UNCOVERING THE GENETIC BASIS AND CHARACTERS ASSOCIATION OF WHEAT SPIKE-
RELATED, AGRONOMIC AND QUALITY TRAITS
A Dissertation Submitted to the Graduate Faculty of the North Dakota State University of Agriculture and Applied Science
By
Morgan Echeverry-Solarte
In Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY
Major Department: Plant Sciences
March 2014
Fargo, North Dakota
North Dakota State University Graduate School
Title
UNCOVERING THE GENETIC BASIS AND CHARACTERS ASSOCIATION OF WHEAT SPIKE-RELATED, AGRONOMIC AND QUALITY TRAITS
By
Morgan Echeverry-Solarte
The Supervisory Committee certifies that this disquisition complies with North
Dakota State University‟s regulations and meets the accepted standards for the degree of
DOCTOR OF PHILOSOPHY
SUPERVISORY COMMITTEE:
Dr. Mohamed Mergoum Chair
Dr. Elias Elias
Dr. Phillip McClean
Dr. Mohammed Alamri
Dr. Jack Rasmussen
Dr. Edward Deckard
Approved:
3/26/2014 Richard D. Horsley Date Department Chair
ABSTRACT
Modern wheat (Triticum aestivum L.) cultivars are characterized by having spikes with fusiform architecture and rachis nodes with one spikelet. However, genotypes with supernumerary spikelets (SS) in which rachis nodes have more than one spikelet exist in nature. Although this may be a promising trait that increases yield components, detailed knowledge about the molecular basis of SS trait and their influence on other wheat traits is still lacking. In the present study, a population of 163 recombinant inbred lines (RILs) derived from an elite line and an exotic line with SS was used to identify QTL for SS and other wheat traits. The RILs, seven checks and the two parents were evaluated for seven SS-related, 10 spike-related, 10 agronomic, and eight quality traits over four to six environments in North Dakota. A genetic map of 3,114.2 cM of length with an average distance of 4.6 cM between any two marker loci was developed using 159 RIL and 939 DArT markers. Composite interval mapping identified 221 QTL, out of which 29% were consistent QTL and 19% were major QTL. Most of the QTL were located on the B- genome (44%) followed by the A-genome (37%) and D-genome (19%). The exotic parent with SS contributed 48% of alleles that increased phenotypic values of the traits suggesting the possibility of enriching the breeding germplasm with genes from this genotype. Seven consistent QTL with epistatic interaction were associated to the SS. QSS.ndsu-2D, a major QTL for SS, was co-located in a cluster of
QTL on 2DS demonstrating either pleiotropic effect or closely linkage with 19 QTL for other wheat traits.
Similarly, a major QTL associated with glume pubescences (QPP.ndsu-1A.1) was co-located on 1AS with seven QTL for other wheat traits. Major and consistent QTL are targets for further marker assisted selection in wheat breeding programs and/or for research projects aiming of gene cloning.
iii ACKNOWLEDGEMENTS
I wish to express my gratitude to my advisor, Dr. Mohamed Mergoum for his support, advices, guidance, and patience during the completion of my doctoral studies and research. I also thanks to my graduate committee, Drs. Ed Deckard, Elias Elias, Phil McClean, Mohamed Alamri, and Jack
Rasmussen. My gratitude is also extended to Dr. Shahryar Kianian and Dr. Gary Secor initial member of my graduate committee. Special thanks to Dr. Senay Simsek for her support during the quality analysis.
Likewise, I would like express my sincere gratitude to Dr. Ajay Kumar for his advices, professionalism, dedication, and commitment during the data analysis and preparation of this document.
I thanks to the members and students of the NDSU Hard Red Spring Breeding and Genetics project, Wheat Quality Program, and Wheat Germplasm Enhancement Project: Jesse Underdahl,
Matthew Abdallah, DeLane Olsen, Kelly McMonagle, Justin Hegstad, Allen Peckrul, Daniel Liane, Eder
Mantovani, Ahmed El-Fatih ElDolfey, Hisam Rabbi, Sepher Mohajeri, Monalisa Almeida and Vishwajeet
Chhikara. Special thanks to my friends and classmates Eder, Ana Maria, Andre, Fabio, Jimmi, Roberto,
Renata, Angela, Wesam, Magan, Juan Calle, Ali, Samira, Mona, Ronald, Atena, Osvaldo, Adriana,
Rosham for their friendship and good moments.
I also thanks to Johanna Villamizar-Ruiz, my wife, girlfriend and colleague for unconditional love, support and patience. Also I extend my gratitude to my parents, Jairo and Mireya, and my brother, Stuart, for their love, support and patience during this almost 8 years of absence. I would like also thanks to Juan
Manuel, Judith (Yuyis), Juan Esteban, Melissa and Nicholas for their love, kindness, support and for open us the doors of their holy home during these wonderful years in Fargo.
I gratefully acknowledge to Dr. Richard Horsley and Dr. Linda Wessel-Beaver to encourage me to be a plant breeder!
There are men who struggle for a day, and they are good. There are others who struggle for a year, and they are better. There are some who struggle many years, and they are better still. But there are those who struggle all their lives, and these are the indispensable ones.” Bertol Brecht
iv DEDICATION
Dedicated to my loved one…
Johanna, “La Incondicional”, my wife.
Jairo “El Indispensable”, my father.
Mireya “Mástil de Hogar”, my mother.
Stuart “El Sensato”, my brother.
All my loved family
v PREFACE
This dissertation consists of five chapters. Chapter 1 includes a general introduction about the dissertation research, along with the objectives, literature review and references. Chapters 2, 3 and 4 are written as three papers to be submitted for publication to the appropriate scientific journals. Therefore, these chapters each include an abstract, introduction, material and methods, results, discussion, conclusions and references. Chapter 5 is the general conclusion. Appendices are included after the end of chapter five.
vi TABLE OF CONTENTS
ABSTRACT ...... iii
ACKNOWLEDGEMENTS ...... iv
DEDICATION ...... v
PREFACE ...... vi
LIST OF TABLES ...... xii
LIST OF FIGURES ...... xiii
LIST OF ABBREVIATIONS...... xiv
LIST OF APPENDIX TABLES...... xvi
LIST OF APPENDIX FIGURES ...... xvii
CHAPTER 1. INTRODUCTION ...... 1
1.1. General Introduction ...... 1
1.2. Objectives ...... 3
1.2.1. General Objective ...... 3
1.2.2. Specific Objectives ...... 3
1.3. Literature Review ...... 4
1.3.1. Branched spikes ...... 4
1.3.1.1. Natural variation for branched spikes ...... 4
1.3.1.2. Associations between branched spikes and other wheat characteristics ...... 6
1.3.1.3. Genetics of branched spikes ...... 8
1.3.2. Spike size traits...... 14
1.3.2.1. Natural variation for spike architecture ...... 14
vii 1.3.2.2. Associations among spike architecture and other wheat traits ...... 15
1.3.2.3. Genetics of spike architecture ...... 17
1.3.3. Spike awnedness ...... 22
1.3.3.1. Natural variation of spike awnedness ...... 22
1.3.3.2. Associations between spike awnedness and other wheat traits ...... 23
1.3.3.3. Genetics of awnedness ...... 24
1.3.4. Variations in glume pubescences ...... 24
1.3.4.1. Natural variation for glume pubescences ...... 24
1.3.4.2. Associations of glumes pubescences and other wheat traits ...... 25
1.3.4.3. Genetic of glume pubescences ...... 25
1.3.5. Genetics of several agronomic and quality traits in wheat ...... 26
1.4. References ...... 40
CHAPTER 2. GENOME-WIDE GENETIC DISSECTION OF SUPERNUMERARY SPIKELET AND RELATED TRAITS IN COMMON WHEAT (TRITICUM AESTIVUM L.) ...... 53
2.1. Abstract ...... 53
2.2. Introduction ...... 53
2.3. Material and Methods ...... 56
2.3.1. Plant material ...... 56
2.3.2. Field experiment ...... 57
2.3.3. Phenotypic data ...... 58
2.3.4. Statistical analysis ...... 59
2.3.5. Molecular marker analysis, map construction, QTL identification, and epitasis analysis ...... 59
2.4. Results ...... 61
viii 2.4.1. Phenotypic analyses ...... 61
2.4.2. Map construction ...... 64
2.4.3. QTL analysis ...... 65
2.5. Discussion ...... 72
2.6. Conclusions ...... 81
2.7. References ...... 82
CHAPTER 3. GENOME-WIDE MAPPING OF SPIKE-RELATED AND AGRONOMIC TRAITS IN A COMMON WHEAT POPULATION DERIVED FROM A SUPERNUMERARY PARENT AND AN ELITE PARENT ...... 92
3.1. Abstract ...... 92
3.2. Introduction ...... 92
3.3. Material and Methods ...... 95
3.3.1. Plant material ...... 95
3.3.2. Field experiment ...... 95
3.3.3. Spike-related traits ...... 96
3.3.4. Agronomic traits ...... 97
3.3.5. Statistical analysis ...... 98
3.3.6. Molecular marker analysis, map construction, QTL identification, and genotypic analysis ...... 98
3.4. Results ...... 99
3.4.1. Phenotypic variation ...... 99
3.4.2. QTL for spike size and pubescences ...... 105
3.4.3. Awn-related QTL ...... 107
3.4.4 .QTL for yield components ...... 108
3.4.5. QTL for important agronomical traits ...... 119 ix 3.4.6. Distribution of QTL at whole genome level and important QTL clusters ...... 126
3.5. Discussion ...... 126
3.5.1. Phenotypic variation ...... 126
3.5.2. QTL for spike-related traits ...... 127
3.5.3. QTL for yield and yield components ...... 131
3.5.4. QTL for other important agronomic traits ...... 134
3.5.5. Genetic associations of spike pubescences and SS with other traits ...... 135
3.6. Conclusion ...... 137
3.7. References ...... 137
CHAPTER 4. GENETIC DISSECTION OF QUALITY TRAITS IN WHEAT USING AN ELITE× EXOTIC RIL POPULATION ...... 147
4.1. Abstract ...... 147
4.2. Introduction ...... 147
4.3. Material and Methods ...... 151
4.3.1. Plant material ...... 151
4.3.2. Field experiment ...... 151
4.3.3. Phenotypic data collection ...... 151
4.3.4. Data analysis ...... 152
4.3.5. QTL analysis ...... 153
4.4. Results ...... 154
4.4.1. Phenotypic data ...... 154
4.4.2. Identification of genomic regions (QTL) associated with eight quality traits ...... 157
4.4.3. QTL for TKW, KVW, and FE ...... 158
x 4.4.4. QTL for GPC ...... 159
4.4.5. QTL for mixograph-related traits...... 159
4.5. Discussion ...... 173
4.5.1. Phenotypic variations ...... 173
4.5.2. QTL for TKW, KVW and FE ...... 174
4.5.3. QTL for GPC and mixograph-related traits ...... 177
4.5.4. Influence of SS loci on quality traits ...... 180
4.6. Conclusion ...... 180
4.7. References ...... 181
CHAPTER 5. GENERAL CONCLUSIONS ...... 190
APPENDIX A. SUPPLEMENTARY TABLES AND FIGURE FOR CHAPTER 2 ...... 193
APPENDIX B. SUPPLEMENTARY TABLES AND FIGURES FOR CHAPTER 3 ...... 200
APPENDIX C. SUPPLEMENTARY TABLES FOR CHAPTER 4 ...... 211
xi LIST OF TABLES
Table Page
1-1. Summary of the main genetic studies for SS in wheat during the 1983 and 2012 period ...... 10
1-2. Summary of QTL information on studies for spike architecture related traits (adapted from Kumar, 2009) ...... 19
1-3. Summary of information related to QTL governing agronomic and quality traits in common wheat ...... 27
2-1. Mean squares, coefficient of variation and heritabilities for supernumerary-spikelets- related traits of parents, RILs and checks evaluated in a maximum of six environments ...... 63
2-2.. QTL identified for supernumerary-spikelet-related traits in WCB414 × WCB617 RIL population of hexaploid wheat ...... 66
2-3. Additive Epistatic Interaction among QTL associated to SS-related traits ...... 71
3-1. Mean squares, coefficient of variation and heritabilities for spike-related and agronomic traits ...... 101
3-2. Correlations coefficients between spike-related traits ...... 102
3-3. Correlations coefficients between agronomic traits ...... 103
3-4. Correlations coefficients between spike-related traits and agronomic traits ...... 104
3-5. QTL identified for spike-related traits in a RIL population of hexaploid wheat derived from the cross of WCB414 and WCB617 ...... 109
3-6. QTL identified for agronomic traits in a RIL population of hexaploid wheat derived from the cross of WCB414 and WCB617 ...... 120
4-1. Mean squares, coefficient of variation and heritabilities for quality traits of the parents, RILs and checks evaluated in four environments in North Dakota, USA, during 2009 and 2010...... 156
4-2. Pearson's correlation coefficients among traits and their significance for the RIL, their parents and checks grown at Prosper and Carrington, ND, in 2009 and 2010...... 157
4-3. Summary of information for QTL identified in a RIL population derived from the cross of an elite line (WCB414) and exotic line (WCB617) with SS at four locations in ND, USA during 2009 and 2010...... 160
xii
LIST OF FIGURES
Figure Page
2-1. Segregation in spike morphology in a spring wheat RIL population derived from the cross of an elite line (WCB414) with non-supernumerary spikelets (SS) and a PI with SS (WCB617)...... 62
2-2. Genetic linkage maps bearing stable QTL associated to seven supernumerary-spikelet- related traits in a RIL population of 159 individual derived from the cross between the elite line WCB414 and the PI with supernumerary spikelets (SS) WCB617...... 68
2-3. Graphical genotypes of RILs with SS phenotype (All RILs with LPSS score ≥1) and non- SS phenotype (20 random RILs with LPSS score=0) for the 2DS region harboring major QTL (QSS.ndsu-2D) ...... 77
