6 12.7 DH43 77.5 14.3 1.6 18.3 8.9 79 26.7 2 42.9 13.2 DH44 84.4 19.3 1.8 26.5 11 92 28.3 2.5 56.1 11.5 DH45 68.8 18.2 1.7 24.6 10.7 77.6 24.2 2.2 41.5 11.1 DH46 77.6 21.7 1.9 32.9 11.3 79.6 30.9 2.3 56.4 13.4 DH47 74 21 1.7 29 12.1 76.8 23.8 2.2 41.5 10.8 DH49 79.4 26.2 2 41.4 13.2 77.9 29.1 2.2 50.3 13.9 DH50 75 26.1 1.9 39.6 13.6 81.4 24.5 2.1 40.6 11.7 DH51 71.1 28.7 2 45.1 14.5 74.8 24.2 2 40 11.8 DH52 62.5 27.2 1.4 30.2 19.4 70.7 25.4 1.8 35.3 14.5 DH53 57.3 18.9 2 29.5 9.6 59.8 24.9 2.2 42.9 11.5 DH54 67.7 28.3 2.2 48.3 13.2 78.4 29.8 2.4 55.5 12.7 DH55 67.6 22.1 1.7 29.8 12.9 75 23.1 1.9 35.2 12.2 DH56 62.8 26 2 40.3 13.3 70.8 22.4 2 35.6 11.2 DH57 75.3 21 1.9 30.8 11.3 69.4 24 2.1 40.2 11.3 DH58 78.6 25.4 2 39 13 83.7 25.9 2 41.3 12.8 DH59 71.1 21.8 1.5 26.6 14.1 80.5 26.4 1.9 39.9 13.9 DH60 78.9 20.5 2.1 34.1 9.7 76.6 31.9 2.3 57.4 14 DH61 70.4 16.7 1.8 23.8 9.3 72.9 27 2.1 44.3 13 DH62 78.1 21.3 1.8 30.2 11.8 75.6 28.9 2.1 48.7 13.6 DH63 73.1 25.9 2.2 44.4 11.9 77.1 28.2 2.3 50.7 12.5 DH64 79.9 21.3 1.8 30.6 11.8 85.6 23.7 1.9 36.6 12.2 DH65 66.7 14.8 1.4 16.9 10.3 75.3 27.9 1.9 42.3 14.6 182 Table E.2 Continued. Name Greenhouse 2012 Greenhouse 2013 PHT FLL FLW FLA FLS PHT FLL FLW FLA FLS DH66 71.6 16.6 2 26.2 8.3 79.4 30.3 2.8 67.9 10.8 DH67 69.4 25.4 1.8 36.1 14.1 73.3 26.3 1.8 38 14.5 DH68 69.3 21.4 2.1 34.7 10.4 73.4 24 2.4 45.3 10.1 DH69 67.7 20.4 1.6 26.3 12.6 78.3 26.8 2 41.1 13.7 DH70 76.7 16.9 1.8 24 9.4 80.2 24.9 2.1 41.8 11.8 DH71 64.7 19.8 2 31.3 9.9 67.6 26.1 2.5 51.8 10.5 DH72 67.7 22.1 1.8 31 12.5 71 23.1 1.9 34.7 12.2 DH73 68.7 18.4 1.8 26.2 10.2 72.6 27 2.1 44.7 12.9 DH75 77.1 18 1.3 17.6 14.6 85.2 26.7 1.8 38.4 14.7 DH76 69.2 21 1.8 29.9 11.7 70.7 24.4 2 38.4 12.6 DH77 75.6 22.3 1.6 27.6 14.3 76.7 31.3 1.8 45.6 17 DH78 82.2 15.3 1.6 20.1 9.5 80.3 28.4 2 45.6 14.1 DH79 79.1 20.5 1.7 27.5 12 82 26.3 2 41.2 13.3 DH80 77.6 19.3 1.7 25.9 11.3 75.8 27.6 1.9 42.2 14.3 DH81 66.5 11.8 1.6 14.9 7.4 70 27 2.3 48.8 11.9 DH82 72.2 20.5 1.8 28.7 11.6 78.8 27 2.1 45.1 12.8 DH83 85.9 19.3 2 30.5 9.7 88.4 24.3 1.8 35.4 13.3 DH84 72.5 17.4 1.6 22.4 10.7 77.4 25 1.9 36.6 13.5 DH85 67.3 17.1 2.1 27.9 8.3 71.5 26 2.2 44.7 12 DH86 72.2 22.8 1.7 30.1 13.6 79.5 24.2 1.8 34.5 13.5 DH87 75.9 21.5 1.6 27.8 13.3 83.2 28.4 1.9 42.7 15 DH89 . . . . . 