3-1. Types of spikes identified in the RIL population derived from the cross of the elite line WCB414 and the exotic line with supernumerary spikelets WCB617. A. Spike with supernumerary spikelets. B. Spike apically awnleted. C. Spike with fusiform architecture at the top of the spike. D. Spike with clavate architecture at the bottom of the spike. E. Spike with glabrous glumes. F. Spike with pubescences on the glumes...... 100
3-2. Genetic map and QTL for 10 spike-related, and 10 agronomic traits of a RIL population derived from the cross of the elite line WCB414 and the exotic lineWCB617 with SS trait, over four to six locations in ND, USA during 2009, 2010, and 2011...... 113
4-1. Frequency distribution of 163 RIL for mean of eight quality traits over four six environments ...... 155
4-2. Genetic map and QTL for 35 spike-related, agronomic, and quality traits of a RIL population derived from the cross of the elite line WCB414 and the exotic lineWCB617 with SS trait, over four to six locations in ND, USA during 2009, 2010, and 2011...... 164
xiii LIST OF ABBREVIATIONS
AAL ...... Awns length total averaged
AE ...... Across environments
AFLP ...... Amplified fragment length polymorphism
ALB ...... Awns length at the bottom of spike
Aless ...... Apical awnleted expression
ALM ...... Awns length at the middle of spike
ALT ...... Awns length at the top of spike
DArT ...... Diversity arrays technology
DH……………… ...... Days to heading
DM ...... Days to maturity
D-H ...... Double haploids
EST ...... Expressed sequence tag
FE ...... Flour extraction
GPC ...... Grain protein content
G-SSR ...... Genomic simple sequence repeat
GY ...... Grain Yield
IL ...... Introgression line
ISSR ...... Inter-simple sequence repeat
KS ...... Kernels per spike
KSk ...... Kernels per spikelet
KNd ...... Kernels per node
KVW…………………… ...... Kernel Volume weight
Ld ...... Lodging
MEPT ...... Mixograph envelope peak time
MMLPT ...... Mixograph MID line peak time
MMLPI ...... Mixograph MID peak integral
xiv Mx ...... General mixogram patterm
Nd ...... Number of nodes
NdD ...... Node density
NNdISk ...... Number of Nodes with immature spikelets
NdNonSS ...... Number of nodes with non sypernumerary spikeles
NdR ...... Number of nodes with extended rachilla
NdSS ...... Number of Nodes with supernumerary spikelets
NIL(s) ...... Near isogenic line(s)
NS ...... Number of spikes per m-2
PH ...... Plant height
PC ...... Penetrance of clavate architecture
PP ...... Penetrance of pubescences
PSS ...... Penetrance of supernumerary spikelets
QTL ...... Quantitative trait locus/loci
RAPD ...... Random amplified polymorphic
RIL(s)………………… ...... Recombinant Inbred Line(s)
SbLi ...... Substitution line
SD ...... Spike density
Sk ...... Number of spikelets
SkNd ...... Spikelets per node
SL ...... Spike length
SRAP ...... Sequence-related amplified polymorphism
SS…………………… ...... Supernumerary Spikelet(s)
SSR ...... Simple sequence repeats
TKW...... Thousand kernel weight
TRAP(s) ...... Target region amplified polymorphism
xv LIST OF APPENDIX TABLES
Table Page
A1. Mean values and RIL ranges of supernumerary-spikelet-related in each environment ...... 193
A2. Coefficient of variance (CV%), and lattice efficiency (EF%) of supernumerary-spikelets- related traits ...... 196
A3. Error mean square (EMS) and Fmax for supernumerary-spikelet-related traits ...... 197
A4. Correlation coefficients (r) among SS-related traits ...... 198
B1. Mean values and RIL ranges of supernumerary-spikelet-related traits in each environment .... 200
B2. Mean values and RIL ranges of agronomic traits in each environment ...... 203
B3. Coefficient of variance (CV%), and lattice efficiecy (EF%) of spike-related and agronomic traits ...... 207
B4. Error mean square (EMS) and Fmax ratio for spike-related and agronomic traits ...... 208
C1. Estimated means of eight quality traits in a RIL population, their parents and seven checks evaluated in four environments ...... 211
C2. Coefficient of variance, lattice efficiency, error mean square, and FMax ratio for eight for quality traits ...... 214
C3. Significant Pearson's correlation coefficients among four spike-related traits and eight quality traits in wheat ...... 215
xvi LIST OF APPENDIX FIGURES
Figure Page
A1. Frequency distribution of 163 RIL for SS-related traits over four to six environments ...... 199
B1. Frequency distribution of 163 RIL for mean of spike-related traits over four to six environments ...... 209
B2. Frequency distribution of 163 RIL for mean of agronomic traits over four to six environments ...... 210
xvii CHAPTER 1. INTRODUCTION
1.1. General Introduction
Spikes or ears are a type of inflorescence composed of stem tissue with nodes and internodes that support floral leaves. In grasses like wheat (Triticum aestivum L.), these structures have a main axis
(rachis) bearing spikelets directly attached (sessile) to the nodes and separated by short internodes. Each spikelet is comprised of two glumes and florets attached to a rachilla (Lersten, 1987; Kirby, 2002). Each floret contains two bractlike structures, the outer lemma and the inner palea (on the opposite side of the rachilla), and the enclosed grass flower. This flower carries three stamens with anthers (about 3 mm in length), a pistil bearing two styles with feathery stigma branches, and two lodicules (Lersten, 1987; Kirby,
2002). The lemma may bear a long, medium, or short awn at its tip, or may be entirely awnless. However, the palea is always awnless. The glumes may be pubescent (covered with short, fine hairs) or glabrous
(smooth) (Peterson, 1965).
The spike structures have photosynthetic activity which contributes to grain mass (Grundbacher,
1963; Li et al., 2006), milling and baking traits. In recent years, several studies in common wheat have uncovered the genetic basis of spike-related traits, showing the existence of a large number of QTL controlling these traits (Sourdille et al., 2000b; Li et al., 2002; Börner et al,. 2002; Jantasuriyarat et al.,
2004; Verma et al., 2005; Marza et al., 2006; Kumar et al., 2007; Li et al., 2007; Ma et al., 2007; Chu et al., 2008; Cui et al., 2012). However, most of these studies did not assess the association effect of these spike-related QTL on agronomic and quality traits of wheat, thus, limiting the knowledge for successful utilization of these QTL in breeding programs. In fact, the improvement of spikes and other structural characteristics to increase yield potential and wheat quality attributes have received less attention of breeders in recent decades, since the incorporation of resistance to biotic and abiotic conditions have become a higher priority for breeding programs (Reynolds et al. 2011).
Reduction of genetic diversity is a major problem in modern wheat breeding which jeopardizes wheat improvement and global food security (Reeves et al., 1997; Hosington et al.1999; Reif et al., 2005).
Natural genetic variation present in Triticum has been reduced through the selection imposed by farmers in early times and by the utilization of a reduced number of landraces to develop modern wheat cultivars 1 by breeders (Reif et al., 2005; Raman et al., 2010). Indeed, most of the recent QTL mapping studies in wheat were conducted in populations derived from closely related elite parental genotypes Therefore, these studies neither recognize QTL already fixed in this elite material nor discover new allelic variations for incorporation in breeding programs. In wheat, it is possible to find broad genetic diversity in exotic germplasm with variation in spike-related traits that could be utilized to enhance genetic diversity and/or optimize other structural characteristics of wheat. Branched spikes [known as supernumerary spikelets
(SS)], pubescent spikes, awnlessness spikes, clavate spikes, gigas spikes, and screwed spikes are examples of spikes variations that could be utilized toward these purposes.
To understand the genetics of SS and several spike-related traits, the NDSU improvement program developed a recombinant inbred line (RIL) population derived from a cross between WCB414, a wheat elite genotype with conventional spikes, and WCB 617, an introduced exotic genotype with SS and pubescent spikes. The population was advanced through single seed descent method to the F7 generation. This RIL population was used in the present study to assess 35 spike-related, agronomic and quality traits. Subsequently, the parents and the RIL population were genotyped with Diversity Array
(DArT) markers (Akbari et al., 2006), which resulted in the identification of a large number of polymorphic markers utilized in this study to construct a genetic map and to identify QTL for the traits evaluated. This evaluation allowed the determination of genetic basis for the traits studied, and established the associations of QTL affecting spike-related traits on agronomic and quality traits. The results of the study are presented in this dissertation in three chapters as follow.
The first chapter of results, titled “Genome-wide genetic dissection of supernumerary spikelet and related traits in common wheat (Triticum aestivum L.)”, presents the first report on genetics of the
SS trait at the whole genome scale. The SS trait was dissected into seven SS-related traits in order to improve the power of QTL detection. Currently, this paper is being submitted to a scientific journal. The second chapter entitled, “Genome-wide mapping of spike-related and agronomic traits in a common wheat population derived from a supernumerary parent and an elite parent” describes the association of QTL affecting spike-related traits and agronomic traits in wheat. The QTL mapping of 10 spike-related traits and 10 agronomic traits in the RIL population is described in detail. Likewise, the description of allelic variations for identified QTL and their potential implementation in breeding programs
2 is discussed. Finally, the third chapter of results is entitled “Genetic dissection of quality traits in wheat using an elite× exotic RIL population”. This chapter describes the QTL mapping of 8 quality traits in the population and the associations of these traits with spike-related traits. Likewise, the utility of alleles derived from each parent for increasing the phenotypic values of some quality traits is discussed.
1.2. Objectives
1.2.1. General Objective
To determine the genetic basis of supernumerary-spikelets-related, spike-related, agronomic and quality traits in wheat using a RIL population derived from the cross a wheat elite line and an exotic line with supernumerary spikelet (SS).
1.2.2. Specific Objectives
To assess the phenotypic variations and correlations of spike-related, agronomic, and quality
traits in a RIL population derived from the cross a wheat elite line and an exotic line with SS.
To determine Quantitative Trait Loci (QTL) associated with supernumerary-spikelet-related traits
at whole genome level.
To determine QTL of spike-related traits and agronomic traits at whole genome level.
To study the relationships between QTL affecting SS, other spike-related traits and agronomic
traits.
To determine QTL associated with quality traits at whole genome level.
To study the relationships between QTL affecting SS and quality traits.
To identify QTL and/or alleles with potential use in breeding programs.
3 1.3. Literature Review
1.3.1. Branched spikes
1.3.1.1. Natural variation for branched spikes
The genus Triticum normally bears one spikelet on each rachis node. However, in nature it is also possible to find a spectrum of spike variations in which a rachis node has more than one spikelet
(Sharman, 1967; Martinek and Bednár, 1998). These variations of spikes usually are categorized as branched spikes or supernumerary spikelets (SS). To some extent, branched spikes are variations in spike architecture; however their morphological characteristics as well as their genetics are different from the spikes discussed in the next sections.
Different approaches have been used to classify branched spikes and there is no a final consensus (Sharman, 1967; Koric, 1973; Pennel and Halloran, 1984b; Martinek and Bednár, 1998; Peng et al., 1998; Dobrovolskaya et al., 2009; Aliyeva and Aminov, 2011; Li et al., 2011). Following, we present a synthesis of the categories described by several authors:
Vertical Sessile Spikelet (VSS) or “Banana twin spikelets”: In these spikes, a rachis node has
two or three spikelets organized in vertical position (Martinek and Bednár, 1988). Sharman (1967)
called this abnormality “Banana” because the spikelet‟s positions in the nodes looks like “a
miniature bunch of bananas”. These types of spikes have been observed in nullisomic lines for
the chromosome 2B of T. aestivum (revised by Sharman, 1967). This is a characteristic with
dominant and recessive expression (revised by Martinek and Bednár, 1998).
Tetrastichon or tetrastichon sessile spikelets (TSS): In these types of spikes, a rachis node
has two or three sessile spikelets organized in horizontal alignment (Martinek and Bednár,
1998).