79.3 23.2 2.1 39.2 10.9 DH90 68.1 27.1 2.1 45 12.9 73.2 27.4 2.2 47.9 12.4 DH91 74 13.8 1.4 15.3 9.9 78.8 27.1 1.9 40.3 14.4 DH92 75.7 22.6 1.8 31.7 12.8 76.1 26.9 1.9 40.2 14.2 DH93 72.5 18.5 1.7 25.2 10.9 67.2 26.1 1.8 37.5 14.4 DH94 72.1 19 1.7 25.5 11.2 81.2 21.3 1.9 32.6 11.1 DH95 86.3 21.5 1.6 27.2 13.4 74.2 22 1.8 32.4 12 DH96 82 17.2 1.5 20.4 11.5 80 27.8 2 45 13.6 DH97 76.5 18.7 2.1 31 8.9 78.1 23.8 2.1 40.4 11.1 DH98 72.6 21.3 2.1 35.3 10.1 72.7 27.2 2.1 44.1 13.4 DH99 77.6 14 1.7 18.8 8.2 74.8 29.1 2.7 61.9 10.8 DH100 64.7 23 1.8 32.6 12.8 66.6 26.2 2 41.2 13.2 DH101 67.9 22.4 1.8 32.3 12.3 80.3 26.3 2.1 44.6 12.2 DH102 78 16.6 1.8 23.6 9.2 79.7 29.6 2.2 52.2 13.3 DH103 71.9 28.3 2.3 50.4 12.6 74.7 28.3 2.6 59 10.8 DH104 78.2 25.2 1.9 37.8 13.3 83 28 2.2 49 12.6 DH105 67.7 28 1.9 40.9 15.2 75.8 28.9 1.9 32.9 11.4 DH106 72.1 17.8 1.9 26.7 9.4 71.3 24.7 2.2 42.3 11.4 DH107 71.1 19.3 1.7 25.7 11.5 76.4 24.4 2 39.2 12 DH108 79.2 11 1.6 14.1 6.8 77.1 27.3 2 42.4 13.8 DH109 69.9 16 1.8 22.8 8.9 68.8 26.6 2 41.1 13.6 DH110 80.1 21.4 1.7 28.3 12.8 81.3 26.5 2 41.4 13.4 DH111 77.8 26.8 1.8 37.1 15.4 82.9 29.1 2.1 47.6 14.1 DH112 81 13.2 1.6 16.4 8.4 74.4 23.7 1.9 36.5 12.2 DH113 82.4 12.6 1.8 17.3 7.2 83.2 24.8 2.1 40.6 12 DH114 62.9 18.8 1.9 28.2 9.9 68.7 27.1 2.2 47.5 12.2 DH115 76.6 12.1 1.6 15.6 7.5 74.9 24 1.9 36.4 12.6 DH116 83.4 18.8 1.9 27.5 10.2 83.6 22.4 2.1 36.5 10.9 DH117 72 22 2.1 36.1 10.6 77.6 25.5 2.3 46 11.2 DH119 76.7 22.4 1.9 34 11.7 78.4 25.3 2.1 43 11.9 DH120 77.6 15.2 1.6 19.4 9.5 76.7 26.6 2 42.1 13.4 DH121 72.9 20.7 1.8 29.9 11.4 72.3 28.2 2.4 53.6 11.8 DH122 79.6 20.4 1.4 22.2 15.5 81.2 27.6 1.9 41.3 14.6 DH123 77.6 17.6 1.4 18.9 13 76.7 21.6 1.3 22.7 16.4 DH124 76.7 20.2 1.9 29.9 10.7 73.4 27.6 2.3 51.2 11.8 DH125 67.8 19.2 1.9 29.2 10.1 71.7 22.6 2 35.1 11.6 DH126 61.6 25.6 1.7 34.9 15.3 74.8 28.9 2.4 53.9 12.3 DH128 70.3 21.4 1.6 28.2 13.1 75.8 29.7 2.3 55.4 12.9 DH129 69.1 27.9 1.7 38.1 16.1 80 24.3 1.9 36.3 12.9 DH130 69.2 22.5 2 35.6 11.3 71.7 25.8 2.1 42.8 12.3 DH131 73.5 17.9 2 28.3 9 77.3 29.2 2.6 60.2 11.2 183 Bibliography Abdi, H., and Williams, L. 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