Floribuda: In these spikes, up to 10 spikelets can share a common rachis node (Dobrovolskaya
et al., 2009). Spike fertility is reduced because most of the inflorescence organs are not fully
developed due to the reduced space between spikelets (Martinek and Bednár, 1998). The term
“multirow spike” (MRS) refer to fertile floribunda types (Martinek and Bednár, 1998;
4 Dobrovolskaya et al., 2009), in which the central part and upper thirds of the spike have three
fertile spikelets per node (Dobrovolskaya et al., 2009).
Tibetan Tripe-spikelet wheat: In this landraces, three spikelets are found on each rachis node.
The trait is genetically stable and resembles to six-row barley. In one spike, it is possible to find
more than 50 spikelets (Yang et al., 2005; Li et al., 2011).
Spike branching, miracle spikes, wonder wheat, or mummy wheat: In these spikes,
miniature spikes are produced from the lowest buds (Sharman, 1944, 1967). Potentially, one of
these spikes can produce 120 spikelets and up to 150 kernels (Percival, 1921; Bonnet, 1966).
Miracle spikes were originally recognized in turgidum wheat. For several years, wide crosses
between branched-tetraploid wheat and hexaploid wheat were used to incorporate these spikes
in hexaploid lines (Martinek and Bednár, 1998). However, Koric (1973) discovered that these
spike types exist in hexaploid wheat and named this wheat as Triticum aestivum ramifera.
Usually, wheat plants with miracle spikes have variation in the expression of branching in their
primary stem and tillers. Therefore, it is possible to find branching spikes as well as conventional
spikes in the same plant. This phenomenon is called „hetero-branching‟. Apparently, other
factors, such as plant density, determine this condition (Denčić, 1988; Huang and Yen, 1988;
Klindworth et al., 1990a; Martinek and Bednár, 1998).
Vavilovoid or Pulled out spikes: These spikes are similar to miracle spikes. However, in
vavilovoid spikes, the rachillas are exceptionally long and each spikelet resembles “a telescope
pulled out” (Sharman, 1967). This spike variation is usually present in the hexaploid wheat
Triticum vavilovi Jakubz and Triticum jakubzineri Udacz. Et Schachm (Sharman, 1967, Aliyeva
and Aminov, 2011).
Forked, Y-shaped heads: Sharman (1967) describes this spike type as the rarest of spike
abnormalities. According to the author, in this type of spike a bifurcation of the rachis axis
produces a “Y shape with two identical arms.
In branched spikes, two spikelet configurations have been described: 1) extra sessile spikelets at a rachis node, which are predominant in hexaploid wheat; and 2) spikes with additional spikelets
5 extended on rachillas, which are predominant in tetraploid wheat (Pennel and Halloran, 1984a, 1984b).
Pennel and Halloran (1983) demonstrated than both configurations are controlled by one recessive genes
(see section 1.3.1.3) and suggest using the term SS to include both type of spikelet organization. Since then, different authors have followed this suggestion (Pennell and Halloran, 1984a, 1984b; Peng et al.,
1998; Dobrovolskaya et al., 2009; Sun et al., 2009; Aliyeva and Aminov, 2011).
The number of SS is influenced by the environment. Sharman (1944) reported that branched germplasm is expressed under short days and low temperatures. Pennel and Halloran (1984a, 1984b) demonstrated that generally, a strong vernalization response and winter sowing, in addition to short photoperiods and low temperature, are conducive for the expression of SS. However, interactions between these factors were observed. For instance, the lines with strong or moderate vernalization response have an increment in the number of SS when they are grown under 24-h photoperiod and low temperature. Likewise, the lines with weak vernalization response have an increment in the number of SS when they are grown under short photoperiods and low temperature (Pennel and Halloran, 1984a).
Genes also play an important role in the expression and stability of SS. According to Pennell and
Halloran (1984a, 1984b), in branched stable lines, a high expression of SS is exhibited in different environments; meanwhile, in branched instable lines, the highest values of SS are observed only under conducive environments. These observations suggest differences in genetic constitution between lines, or differential gene × gene interaction in each line. Further discussion of the genetics of branched spikes is provided in this chapter.
1.3.1.2. Associations between branched spikes and other wheat characteristics
SS have been presented as one way to increase the yield potential of wheat due to the high number of florets producing caryopsis (Pennell and Halloran, 1983, 1984b, Hucl and Fowler, 1992; Peng et al., 1998). However, branched varieties are far from meeting this expectation. Salunke and Asana
(1971) when compared one branched variety and one normal-ear type variety (“Kalyan Sona”) found that although the production of dry matter was similar in both lines, the branched-spike produced a very small kernel number per spike, resulting in reduced grain yield. Similarly, Rawson and Ruwali (1972) compared the grain yield of one branched variety of wheat and two checks under irrigated conditions. Among these the checks was an adapted cultivar (“Kalyan Sona”), and the other one was an exotic cultivar (“Late 6 Mexico”). Their results showed that the branched line yield less compared to “Kalyan Sona," but exceeded the performance of the exotic cultivar. Likewise, they reported sterility in many of the spikelets in this line, resulting in low number of kernels per spike. Hucl and Fowler (1992) compared two varieties of spring wheat with a tetraploid line named “Branched Spike Wheat” (BSW) and observed that the branched cultivar produced lower number of spikes per m-2 and had the lowest grain yield regardless of the environment and seeding rate. Interestingly, under low precipitation rates during the growing season,
Hucl and Fowler (1992) found that the branched lines had reduced spikelet formation as well as the kernel number.
Despite the poor grain yield performance of the branched genotypes, some authors state that grain yield can be improved in some SS lines. For instance, Saluke and Asana (1971) suggested that the grain yield in branched lines can be increased if the fertility of the florets is improved. According to
Rawson and Ruwali (1972), spikelet sterility is a characteristic associated with the whole plant physiology and not only related with the spike development. Similarly, Pennell and Halloran (1984a, 1984b) suggested that lines with a stable and high expression of SS could be used to incorporate this trait in commercial wheat varieties.
Associations between branched spikes and other agronomic traits (other than grain yield) have also been studied. Koric (1973) reported a large number of kernels per spike in germplasm with SS but had low thousand kernel weight (TKW). Pennel and Halloran (1983), however, could not find any relationship between SS and plant height, TKW and days to emergence. Millet (1986, 1987) reported strong associations between heading date and spikelet number in a multi-spikelet line. On the other hand, in the specific case of the Tibetan Triple-Spikelet wheat, some lines have excellent seed production (>120 seeds per spike), and normal TKW (Yang et al., 2005). Recently, Zhang et al. (2012) developed four near isogenic lines (NIL) with variations in the alleles for two genes involved in the production of SS. They found that the NIL with SS had higher grain number, number of spikelets and fertile florets, but lower grain weight as well as late maturity. Finally, there is hardly any report which has attempted to study the associations of SS and quality traits, such as flour yield, grain protein content and gluten strength. To our best knowledge, the results reported in this study are the first of this type.
7 1.3.1.3. Genetics of branched spikes
The genetics of branched spikes has been studied for almost a century. In tetraploid wheat,
Percival (1921) and Sharman (1942, 1967) described that the branched –headed character is controlled by a recessive gene. Sharman (1944), suggested the symbol bh for the recessive allele that produce the branched phenotype. However, after the discovery of branched germplasm in hexaploid wheat (Koric,
1973), different inheritance patterns have been suggested. Table 1-1 summarizes the main genetics studies since 1973.
Koric (1973) reported that the branched spikes of hexaploid wheat are controlled by a complex of genes. Three important factors, Ramifera (Rm), Tetrastichon (Ts) and Normalizator (Nr) were reported.
The factor Rm and Ts work in complementary action in the formation of the branched spike; meanwhile, the Nr is a repressor of the branched phenotype. Therefore, the branched phenotype is possible only when the inhibitor is silenced or absent.
Pennel and Halloran (1983) reported a detailed study of inheritance of SS in hexaploid wheat.
Crosses were made between hexaploid wheat line with SS and three commercial hexaploid semi-dwarf cultivars („Condor‟, „Egret‟, and „Phoenix‟). They attempted to perform similar crosses with a branched tetraploid wheat line, but most of the F1 seed did not germinate due to the unbalanced chromosome number in the gametes of the pentaploid hybrid. In hexaploid wheat, the analysis of F1, F2, and backcrosses showed that this trait is controlled by two independent recessive genes and one independent repressor gene. The recessive condition was evidenced by the absence of SS in the F1 generation as well as in the F1 of the first backcross when the recurrent parents were „Condor‟ and „Egret‟. In F2 a ratio
15: 1 was observed, which fit with the presence of two recessive genes. Interestingly, in the commercial cultivar „Phoenix‟, the presence of one recessive gene for SS was observed, which was confirmed by the presence of SS in the F1 plants of the first backcross when „Phoenix‟ was the recurrent parent. The authors suggested that „Phoenix‟ differed from the parental line with SS in only one of the genes involved in SS characteristic. Most likely, this gene is a suppressor which inhibited the expression of SS in
„Phoenix‟. Additional evidence of this gene repressor was observed in the failure to fit a 3:1 (Normal: SS) ratio in the F1 of backcrosses when the SS was the recurrent parent. The authors observed that the cultivar „Phoenix‟ had a large number of spikelets and nodes per spikes in relation to the other
8 commercial cultivars studied. They suggested that these characteristics can be related to the presence of some of the branched genes. This suggestion opens the door to the speculation that some of the higher yielding wheat cultivars have the presence of some of the branched alleles suppressed by one gene.
Millet (1986, 1987) crossed the multispikelet line “Noa” with the conventional-spike-type line
“Mara” and with monosomic lines derived from “Mara.” “Mara” also carried day-length insensitive allele
Ppd1 on its 2D chromosome, which causes early heading date. Phenologic and morphologic studies stated that “Noa” had a longer spike development phase, higher initial number of spikelet primordia, reduced rate of spikelet production and later heading than “Mara.” The monosomic 2D line of “Mara” was the only monosomic line with a reduced final number of spikelets (Millet, 1987). Likewise, this monosomic line increased the duration of the spikelet development phase, causing a decrease in the rate of spikelet production (rate determined as the ratio between the produced number spikelets after double ridge stage and the duration of the spikelet phase). The monosomic F1 produced from the cross of the monosomic 2D line and “Noa” had increased number of spikelets and later heading date compared to the euploid F1. This result demonstrated that the chromosomes 2D of „Noa‟ carries recessive alleles for the number of spikelets per spike and delayed heading date. Chromosomes 5D and 7A were also reported to have minor effects on spikelet number (Millet, 1987).
In common wheat, Denčić (1988) studied the F1 and F2 progenies of three crosses involving branched germplasm (normal spike x branched spike; normal spike x tetrastichon spike; and tetrastichon spike x branched spike). The progeny analysis of these crosses suggested the presence of an inhibitor of the branched phenotype (N) and two additional genes (R and T). Contrary with previous studies, Denčić
(1988) suggested that R and T had a complementary gene action which determined the branched or tetrastichon phenotype. Thus, in absence of the inhibitor, the genotypes RRtt or rrTT determine spike with tetrastichon characteristics; while the genotypes R_T_ determine spike with branched characteristics.
Huang and Yen (1988) found four bh genes associated with branchness through the progeny analysis of F1 and F2 of 18 crosses between branched lines and non-branching lines of common wheat.
According to these authors, the four genes are independent, recessively inherited and with different contribution to the phenotype. The analyses of six reciprocal crosses showed that when the non- branched phenotype is used as male, the frequency of branched spikes in F2 is less than the frequency
9 observed when the non-branched phenotype is used as female. This result suggested the influence of cytoplasmic genes on branched spikes in common wheat. On the other hand, Huang and Yen (1988) classified the branched spikes in long branch (LB), short branch (SB), lateral supernumerary spikelet
(LSS), opposite supernumerary spikelet (OSS) and normal multispikelet (NMS). They found that under different environments and/or genetic backgrounds, one genotype can express LB, SB, LSS or NMS. In the case of OSS Huang and Yen (1988) suggested that this type of spike is controlled by another gene system.
Table 1-1. Summary of the main genetic studies for SS in wheat during the 1983 and 2012 period Plant Reference Wheat sp. Inheritance suggested Location material Pennel and Halloran F , F , Recessive: Two genes Hexaploid 1 2 N.A (1983) backcross and one repressor. F , F , 1 2 Recessive Major effect backcross, Millet (1986, 1987) Hexaploid on 2D; Minor effect on 2D, 5D, 7A monosomic 5D and 7A. lines Recessive and cytoplasmic: Two genes Denčić (1988) Hexaploid F1, F2 with complementary NA action and one repressor gene. F1, F2, Recessive: four Huang and Yen backcross, independent genes with Hexaploid NA (1988) reciprocal different contribution on crosses the phenotype. Quantitative: A single Klindworth et al. F to F , Tetraploid 1 6 major gene and several NA (1990a) backcross minor genes „Langdon‟ D- Tetraploid: 2A Klindworth et al. Tetraploid and D genome Recessive and 2B (1990b) genome disomic SbLi Hexaploid: 2D Monosomic 2D, 4A, 4B Peng et al. (1998) Hexaploid Recessive, polygenic analysis and 5A Two dominant genes Hexaploid wheat Sun et al. (2009a) F , F , F with complementary N/A and rye 1 2 3 action Dobrovolskaya et al. Hexaploid F Recessive. One gene 2D (2009) 2 F , F , Yang et al. (2005, 1 2 Hexaploid backcross, F Recessive. Two genes 2A 2009) 2 generation Aliyeva and Aminov F , F , Complex hybrid 1 2 Recessive N/A (2011) backcross Hexaploid, Gene Zhang et al.(2012) introduced from NILs Recessive. Two genes N/A durum
10 SS in tetraploid wheat were studied by Klindworth et al. (1990a). A SS wheat cultivar was crossed with the normal-spike “Langdon” durum, and the F1 was backcrossed to each parent. The authors classified the spikes in three basic types: normal spikes, four-rowed spikes and ramified spikes. The analysis of segregation in F3 and BC1F2 families indicated that the SS gene is controlled by a major recessive gene and numerous minor genes, suggesting that the SS is quantitatively inherited. In addition, the stability observed through generations in the types of branchness (four rowed or ramified) could not be explained by environmental conditions. If the plants were homozygous dominant for the major gene, then the phenotype should have normal spikes. However, if the plant was homozygous recessive for the major gene, the phenotype observed is ramified although minor genes can affect its expression.
Heterozygous plants for the major gene results in normal spikes, but high frequency of the minor genes can produce SS. In a subsequent study, Klindworth et al. (1990b) used a set of “Langdon” D-genome disomic substitution lines, to precisely identify the chromosomal location of SS genes. A major gene was identified on chromosome 2A in durum, and one minor gene was identified on chromosome 2B.
Additional experiments with a “Langdon” 2A telosomic line located the major gene on the short arm of the chromosome 2A. Interestingly, Klindworth et al. (1990b) noticed that 2D monosomic addition lines had inhibitory effect on the expression of SS. This effect was larger in the 2D monosomic addition lines derived from “Langdon” 2D(2A) than “Langdon” 2D(2B). Considering that D chromosomes come from
“Chinese spring” wheat, they concluded that an inhibitor for SS is located on chromosome 2D in hexaploid wheat. On the other hand, further studies with molecular markers confirmed the presence of a recessive locus for the branched head (bh) phenotype on the short arm of chromosome arm 2A in durum wheat which was associated with the microsatellite marker Xgwm425 (Haque et al., 2012).
Peng et al. (1998) found that genes for branched spikes were present on chromosomes 2D, 4A,
4B and 5A in the hexaploid wheat line “Yupi Branching”. They used monosomic lines as Miller (1986,
1987) did previously.These monosomic lines were derived from “Chinese spring” wheat which was crossed with “Yupi Branching”. In this study, the disomic F1 hybrids did not express the branched character; therefore, they concluded that all the genes for branchness were recessives. They found plants with branched spikes in 2D, 4A, 4B and 5A in F2 monosomic populations; although the largest number of branched plants was observed in the 2D F2 populations. According to the authors this result suggests that
11 2D carries the genes with larger effect on branched spikes. On the other hand, the number of branched plants in the disomic and monosomic subpopulations indicated that the chromosomes 4A and 5A are strong effective bh genes, and 4B is the location for a weak effective gene for this trait. Subsequent studies demonstrated that branching gene located on chromosome 5A and 4B increased the rachis node number and spike length of normal spikes (Peng et al., 2000).
Sun et al. (2009a) reported different conclusions on the genetics of SS in wheat. They worked with the SS line “518885”, an atypical germplasm derived from the cross between the octoploid Triticale and common wheat line “Fei 5056”. According to the authors, the line “518885” has SS but not branched.
It is important to note that Sun et al. (2009a) reported other system of classifications of SS: spikes containing over 28 spikelets are classified as SS, while with less than 28 spikelets per spike are classified as normal spike. The authors did not explain if the SS phenotype is produced by a large number of rachis nodes bearing one spikelet or by each rachis node bearing more than one spikelet (sessile-type spikelet configuration). The line “518885” was crossed with seven commercial lines of common wheat in order to study the inheritance of SS. Their results showed that the SS character is controlled by two dominant genes with complementary action (epistasis). Moreover, it was also suggested that these genes are derived from rye (Secale cereale) and not from wheat (Su et al., 2009a).
Dobrovolskaya et al. (2009) report was the first molecular mapping of genes study addressing the
SS character. They worked with the floribunda type (MRS, see section 1.3.1.1) of wheat, and with the
“monstrosum ear” from rye. Two F2 populations were developed in order to map the MRS trait: The population-I was the result from the cross between “Rŭc163-1-02” and “Sol149-1-02”, whereas population-II was developed from the cross between “Rŭc167-1-02” and “Sol149-1-02”. The Rŭc lines are
MRS types, meanwhile the line “Sol149-1-02” is a conventional wheat type. The two populations were genotyped with a set of SSR markers belonging to chromosomes 2A, 2B, 2D and 4A. In both
F2populations, the results showed a 3:1 (Normal: MRS) ratios. Consequently, the trait was controlled by one recessive gene. In both populations, the SS traits co-segregated with the microsatellite locus
Xwmc453 on chromosome 2D. The authors named the mutant allele mrs1 and the wild-type allele Mrs1.
Subsequent analysis with chromosome deletion bin lines delimited the physical location of the gene Mrs1
12 into the distal half of chromosome 2DS. A consensus map 2S showed that this gene (Mrs1) is located in a gene rich region named 2S0.8 (Erayman et al., 2004).
Yang et al. (2005) and Li et al. (2011) described the genetics of another type of spike with variation in the spikelets number: the Tibetan triple-spikelet wheat. Yang et al. (2005) reported that this trait was controlled by two independent recessive genes, which they called Ts1 and Ts2. In a subsequent study, Li et al. (2011) identified one major QTL linked to the triple spikelet trait on the chromosome 2A using two F2 populations derived from the crosses of the Tibetan triple spike line “TTSW-5” with two conventional wheat lines “Jian 3” and “Chuanmai 55”. This QTL was named qTS2A-1 and explained
33.1% of the phenotypic variation. Unfortunately, the authors were not able to detect other gene controlling the triple-spikelet phenotype and suggested more saturation of this molecular map in order to find minor genes associated to this trait. The genetics of another atypical spike variation was studied by
Aliyeva and Aminov (2011). They studied a new branched spike form named 166-Schakheli. This line was obtained from the cross between a hexaploid wheat line 171AC {[(T. durum Desf. X Ae. Tauschii
Coss.) x S. cereale L. ssp. segetale Zhunk} x T. aestivum L. “Chinese Spring”] and durum wheat variety
T. durum Desf. “Bereketli-95”. The spikes of the line 166-Schakheli resemble the vavilovii type (see section 1.3.1.1) but differ in more extended rachillas. The authors of this study found that the branched trait was recessive and the gene is dose independent.
Recently, Zhang et al. (2012) reported the development of NIL for two recessive genes (Sb) related to SS located on chromosome 2A and 2D. These genes were identified in hexaploid wheat line
“Fen33” (a SS line resulted from the interspecies hybridization of durum x common wheat (reviewed by
Zhang et al., 2012). To develop these NIL, “Fen33” was the donor parent of Sb genes; meanwhile, the conventional wheat line “Taishan” was the recurrent parent. Four NIL were obtained: 1) double recessive
(DR), sb1sb1 sb2sb2; 2) double dominant (DD), Sb1Sb1 Sb2Sb2; 3) single recessive 1 (SR1), Sb1Sb1 sb2sb2; and 4) single recessive 2 (SR2), sb1sb1 Sb2Sb2. Genetic studies using these NIL showed that
NILs with only DR alleles at both genes loci had SS suggesting that the production of SS results from the interaction of both genes. DR genotype had a high number of grains, spikelets, and fertile flowers, but a low grain set and grain weight. Both SR types had normal spikes. Interestingly, SR1 needed several days
13 for heading and anthesis; while, SR2 reduced the number of fertile florets and grains. Therefore, the Sb genes have pleiotropic effects on these traits and/or they are linked to genes associated to them.
1.3.2. Spike size traits
1.3.2.1. Natural variation for spike architecture
Spike architecture includes traits determining spike compactness, such as spike length and number of rachis nodes. The spikes of common wheat differ greatly in form and degree of compactness.
Spikes usually are classified in four general categories that incorporate length and width: 1) fusiform, in which the spikes taper toward the apex or from the middle toward both base and apex, 2) oblong, in which the spikes are uniform in width and thickness, but tend to be longer than wide, 3) clavate, in which the spikes are larger and more dense at the apex, and 4) elliptical, in which the spikes are short and uniformly rounded at both the base and apex but are flattened on the sides (Briggle and Reitz, 1963).
Differences in spike architecture are the result, in part, of differences in density (Briggle and Reitz
1963). Most of authors define spike density as the number of spikelets per longitude of spike (Sourdille et al., 2000b; Jantasuriyarat et al., 2004; Ma et al., 2007, Cui et al., 2012). However, other approaches have been used to categorize the spike density. For instance, Briggle and Reitz (1963) consider spike density as the distance in mm occupied by 10 internodes of the rachis measured from the center of the spike.
Considering this definition, these authors suggested three spike density categories: 1) lax, in which 10 internodes occupy from 50 to 75 mm; 2) mid-dense, in which 10 internodes occupy from 35 to 60 mm; and 3) dense, in which 10 internodes occupy from 20 to 45 mm.
Differences in spike morphology have been recognized as a criterion of further classification of bread wheat. Currently, scientists have described six subspecies of bread wheat : 1) T. Spelta L. [Triticum aestivum (L.) Thell. ssp. Spelta (L.)]; 2) T. Compactum Host. or club wheat [T. aestivum (L.) Thell. ssp. compactum (Host.) Mk., T. vulgare compactum Alef., T. sativum compactum spp compactum Asch. and
Graeb.]; 3) T. sphaerococcum Perc. [Triticum aestivum (L.) Thell. Ssp. Sphaerococcum (Perc) Mk]; 4) T. macha Dek. and Men.[Triticum aestivum (L.) Thell. ssp. macha (Dek. and Men) Mk. T. tuballicum Dek., T. imeriticum Dek.] ; 5) T. Vavilovi (Tum.) Jakubz. [Triticum aestivum (L.) Thell ssp. vavilovi (Tum.) Sears]; and 6) T. aestivum L. [Triticum aestivum (L) Thell ssp. vulgare (Vull) Mk., T. vulgare Vill., T. vulgare Host.,
14 T. hybericum L., T. sativum Lamk., T. sativum Pers.] (Miller, 1987). The differences between these hexaploid forms are caused partially by genes affecting the gross spike architecture (see section 1.3.2.3).
The spikes of spelt wheat are long and lax, and their spikelets are well separated from each other on the rachis. These spikes are 10 to 15 cm long with 16 to 23 spikelets (Percival, 1921). Club wheat is characterized by the short, stiff, and compact spikes. Usually, the club spikes are of uniform density and oblong with a spike length of 3.5 to 6 cm with 17 to 25 closely packed spikelets (Percival, 1921). T. sphaerococcum is characterized by short and dense spikes and small near-hemisperical grains (Miller,
1987). T. macha is distinguished by laterally compressed wide fragile spikes (Miller, 1987). T. vavilovi has branched spikes with a fragile rachis, and glumes attached to the grain (Peterson, 1965) (for more details see section 1.3.2.3). Finally, in the case of T. aestivum aestivum or common wheat, the spikes range in length from 6 to 18 cm, with an average of 20 spikelets per spike (Percival, 1921). A broad variety of spike architectures resulted from variations in spike length, number of spikelets, density, immature spikelets, etc. are present in common wheat.
1.3.2.2. Associations among spike architecture and other wheat traits
Compared to cultivated common wheat (ssp. aestivum), knowledge of agronomic and quality performance of other five subspecies is lacking in general. The spelt wheat is tall with lodging problems and low yield (Bertin et al., 2001). In terms of quality, spelt has high flour protein content and short dough- mix times indicating weak gluten (Wilson et al., 2008). Club wheat (Triticum aestivum ssp. compactum) is a type of soft white wheat with a significant role worldwide in drylands (Johnson et al., 2008). Club spikes are one-half the length of common wheat spikes. However, the spikelet number of club wheat is not different than non-club types. This sub-species has small grains compared to common wheat, but the decrease in spike size is compensated by an increase in the seed number (Zwer et al., 1995). Thus, the yields reached by the club wheat are closely comparable to the yields produced by common wheat. The limited spaces within the club spikes constrain a full kernel development; resulting in a lower test weight
(Steven et al., 1994). Rarely, other subspecies of wheat have been studied in terms of agronomic and quality traits. Overall, we know that T. vavilovi has good performance in dry growing conditions (Peterson,
1965) and, T. macha is cultivated in Georgia with salt tolerance (Badridze et al., 2009).
15 In common wheat, spike characteristics determine the number of kernels per spike; therefore, in part, they are influencing grain yield (Cui et al., 2012). Spike length is positively associated with grain yield, grain weight per spike, grain-weight, plant height, culm length, and no. of spikelets per spike (Kato et al., 2000; Börner et al., 2002). Meanwhile, spike length is negatively associated with the tiller number per plant (Kato et al., 2000). There are contradictory conclusions about associations of spike length and days to heading. Kato et al. (2000) found negative correlation between these traits, while Börner et al.
(2002) reports a positive association. On the other hand, spikelets per spike is positively associated with yield, plant height, culm length, grain weight per spike and grain weight (Kato et al., 2000). However, the number of spikelets per spike is negatively correlated with days to heading (Kato et al., 2000). Both spike length and number of spikelets per spikes are positively associated (Kato et al., 2000). In terms of quality,
Steve et al. (1994) reported no relationship between spike-related traits (spike length and number of spikelets) and test weight. However, they found negative correlation between test weight and kernels per spike and kernels per spikelet.
The association between the number of spikelets per spike and photoperiod has been studied in detail. Rahman and Wilson (1977) demonstrate that several wheat cultivars increase their number of spikelets as the photoperiod decreased from 24 to 8 hours. Same authors found that the length of spikelet formation and rate of spikelet initiation are the most important factors behind number of spikelets per spike. They found a positive correlation (r = 0.55) between spikelet number and length of the spikelet phase (starting from the initiation of floral structure to the formation of terminal spikelet), but a negative correlation between the rate of spike initiation and length of spike phase (r = -0.93). Rahman and Wilson
(1977) hypothesized that the rate of spike initiation and the length of spike phase can be controlled by separated factors (such as hormones). Thus, favorable conditions for one of the factors inhibit the activity of the other factor. This compensatory mechanism (feedback) keeps an adequate number of spikelets per spike because in nature, the cultivars with too few or too many spikelets are at disadvantage (Rhaman and Wilson 1977).
Recently, Cui et al. (2102) reported an association between spike-related characteristics in two
RIL populations. They collected information on spike length, spike density, spikelet number, basal sterile spikelet number, tops sterile spikelet number, sterile spikelet number in total, and fertile spikelet number.
16 For most of these traits, they found weak correlations. The most important associations were: 1) positive correlation of spike length with spikelet number and fertile spikelet number; 2) positive correlation of spikelet number and the number of fertile spikelets; 3) positive association of spike density with number of spikelets per spike and number of fertile spikelets; 4) negative association of spike density and spike length; 5) positive association of sterile spikelet number with top sterile spikelet number and basal sterile spikelet number; and 6) negative correlation between sterile spikelet number and the number of fertile spikelets.
1.3.2.3. Genetics of spike architecture
In wheat, four major complexes of genes controlling spike architecture were reported: Q, C, S1 and V. These genes determine the phenotype of the subspecies Triticum aestivum ssp. aestivum
(QQccS1S1), T. aestivum ssp. spelta (qqccS1S1), T. aestivum ssp. compactum (QQCCS1S1), T. aestivum ssp. macha (qqCCS1S1), and T. aestivum ssp. sphaerococcum (QQccS1S1) (Miller, 1987; Jantasuriyarat et al., 2004) (see section 1.3.1.1). T. aestivum ssp. vavilovii is an exception because it is produced by the
V factor (Miller, 1987). The complex Q gene is located on the chromosome arm 5AL and determines whether a spike is normal or speltoid. The allele q is considered as the speltoid gene. Therefore, wheat cultivars with the dominant Q allele are shorter than those with the recessive allele, but they have compact spikes, non-brittle rachis, and free-threshing seeds (Jantasuriyarat et al., 2004). The complex C is located close to the centromere of chromosome 2D and determines if a spike is lax or compacted.
Thus, the dominant allele C produces club wheat (Triticum compactum group). The genes C inhibit the free-threshing character from the complex Q. Therefore, at least for tenacious glumes, the C genes are dominant over Q genes (Jantasuriyarat et al., 2004). The complex S1 is located on the short arm of chromosome 3D, and it is associated with T. aestivum ssp. sphaerococcum. On the other hand, the complex V of T. aestivum ssp. vavilovii is located on chromosome 5A.
In common wheat, the evidence collected during the last 15 years suggests that variation in traits such as spike length and spike density is also caused by genes other than Q, C, S1 and V. The allelic variation on these gene complexes is reduced in this subspecie, and differences in spike morphology cannot always be explained by these complexes (Sourdille et al., 2000b; Cui et al., 212). Instead, it is considered that spike length, number of spikelets (in non-branch spikes) and spike density in common 17 wheat is controlled by multiple QTLs distributed throughout the wheat genome (Sourdille et al., 2000b; Li et al., 2002; Börner et al., 2002; Jantasuriyarat et al., 2004; Verma et al., 2005; Marza et al., 2006; Kumar et al., 2007; Li et al., 2007; Ma et al., 2007; Chu et al., 2008; Cui et al., 2012). These QTLs are highly influenced by environment. Table 1-2 shows a summary of QTL associated with spike-dimensions traits in common wheat (updated from Kumar, 2009).
18
Table 1-2. Summary of QTL information on studies for spike architecture related traits (adapted from Kumar, 2009)
Trait QTL Location Pop. Marker Reference
Spike Length 1AL, 2BS, 2DS, 4AS, 5AL D-H RFLP, SSR Sourdille et al. (2000b)
1BS, 4AL, 4AS, 5AL RIL RFLP, SSR Börner et al. (2002)
1AL, 1BS, 4AL, 7AL RIL RFLP, SSR Li et al. (2002)
1BS, 4AL, 4DL, 7AS RIL RFLP, SSR Jantasuriyarat et al. (2004)
2DS, 4D D-H SSR Verma et al. (2005)
1AL, 1AS, 1B, 2BL, 2BS, 3BL, 4BL, 5BL, RIL AFLP, SSR Marza et al. (2006) 7AS, 7BL 1AS, 1BL, 1DL,2BL, 2DL, 2DS, 4AL, 5AL, RIL RFLP SSR Kumar et al. (2007)
19 5DL
1AS, 2DS, 4AS, 5AS, 5BL, 7DS RIL and IF2 RAPD, SSR Ma et al. (2007)
3DS, 4AL, 5AL D-H SSR Chu et al. (2008)
1A, 2D, 6A D-H AFLP, RAPD, SSR Heidari et al. (2011)
G-SSR, EST-SSR, 1B, 1D, 2A, 2B,3B, 4A, 5A, 5B, 6B, 6D, 7A, RIL ISSR, STS, SRAP Cui et al. (2012) 7D and RAPD
Substitution Spikelets/spike 5AS, 5AL RFLP Kato et al. (2000) lines for 5A (Continues)
Table 1-2. Previous QTL studies for spike architecture related traits (continued)
Trait QTL Location Pop. Marker Reference
Spikelets/spike 2AS, 2BS, 5AL D-H RFLP, SSR Sourdille et al. (2000b)
2DS, 7AL RILs RFLP, SSR Li et al. (2002)
3AS, 3DL, 4AL, 7AL RILs RFLP, SSR Jantasuriyarat et al. (2004)
2BS, 2DS, 4AL, 4DS, 5AL, 6AS, 6AL RIL RFLP SSR Kumar et al. (2007)
1BL, 2DL, 3BL, 5AL, 5BL, 7AS, 7DS RIL and IF2 RAPD, SSR Ma et al. (2007)
5DL RILs SSR, EST-SSR, ISSR Li et al. (2007)
4DL D-H SSR Chu et al. (2008)
20
1A, 2A, 4B D-H AFLP, RAPD, SSR Heidari et al. (2011) G-SSR, EST-SSR, 1AS, 1B, 1DL, 2B, 3B, 4AL, 4D, 5B 5D, 6A, RIL ISSR, STS, SRAP Cui et al. (2012) 6B, 6D, 7B and RAPD Spike Density 1AL, 2AL, 2BS, 2DS, 4AS, 5AL D-H RFLP, SSR Sourdille et al. (2000b)
1BS, 2BL, 4AL, 5AL, 6AS RIL RFLP, SSR Jantasuriyarat et al. (2004)
1B, 4AL, 7BS, 7DL RIL AFLP, SSR Marza et al. (2006)
4AL RIL SSR, EST Kiriwi et al. (2007)
(Continues)
Table 1-2. Previous QTL studies for spike architecture related traits (continued)
Trait QTL Location Pop. Marker Reference
Spike Density 1AS, 2DS, 4AS, 5AS, 5AL, 5BL, 7DS RIL and IF2 RAPD, SSR Ma et al. (2007)
5AL, 5BL D-H SSR Chu et al. (2008)
2A, 2D, 4B, 6A D-H AFLP, RAPD, SSR Heidari et al. (2011)
G-SSR, EST-SSR, 1A, 2A, 2B, 2D, 3A, 3B, 4A, 4D, 5A, 5D, 6D, RIL ISSR, STS, SRAP Cui et al. (2012) 7D and RAPD
21
1.3.3. Spike awnedness
1.3.3.1. Natural variation of spike awnedness
The awns in cereals are considered rudimentary leaves (Grundbacher, 1963), and awnedness refers to the presence or absence of awns in the spikes. Börner et al. (2005) found associations between geographical origin and the presence/absence of awns. According to these authors, the absence of awns
(awnless) is a common characteristic in cooler, temperate climatic regions of northern and central Europe as well as northern America. On the other hand, the presence of awns (awned) is typical of
Mediterranean areas, northern Africa, and Middle East.
The physiological importance of the awns has been recognized through decades. For instances,
Olugbemi et al. (1976) demonstrated that the awns intercept 9% of the incident visible light and contribute
12.2% to canopy gross photosynthesis. Li et al. (2006) demonstrated the photosynthetic importance of the awns during dough-development stage and maturity. According to these authors, at these stages, the number of chloroplast and photosynthetic activity in the flag leaf are reduced, while the awns increase the number of these organelles as well their photosynthesis. Li et al. (2006) also demonstrated that the activity of the enzyme PECase (EC 4.1.1.3.1) is high in the awns than in the flag leaves. These authors suggested that this enzyme provided a new source of substrates for carbohydrate synthesis in the awns at grain filling, as well as a CO2 recycling activity which is desirable under drought conditions.
Considering awnedness, wheat varieties have been classified differently. Watkins and Ellerton
(1940), suggested the following classification of the Triticum varieties as: 1) bearded or fully awned; 2) tipped 1, in which very short awn tips at the base and center of the spike are observed, but in the apical region are awns with a length of 1 cm in average found; 3) tipped 2, which has short tips in all the spike;
4) half-awned, in which the awns gradually increase in length toward the spike apex; 5) Hooded bearded, in which the awns are short and curved; 6) Hooded beardless, which are similar to hooded bearded spike but with shorter awns; 7) Beardless, in which awns tips are hardly observed.
However, Briggle and Reitz (1963) classified the varieties as: 1) awnless or without awns; 2) apically awnleted, in which the apex of the spike has awnlets of 1 to 15 mm long; 3) awnleted, in which
22
awnlets of 3 to 40 mm are observed in all the spike, but increase in length toward the apex; and 4) awned, in which all lemmas have a defined awn.
1.3.3.2. Associations between spike awnedness and other wheat traits
The impact of wheat awns on agronomic and quality traits have been studied by comparing awned with awnletted (spikes containing tip awns) and awnless wheat lines (Atkins and Norris, 1955;
Grundbacher, 1963 McNeal et al., 1969; McKenzie, 1972; Olugbemi et al., 1976; Weyhrich et al., 1994).
Most of the studies reported an advantage in test weight and kernel weight in favor of the awned cultivars compared to the awnless or awnletted. Thus, an awned variety produces more starch for the kernels compared to awnless or awnletted varieties. In addition, under semi-arid conditions, the awns photosynthesize and translocate photosynthates to the kernel for several days (Grundbacher, 1963).
Similarly, Börner et al. (2005) observed a high frequency of awned germplasm in regions with severe drought stress (Mediterranean areas, northern Africa and Middle East)
The relationships between awns and yield have sometimes resulted in controversial reports.
McKenzie (1972) reported that yield in awnletted spikes was superior to the awned yield under both irrigated and not irrigated conditions. These results were contradictory to most the previous conclusions
(Atkins and Norris, 1955; Grundbacher, 1963; McNeal et al., 1969). However, later on, Weyhrich et al.
(1994) reported that the effects of awn suppression on grain yield in hard red winter wheat appeared to be genotype-dependent. This conclusion could explain some of the controversial results obtained in the past decades. However, additional research using modern approaches is needed to clarify these issues.
Few studies have been conducted to study the relationship between awns and wheat quality.
McNeal et al. (1969) reported a paradox in the breeding for awns in wheat. In general, producers prefer awned cultivars while the milling industry prefers awnless cultivars. According to their results, the awnleted wheat is useful for the milling industry since it produced 3.7% more flour than the awned population. At the same time, loaf volume was significantly higher for the awnletted compared to awned wheat cultivars. Differences between awned and awnleted wheat were not obtained in flour ash, flour protein, mixing time or bread quality.
23
1.3.3.3. Genetics of awnedness
The genetic control of awns has been studied for almost a century. Early in the 20th century, it was demonstrated that awnless condition is dominant compared to the awned condition, with at least two genes controlling this trait (Stewart, 1928). However, Watkins and Ellerton (1940) suggested the presence of three major genes leading the production of the awn classes: 1) The gene B1 controlling
Tipped 1 (and presumably half-awned); 2) the gene B2 related with Tipped 2 (and presumably half- awned); and 3) the gene Hd associated to hooded types (hooded bearded and hooded beardless).
Awned wheat plants have the alleles hd, b1 and b2 in homozygous condition, while the awnless phenotypes carry dominant alleles. For instance, the awnless varieties “Chinese Spring” and “Federation” have the gene combination Hd B2 and B1 B2 respectively (Sourdille et al., 2002). Nevertheless, Watkins and Ellerton (1940) suggested that other genes could be associated with the phenotypes where the awns length is equal in all parts of the spikes.
Using SSR, Sourdille et al. (2002) mapped a Double Haploid population segregating for awnness.
The parents of this population were the awnless line “Chinese Spring” and the awned lines “Courtlot”.
Two major QTLs were detected on chromosome 4AS and 6BL. Subsequently, these authors used deletion lines to confirm that the QTL on chromosome 4A belongs to the Hd gene; while the QTL on chromosome 6B is the B2 gene.
1.3.4. Variations in glume pubescences
1.3.4.1. Natural variation for glume pubescences
The glumes are two opposite rigid boat-shaped bracts located at the base of the spikelet. Their size, shape, texture, form of keel, and terminal tooth are characters used in wheat classification (Percival,
1921). However, the notorious difference between absence and presence of hairs (pubescences) on glumes has been considered a major criterion of classification of wheat varieties (Briggle and Ritz, 1963).
Most of the commercial wheat varieties are named glabrous because of lack of hairs on glumes. Instead, wheat varieties with hairs are denominated as pubescents. The glume hairs are unicellular structures that terminate in a fine point. The hairs vary in longitude; the long hairs ranged from 6 mm to 1.2 mm, while the shorter one measure from 2 mm to 4 mm (Percival, 1921).
24
In the past, the adaptive role of pubescent glumes has been discussed in few studies. Jain et al.
(1975) were not able to find a geographic pattern of the glume pubescence in a world collection of durum wheat. In a similar study, but using a world collection of bread wheat, Börner et al. (2005) found that this trait is randomly distributed at a low frequency throughout the world. However, 20% of the hairy accession were originated from northern Europe (Finland, Sweden), Asia, (Afghanistan, India, Iran, Nepal, Pakistan,
Turkey) and North Africa (Libya). Among these regions, the maximum frequencies of accessions with pubescences were found in Libya (65%) and Nepal (41%). Therefore, they concluded that their data provides some support for a positive effect of pubescence on dry and cold environments. On the other hand, Maes et al. (2001) reported a beneficial effect of pubescences at low temperatures under controlled condition in a growth chamber. Overall, they reported that floret temperature in pubescent varieties is high in relation to no-pubescent material. However, individual comparison between some lines indicated significant differences among few but not all the genotypes. Therefore, the authors concluded that in addition to the pubescences, other possible factors may be involved in frost tolerance. Maes et al. (2001) also reported that the presence of pubescences was also related to a delay in the time needed to reach damaging temperatures and pubescent plants produced more grains per spikelets compared with non- pubescent material.
1.3.4.2. Associations of glumes pubescences and other wheat traits
Studies reporting the associations of glumes pubescences with any other trait are scarce. Briggle and Sears (1966) reported association between the presence of pubescences on glumes and resistance to powdery mildew. Maes et al. (2001) found a positive relation between the number of kernels per spikelets and the presence of pubescences in the glumes under growth chamber conditions.
1.3.4.3. Genetic of glume pubescences
Gentically, the trait glume pubescence is designed by the symbol Hg. Percival (1921) described the pubescences on glumes as a characteristic with dominant inheritance and controlled by two alleles.
However, Sheybani and Jenkins (1961) proposed an allelic series controlling this trait in durum; Hg, hg, and hg1. According to these authors, when the pubescent varieties “reichenbachii” and “Golden Ball” are crossed with any glabrous variety, a ratio 3:1 of pubescent to glabrous (respectively) is obtained.
25
Nevertheless, when the pubescent variety “Khala” is crossed with a glabrous variety, a 3:1 ratio of glabrous to pubescent (respectively) is obtained. Therefore, the authors suggested that Hg was a dominant allele for pubescences, and hg is a recessive allele for glabrous, but dominant to hg1. Thus, the genotypes “reichenbachii” and “Golden Ball” are HgHg, Khala is hg1hg1, and the glabrous germplasm is hghg.
The gene Hg has been located on the short arm of chromosome 1A tightly linked to the locus Bg
(dominant gene that produces black color of glumes during maturation) and the gliadin locus (Gli-A1)
(Khlestkina et al., 2000). In addition, Hg is located close to a number of major fungal resistance genes.
Therefore, the absence/presence of pubescences could be used as a morphological marker for disease resistance selection (Khlestkina et al., 2006).
1.3.5. Genetics of several agronomic and quality traits in wheat
Since we mapped several genes affecting several agronomic and quality traits in this study, we consider that is important to describe previous QTL mapped for these traits. Kumar (2009) presented a summary of QTL mapping studies for some important traits in wheat. In Table 1-3 we are presenting a section of the summary reported by Kumar (2009) as well as an updated summary of most important QTL related to the agronomic and quality traits.
26
Table 1-3. Summary of information related to QTL governing agronomic and quality traits in common wheat
Trait Authors Chromosomal Location of QTLs Population Marker Agronomic Traits
Grain yield Groos et al. (2003) 2B, 3BS, 4AL, 4BL, 5AL, 5BL, 7DL RILs RFLP, SSR
Huang et al. (2003) 1AL, 1BL, 2AS, 2BL, 2DS, 2DL, 3BS, 4DS, 4DL, 5BS BC2F2 SSR
Huang et al. (2004) 1AS, 3DS, 4DL, 5AS, 5AL, 5BL, 6BS, 6DS, 6DL BC2F2 SSR
McCartney et al. (2005) 2AS, 2BS, 3DL, 4AL, 4DS D-H SSR, EST (SNP)
Huang et al. (2006) 5AL, 7AS, 7BS D-H SSR
Marza et al. (2006) 1AL, 1B, 2BL, 4AL, 4B, 5A, 5B, 6B, 7A, 7DL RILs AFLP, SSR Narasimhamoorthy et al. 2DS, 7DL BC F : SSR (2006) 2 2 4
27 Kirigwi et al. (2007) 4AL RILs SSR, EST
Kuchel et al. (2007) 1BS, 2DL, 3DL, 4AL, 4DL, 5BL, 6AL, 6DL, 7B D-H SSR
Kumar et al. (2007) 1AL, 2AS, 2DS, 2DL, 3BS, 4BL, 6AL, 6DL RILs RFLP, SSR SSR, EST-SSR, Li et al. (2007) 2AS, 2DL, 3BS, 6AL RILs ISSR Cuthbert et al. (2008) 1AS, 2DL, 3BS, 5AL D-H SSR
Heidari et al (2011) 6A, 6D D-H AFLP, RAPD, SSR
Bennet et al. (2012) 1A, 1B, 2A, 2B, 2D, 4D, 6D, 7A D-H DArT
Mergoum et al (2013) 5AS, 6BS D-H DArT, SSR (Continues)
Table 1-3. Summary of information related to QTL governing agronomic and quality traits in common wheat (continued)
Trait Authors Chromosomal Location of QTLs Population Marker Days to heading Keller et al (1999) 1A, 2A, 2B, 4A, 4B, 5A, 5B, 7B RILs RFLP, SSR
Börner et al (2002) 2DS, 5DL, 5DS, 7DS RILs SSR
Sourdille et al( 2000a) 1AL, 2BS, 4DL, 5AL, 7BS D-H RFLP, SSR, AFLP
Sourdille et al( 2003) 2B, 5A, 7B, 7D D-H RFLP, AFLP
Huang et al .(2004) 2DL, 3AL, 4AL, 7AL, 7DS BC2F2 SSR
Huang et al. (2003) 2AS, 2DS, 3BS, 5AL, 5BL, 6AL, 7BS BC2F2 SSR
Xu et al. (2005) 2D RILs AFLP, SSR
Marza et al. (2006) 3BL, 5B, 6B RILs AFLP, SSR Narasimhamoorthy et al. 28 2D, 3D BC2F : SSR (2006) 2 4
Cultbert et al. (2008) 1BS, 2D, 3A, 5AL, 6BS, 7BS, 7DL D-H SSR
Days To maturity Li et al. (2002) 2AS, 2DS RILs RFLP, SSR
McCartney et al. (2005) 3BS, 4AL, 4DS, 7DS D-H SSR
Marza et al. (2006) 1B, 3AS, 6B RILs AFLP, SSR
Huang et al. (2006) 2D, 5D, 7D D-H SSR
Cuthbert et al. (2008) 1BS, 3B, 5AL,5B, 6BS, 7A, 7BS, 7DL D-H SSR (Continues)
Table 1-3. Summary of information related to QTL governing agronomic and quality traits in common wheat (continued)
Trait Authors Chromosomal Location of QTLs Population Marker Plant height Cadalen et al. (1998) 3DL, 5AL, 5BL, 4BS, 4DL, 6DS, 7AL, 7BL D-H RFLP, SSR
Araki et al. (1999) 4AS, 4AL SbLi RFLP
Ahmed et al. (2000) 1AL, 1DL, 2BS, 2DS, 2DL 4BL RILs RFLP 1AS, 1BL, 2BS, 2DS, 3AL, 3BS, 3DL, 4AL, 4BL, 4DL, Börner et al (2002) RILs SSR 5DL, 6AS, 6BS Sourdille et al. (2003) 4BS, 4DL, 7AL, 7BL D-H RFLP
1AS, 1DL, 3AS, 3BL, 4AS, 4BL, 5AS, 5AL, 5BL, 6AL, Huang et al. (2004) BC F SSR 6DS, 7AS, 7AL, 7DS 2 1
McCartney et al. (2005) 2DS, 4BS, 4DS, 5BL, 7AL, 7BS D-H SSR, EST (SNP)
Huang et al. (2006) 4BL, 4DS, 5DL, 7BS D-H SSR
29
Marza et al. (2006) 2BL, 2BS, 2DL, 3BL, 4B, 6A RILs AFLP, SSR
Spielmeyer et al. (2004) 2DS, 6AS RILs SSR
Chu et al. (2008) 4DS, 5AL DH SSR
Wang et al. (2009) 1D, 2D, 3D, 4D RILs SSR
Bennett et al. (2012) 1D, 2D, 3A, 5A D-H DArT
Lodging Keller et al (1999) 1BS, 2AS, 2D, 3AS, 4AS, 5AL, 5BL, 6BL, 7BL RILs RFLP, SSR
Börner et al (2002) 2D, 6AS RILs SSR
McCartney et al. (2005) 3DL, 4BS, 4DS D-H SSR, EST (SNP) (Continues)
Table 1-3. Summary of information related to QTL governing agronomic and quality traits in common wheat (continued)
Trait Authors Chromosomal Location of QTLs Population Marker Lodging Verma (2005) 1D, 2B, 4B, 4D, 6D, 7D D-H SSR
Marza et al. (2006) 1B, 4AL, 5A RILs AFLP, SSR
Huang et al. (2006) 4D, 5A, 6D D-H SSR
Kernels per spike Börner et al (2002) 2DS, 4AL RILs RFLP, SSR
Huang et al. (2004b) 1DL, 2AS, 3DS, 6AS, 6AL, 7AS, 7AL, 7DS BC2F1 SSR
Marza et al. (2006) 1AL, 1B, 2BS, 2DL, 3BS, 4B, 6A, 7BS RILs AFLP, SSR Narasimhamoorthy et al. 3DS BC F2: SSR (2006) 2 4 1AL, 1BL, 2AS, 2BS, 2DS, 2DL, 3BS, 3DL, 4BS, 7AS, 30 Kumar et al. (2007) RILs RFLP, SSR 7AL
SSR, EST-SSR, Li et al. (2007) 1DS, 2AS, 3BS, 6AL,6BS RILs ISSR, SRAP, TRAP Cuthbert et al. (2008) 1AS, 2DL, 3BS, 5AL, 7AL D-H SSR
Wang et al.(2009) 1DL, 2AS, 2DS, 3A, 4D, 6DS RILs SSR
Heidari et al (2011) 1A, 2B D-H AFLP, RAPD, SSR
Bennett et al.(2012) 1A, 1B, 3A, 7ª D-H DArT
Mergoum et al. (2013) 5AS, 6BS RILs DArT, SSR (Continues)
Table 1-3. Summary of information related to QTL governing agronomic and quality traits in common wheat (continued)
Trait Authors Chromosomal Location of QTLs Population Marker 2 Spikes per m- Huang et al (2003 1BL, 2AL, 2DL, 3BS, 4DS, 5DL, 6DL, 7AS BC2F2 SSR
Huang et al. (2004) 1BS, 7AS BC2F1 SSR
Marza et al. (2006) 3BS RILs AFLP, SSR
Kirigwi et al. (2007) 4AL RILs SSR, EST
Li et al. (2007) 1AL, 1DS, 2AL, 2DL, 3BS, 3BL, 4BL, 7DL RILs SSR, ISSR,
Cuthbert et al. (2008) 3BS, 5AS, 5AL, 5BS, 7DL D-H SSR
Heidari et al (2011) 1A, 2D, 7ª D-H AFLP, RAPD, SSR
Bennett et al (2012) 1DL, 2B, 2DS, 4BL, 6A D-H DArT
31
Quality Traits Thousand-Kernel- Araki et al. (1999) 4AL-4AS SbLi RFLP weight Börner et al. (2002) 5A RILs RFLP, SSR
Kato et al. (2000) 5AL RILs RFLP
Groos et al. (2003) 1D, 2BS, 2DS, 3AL, 5BL, 6AL, 6DS, 7AS, 7DL RILs RFLP, SSR
Huang et al. (2003) 2AS, 2DL, 4DS, 5BS, 7AS, 7BL, 7DS BC2F2 SSR 1BS, 1DL, 2AS, 2DL, 3AL, 3BS, 3BL, 3DS, 4BL, 6AS, Huang et al. (2004) BC F SSR 6AL, 7AS, 7AL, 7DS 2 1 (Continues)
Table 1-3. Summary of information related to QTL governing agronomic and quality traits in common wheat (continued)
Trait Authors Chromosomal Location of QTLs Population Marker Thousand-Kernel- McCartney et al. (2005) 2AS, 3DL, 4AL, 4BL, 4DS, 6DL D-H SSR EST (SNP) weight Huang et al. (2006) 2BS, 2DS, 3BL, 4BL, 4DS, 6AL D-H SSR
Kumar et al. (2006) 1AS, 2BS, 7AS RILs SSR, AFLP SSR, EST-SSR, Li et al. (2007) 1DS, 3BL, 5DL, 6AL, 7DL RILs ISSR, SRAP, TRAP Cuthbert et al. (2008) 2DL, 3BS, 5AS, 5AL, 7AS D-H SSR
Wang et al. (2009) 1AS, 1BS, 2AS, 2DL, 3BS, 4AS, 4DS, 5AS, 6DL, 7DS RIL SSR SSR, EST-SSR, Sun et al. (2009b) 1DS, 2AL, 5DS, 6AL RILs ISSR, SRAP, TRAP Tsilo et al. (2010) 2AS,5B, 6B, 7A RILs SSR, DArT
32 Heidari et al (2011) 2B, 2D, 4B D-H AFLP, RAPD, SSR
Simons et al. (2012) 1BS, 5BL, 6AS RILs SSR
Bennett et al (2012) 1D, 2BS, 3D, 4A, 5B, 6A, 6B, 7AL, 7D D-H DArT
Grain protein Galande et al.(2001) 2B, 6B RILs ISSR content Groos et al. (2003) 1A, 2AS, 3AL, 3BS, 4AS, 4DL, 5BL, 6AL, 7AS, 7DL RILs RFLP, SSR
Prasad et al. (2003) 2AS, 2BL, 2DL, 3DS, 4AL, 6BS, 7AS, 7DS RILs SSR (Continues)
Table 1-3. Summary of information related to QTL governing agronomic and quality traits in common wheat (continued)
Trait Authors Chromosomal Location of QTLs Population Marker Grain protein Sourdille et al. (2003) 1BL, 6AS D-H RFLP, AFLP content 1AS, 1BL, 1DL, 2AS, 2AL, 2BL, 2DS, 2DL, 3BS, 4AS, Kulwal et al. (2005) RILs RFLP, SSR 5BL, 5DL, 6DL, 7AL, 7DS Huang et al. (2006) 4DS, 7BL D-H SSR
Kunert et al. (2007) 3AL, 4AL, 4BL, 5DL, 7BS, 7DS BC2F3 SSR
Mann et al (2009) 1B, 3A D-H SSR
Nelson et al (2006) 2A, 2D, RILs RFLP
Raman et al. (2009) 4A D-H DArTs
Sun et al. (2010) 3AS, 4B RILs SSR
33 Tsilo et al. (2010) 2BS, 5A, 6D RILs SSR, DArT
EST, ISSR, RFLP, Zhao et al. (2010) 3A, 3B, 5D, 6DS D-H SSR SSR, SNP, RFLP, Conti et al (2011) 1BS, 2AL, 2BS, 3BS, 3BL, 4AL, 5AS, 5BL, 7AS, 7BL RILs STS 1A, 1B, 2A, 2B, 2D, 3A, 4A, 4B, 4D, 5A, 5B, 5D, 6B, Li et al. (2012a) RILs G-SSR, EST-SSR 7A, 7B, 7D
Li et al (2012b) 1AS, 2DL, 4BL, 5DL, 6AS, 6BL, 6D, 7B BC5, IL SSR
Carter et al. (2012) 3BL RILs SSR, SNP (Continues)
Table 1-3. Summary of information related to QTL governing agronomic and quality traits in common wheat (continued)
Trait Authors Chromosomal Location of QTLs Population Marker Maphosa et al. (2013) 2B, 2D, 3A, 4A, 6A, 7A D-H DArT, SSR Kernel Volume Campbell et al. (1999) 2B, 4A, 5A, 6A, 7A RILs RFLP Weight McCartney et al. (2005) 1BS, 1DL, 2BL, 2DL, 3B, 3DL, 4DS, 5DL, 6BS, 7D D-H SSR EST(SNP) Narasimhamoorthy et al. 2DS BC F2: SSR (2006) 2 4 Huang et al. (2006) 2DS, 4AL, 4DS, 5AL, 7AS D-H SSR
Kunert et al. (2007) 3B, 4AL, 6BL, 7AS, 7BS BC2F3 SSR SSR, EST-SSR, Sun et al. (2009) 2AL, 3BL, 4AL, 5DS, 6AL, 6BS, 7BL RILs ISSR, SRAP, TRAP Sun et al. (2010) 1DL, 2DL, 4AS, 5AS, 5AL, 5BS, 6AS RILs SSR
34
Kernel Volume Bennett et al. (2012) 1B, 1D, 2A, 3A, 4A, 4D, 6A D-H DArT Weight Simons et al. (2012) 1BS, 4BL, 5BL RILs SSR
Carter et al. (2012) 5B RILs SSR, SNP
Flour Extraction Campbell et al. (2001) 3S, 5AS, 5BS, 5DS RILs RFLP
Kuchel et al. (2006) 1A, 2A, 6A D-H SSR, STS, Proteins
Nelson et al. (2006) 4A RILs RFLP (Continues)
Table 1-3. Summary of information related to QTL governing agronomic and quality traits in common wheat (continued)
Trait Authors Chromosomal Location of QTLs Population Marker Flour Extraction Raman et al. (2009) 7A D-H DArTs
Carter et al. (2012) 3BL, 4DS, 6DL RILs SSR, SNP
Simons et al. (2012) 1BL, 4BL RILs SSR
Maphosa et al. (2013) 2D, 3A, 3D, 4A D-H DArT, SSR
Mixogram Pattern Tsilo et al. (2011) 1B, 1D, 3B, 6D RILs SSR, DArT
Mixogram Midline Campbell et al. (2001) 1DL, 4AL, 7AS, 7DS RILs RFLP peak time, min Tsilo et al. (2011) 1B1, 1D, 2A, 6D, 7D RIL SSR, DArT
35 Li et al. (2012b) 2DL, 4A BC5, IL SSR
Mixogram Midline Simons et al. (2012) 1DL RILs SSR peak time, min Mergoum et al. (2013) 2B, 7BS RILs DART, SSR
Mixogram Midline Tsilo et al. (2011) 1A, 1B, 1D, 6D RILs SSR, DArT peak value, %
Mixogram Midline peak value, % Li et al. (2012b) 1AL, 1BS, 1DS, 2B, 2DL, 3AL, 4BL, 5AS, 5B, 6AL, 7B BC5, IL SSR (Continues)
Table 1-3. Summary of information related to QTL governing agronomic and quality traits in common wheat (continued)
Trait Authors Chromosomal Location of QTLs Population Marker Mixogram Midline peak value, % Simons et al. (2012) 1BS, 1DL, 5BL RILs SSR
Mixogram line peak width, % Tsilo et al. (2011) 1A, 1B, 6D RILs SSR, DArT
Li et al. (2012b) 1AS, 1BS, 1DS, 2B, 2DL, 3AL, 4BL, 5AS BC5, IL SSR
Simons et al. (2012) 1BS, 1DL RILs SSR
Mixogram line peak integral Tsilo et al. (2011) 1B, 1D, 6D, 7D RILs SSR, DArT
Simons et al. (2012) 1DL RILs SSR
36
Mixogram Mixing Tolerance Campbell et al. 2001 1AL, 1BL RILs RFLP
Li et al. (2012b) 1BS, 2DL, 4A, 5AS, 6AL BC5, IL SSR
Mixogram Weakening slope Li et al. (2012b) 4A, 6AL BC5, IL SSR
Mixogram Mixing development time Huang et al. (2006) 1B, 1DL, 3B D-H SSR
McCartney et al. (2006) 1B, 4D D-H SSR (Continues)
Table 1-3. Summary of information related to QTL governing agronomic and quality traits in common wheat (continued)
Trait Authors Chromosomal Location of QTLs Population Marker Mixogram Peak Height Huang et al. (2006) 1B, 1DL, 4DS D-H SSR
Mixogram Peak Height McCartey et al. (2006) 4D D-H SSR
Mixogram Energy to peak Huang et al. (2006) 1B, 1DL, 3B D-H SSR
McCartney et al. (2006) 1B, 4D D-H SSR Mixogram First minute slope Huang et al. (2006) 1DL, 4DS D-H SSR
McCartey et al. (2006) 1B, 4D, 7B, 7D D-H SSR Mixogram Peak
37 bandwidth Huang et al. (2006) 1DL D-H SSR
McCartey et al. (2006) 2B, 4D, 7D D-H SSR Mixogram Slope after peak Huang et al. (2006) 1DL, 4DS D-H SSR
McCartey et al. (2006) 1B, 4D D-H SSR Mixogram Total energy Huang et al. (2006) 1B, 5DS D-H SSR
McCartey et al. (2006) 1B, 2B, 4D, 7D D-H SSR Mixogram Bandwidth energy Huang et al. (2006) 1B, 5DS D-H SSR (Continues)
Table 1-3. Summary of information related to QTL governing agronomic and quality traits in common wheat (continued)
Trait Authors Chromosomal Location of QTLs Population Marker Mixogram McCartey et al. (2006) 1B, 2A, 2B, 6A, 7D D-H SSR Bandwidth energy
Mixogram Midline Li et al. (2012b) 1BS, 1DS, 2DL, 3AL, 4A, 4BL, 5AS, 5DL, 6AL BC , IL SSR time x=8 width 5
Midline time x=8 Li et al. (2012b) 1AS, 1BS, 1DS, 2B, 3AL, 4A, 4BL, 5AS, 5B, 6AL BC , IL SSR min value 5
Mixogram Mid line right of peak Li et al. (2012b) 1AL, 1BS, 2B, 2DL, 3AL, 4A, 4BL, 5AS, 6AL BC5, IL SSR width
38 Mixogram Midline
right of peak Li et al. (2012b) 1AL, 1BS, 1DS, 2B, 2DL, 4BL, 5B, 6AL BC5, IL SSR value
Mixogram Midline Li et al. (2012b) 1AL, 1BS, 1DS, 2B, 3AL, 4BL, 5AS, 6AL BC , IL SSR left of peak width 5
Mixogram Midline Li et al. (2012b) 1AL, 1BS, 1DS, 2B, 2DL, 4BL, 5AS, 6AL BC , IL SSR left of peak value 5 Mixing Time Mann et al. (2009) 1A, 1B, 1D D-H SSR (Continues)
Table 1-3. Summary of information related to QTL governing agronomic and quality traits in common wheat (continued)
Trait Authors Chromosomal Location of QTLs Population Marker Maximum band Mann et al. (2009) 1A, 1B, 4D, 5D, 7B D-H SSR width
Resistance break Mann et al. (2009) 1D, 7B D-H SSR down
Dough stability Maphosa et al. (2013) 1B, 3B D-H DArT, SSR
Dough Maphosa et al. (2013) 1B, 3B D-H DArT, SSR development time
39
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CHAPTER 2. GENOME-WIDE GENETIC DISSECTION OF SUPERNUMERARY SPIKELET AND
RELATED TRAITS IN COMMON WHEAT (TRITICUM AESTIVUM L.)
2.1. Abstract
Branched spike or supernumerary spikelet (SS) is a naturally occurring variant in wheat and holds great potential for increasing the number of grains per spike, and ultimately, increasing wheat yield.
However, detailed knowledge of the molecular basis of spike branching in common wheat is lacking. In the present study, a recombinant inbred line (RIL) population derived from the cross of an accession with
SS and an elite line with non-SS was developed and evaluated over four to six environments for seven
SS-related traits to identify the genetic basis of SS in wheat (Triticum aestivum L.). A framework linkage map was generated using 939 DArT markers. Composite interval mapping (CIM) identified a total of seven consistent QTL located on five chromosomes (2D, 5B, 6A, 6B and 7B), suggesting a polygenic inheritance of SS. The phenotypic variation explained (PVE) by individual QTL ranged from 3.3-37.3%.
The QTL located on 2D (QSS.ndsu-2D) and 7B (QSS.ndsu-7B.2 have major effects (PVE>15%), while the remaining five QTL (QSS.ndsu-5B, QSS.ndsu-6A, QSS.ndsu-6B.1, QSS.ndsu-6B.2, QSS.ndsu-7B.1) have minor effects (PVE <15%). Comparison of the genomic locations of the QTL suggested that
QSS.ndsu-2D was located in the same regions on 2DS where QTL for several traits have been reported.
However, the remaining six QTL for SS are reported for the first time. Multiple interval mapping showed that all these six QTL are involved in epistatic interaction. The major genomic region controlling the SS related-traits may prove invaluable for wheat improvement and could also be the target for future studies aimed at cloning this gene.
2.2. Introduction
World food production is challenged due to loss of arable land, climate changes, volatile prices, biofuel demand, high meat demand, and poor commodity distribution (Godfray et al., 2010). An increase in food grain production is needed to feed the ever-increasing population, which is expected to reach 9 billion by 2050. Wheat (Triticum aestivum L.), one of the most important world food crops, will play a major role in ensuring world food security, (Rajaram, 2002). However, in the last few decades, only limited
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breeding efforts were focused on enhancing the yield potential of this important crop (Reynolds et al.,
2011).
Grain yield is a complex trait determined by multiple components such as spikes per unit area, grain weight, and grains per unit area (Hucl and Fowler, 1992; Ma et al., 2007). In turn, these components are impacted by the inflorescence architecture, spike meristem growth, and spike fertility (Sreenivasulu and Schnurbusch, 2012). Considering that conventional wheat cultivars bear one spikelet per rachis node and produce between 20 and 50 kernels per spike (Bonnett, 1966; Perkins, 1997), the implementation of exotic germplasm bearing more than one spikelet per rachis node has been suggested as a strategy to increase grain yield (Saluke and Asana, 1970; Rawson and Ruwali, 1972; Pennell and Halloran, 1984a,
1984b; Hucl and Fowler, 1992; Peng et al., 1998; Yang et al., 2005; Sun et al., 2009; Aliyeva and Aminov,
2011; Li et al., 2011; Sreenivasulu and Schnurbusch, 2012). These spike characteristics are collectively termed “branched spikes”, “Blé d‟Osisris”, “Egyptian wheat”, “miracle spikes”, “mummy wheat”, “spike branching”, “wonder wheat”, or “Wunderweizen” (Percival, 1921; Sharman, 1944, 1967; Martinek and
Bednár, 1998; Sreenivasulu and Schnurbusch, 2012). Branched spikes were originally identified in tetraploid wheat (Triticum turgidum L. var durum Desf.) (Percival, 1921; Sharman, 1944, 1967) and later in hexaploid wheat (Koric, 1973). Branched spikes have extra sessile spikelets at a rachis node, or additional spikelets extended on rachillas (Pennell and Halloran, 1983, 1984a, 1984b), and potentially could produce 150 kernels per spike (Percival, 1921). Pennell and Halloran (1983) later suggested the term supernumerary spikelets (SS) to include both types of spikelet organization.
Studies aimed at understanding the genetics of SS phenotype started almost a century ago
(Percival, 1921). Earliest studies in tetraploid wheat observed that SS is controlled by a single recessive gene (Percival, 1921; Sharman, 1942, 1967). In hexaploid wheat, however, three genes designated as
Ramifera (Rm), Tetrastichon (Ts) and Normalizator (Nr) were reported to control SS (Koric, 1973). The
Rm and Ts genes work in a complementary fashion in the formation of the branched spike, while Nr is a dominant repressor of the branched phenotype. Therefore, the branched phenotype is possible when the inhibitor is silenced or absent. Other studies in hexaploid wheat also suggested that the branched spike phenotype was controlled by three genes (two genes working in a recessive or complementary fashion and a repressor) (Pennell and Halloran, 1983; Denčić, 1988). Pennell and Halloran (1983) also reported
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the presence of at least one gene for SS in the conventional spike (or non-SS) variety “Phoenix”. This observation suggests that some of the high-yielding wheat cultivars in the world might have branched traits suppressed by one gene. However, a study in tetraploid wheat suggested that SS trait is inherited quantitatively with one major recessive gene and numerous minor genes (Klindworth et al., 1990a).
Another recent study using line “51885” (a SS genotype derived from the cross of octoploid Triticale and common wheat) also found that the inheritance of SS trait is controlled by two dominant genes and probably a few minor genes (Sun et al., 2009). In contrast, another study in a vavilovii-branched line called “166-Schakheli” suggested that the branched spike trait is controlled by a single recessive gene
(Aliyeva and Aminov, 2011).
Initial efforts to identify the chromosomal location of genes controlling SS trait were based on cytogenetic analyses (Millet, 1986, 1987; Klindworth et al., 1990b; Peng et al., 1998). In common wheat,
Millet (1986, 1987) crossed the SS line “Noa” with the non-SS line “Mara” as well as with monosomic lines derived from “Mara.” The results showed that chromosome 2D of “Noa” carries a major recessive gene for the number of spikelets per spike, while chromosomes 5D and 7A have genes with minor effects for the spikelet number. Using a set of “Langdon” D-genome disomic substitution lines, Klindworth et al. (1990b) identified a major gene on chromosome 2A and one minor gene on chromosome 2B for SS in tetraploid wheat. Additional experiments with a “Langdon” 2A telosomic line located the major gene to the short arm of chromosome 2A. Interestingly, they observed that 2D monosomic addition lines had an inhibitory effect on the expression of SS (Klindworth et al., 1990b). Considering that D chromosomes come from “Chinese
Spring” wheat, it was concluded that a strong inhibitor for SS is located on chromosome 2D in hexaploid wheat (Klindworth et al., 1990b). Later, Peng et al. (1998) reported the presence of major genes for SS on chromosomes 2D, 4A, 5A and a minor gene on 4B in the hexaploid wheat line “Yupi Branching” when it was crossed with monosomic lines derived from “Chinese Spring” wheat.
Although several progeny analysis and cytogenetic studies defined the inheritance of SS in wheat, only few studies reported genetic dissection of SS using molecular markers. Using multi row spikes (MRS) wheat (a type of branched spike phenotype), Dobrovolskaya et al. (2009) evaluated two F2 populations and identified a major gene (Mrs1) on chromosome 2D linked to microsatellite locus
Xwmc453. Subsequent analysis with chromosome deletion bin lines delimited the physical location of the
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gene Mrs1 to the distal half of chromosome 2DS (Dobrovolskaya et al., 2009). Another study using the
Tibetan triple spikelet trait in wheat identified one QTL on the short arm of chromosome 2A associated with SSR markers, Xgwm275 and Xgwm122 (Li et al., 2011). The presence of a gene for SS trait on 2AS was further confirmed by another study which used three F2 mapping populations of durum wheat to map a major recessive locus for the branched head (bh) phenotype on the short arm of chromosome arm 2A
(Haque et al., 2012). This locus was associated with the SSR marker Xgwm425.
These molecular analyses of the SS phenotype focused on either a single chromosome or a single homeologous group. Yet, previous research also suggested that SS might be under polygenic control (Huang and Yen 1988; Klindworth et al., 1990a; Peng et al., 1998). Therefore, it becomes very important to dissect this trait at the whole genome level. The present study was aimed at identification of
QTL influencing SS in common wheat at the whole genome level using a recombinant inbred line (RIL) population derived from an exotic line WCB617 and an elite line WCB414. The genotype WCB617 has spikes with SS phenotype which are similar to the spikes of Triticum aestivum ramifera (Koric, 1973) and
“Miracle spikes” (Percival, 1921; Sharman, 1944, 1967); while, WCB414 is an elite white wheat (WW) line with non-SS phenotype. In the present study, the SS trait is segmented into seven component traits (or
SS-related traits) in order to improve QTL detection power and to understand the role of the component traits in determining SS phenotype. To the best of our knowledge, this is the first detailed report on whole genome dissection of SS phenotype using molecular markers.
2.3. Material and Methods
2.3.1. Plant material
The present study was conducted using a population of 163 RILs derived from a cross between the hexaploid hard white wheat line WCB414 and the hexaploid hard red wheat line WCB617 (used as pollen donator). WCB414 was developed by the Hard White and Specialty Wheat breeding program at
North Dakota State University (NDSU), Fargo, ND USA, and has conventional spikes with fusiform architecture, awned and glabrous glumes. WCB617 was identified and maintained by the NDSU wheat
Germplasm Enhancement project as a source for the SS phenotype, and has awns, pubescence on the
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glumes, and heterobranching behavior (variation in the penetrance of SS, in which a plant shows both branched and conventional spikes).
The RIL population was advanced to F7 generation through single seed descent method. Later, the seeds of this population were increased and advanced to F8 generation in greenhouse facilities. To ensure genetic purity, one spike derived from a main tiller of each RIL was collected in each season of field evaluations at Carrington (2009 and 2010) and sent to New Zealand for planting as spike-rows. The seeds harvested from each RIL in New Zealand were then planted in the next growing season in North
Dakota, and phenotypic data was collected. The F7:9, F10:11, and F12:13 populations were assessed during the years 2009, 2010, and 2011.
Six hard red spring wheat (HRSW) and one white wheat (WW) cultivars were used as checks in this study. The HRSW cultivars were “Alsen” (PI 615543) (Frohberg et al., 2006), “Steel-ND” (PI 634981)
(Mergoum et al., 2005), “Glenn” (PI 639273) (Mergoum et al., 2006), “Faller” (PI 648350) (Mergoum et al.,
2008), “Barlow” (PI 658018) (Mergoum et al., 2011), and “Briggs” (PI 632970) (Devkota et al., 2007), while the WW cultivar was “Alpine” (Agripro® wheat variety, USA).
2.3.2. Field experiment
Field trials were conducted at two different locations (Prosper and Carrington) in North Dakota,
USA over a period of three years (2009, 2010 and 2011). In total, the phenotypic data was recorded in six environments designated as I-VI (I=Prosper 2009, II= Carrington 2009, III= Prosper 2010, IV= Carrington
2010, V= Prosper 2011, and VI= Carrington 2011). Prosper is located in eastern North Dakota
(46.96300N, 97.01980 W) at an altitude 274 m and its soil are of the Bearden series. Carrington is located in central North Dakota (47.45000N, 99.12390 W) at an altitude of 484 m and its soils are of the Heimdal-
Emrick series. The average air temperatures during the growing seasons of 2009, 2010, and 2011 were
16.30C, 18.70C, 18.70C respectively in Prosper and 15.70C, 16.50C, and 17.30C, respectively in
Carrington. While, the total rainfall during the growing seasons of 2009, 2010, and 2011 was 39.6 mm,
87.6 mm, and 106.3 mm, respectively in Prosper and 41 mm, 52.3 mm, and 113.1 mm, respectively in
Carrington (NDAWN, 2012).
In each environment, the RILs, parents, and seven checks were planted in a 13 × 13 partially balanced square lattice design with two replicates. Each genotype was planted in plots comprised of
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seven rows of 2.44 m length; with a row to row distance of 12.7 cm. Sowing rate for every genotype was
113 kg ha-1.
2.3.3. Phenotypic data
In the field, a scale of 0 to 4 was used to determine the ratios of spikes with SS and conventional type (non-SS) in each experimental unit. This scale provides a measurement at the level of penetrance of
SS (PSS) or heterobranching behavior. When all the spikes in the experimental unit had SS phenotype, a score of 4 was assigned. A ratio of 3:1 (SS: conventional spike type) corresponded to a score of 3, a ratio
1:1 was scored as 2, and a ratio 1:3 (SS: conventional spike type) was scored as 1. If all the spikes for any genotype were conventional type, a score of 0 was assigned.
Four spikes were taken randomly from the primary tillers from each experimental unit to measure other SS components (SS-related traits). The spikes of the prevalent phenotype were collected for the genotypes with PSS scores of 3 and 1. When two phenotypes (branched and non-branched) were present in equal proportions (ratio 1:1, score 2) in any particular replicate of any environment, the prevalent phenotype for that particular genotype was decided based on the phenotype of same genotype in other replicates and environments. The other SS-related traits for which data was recorded include i) number of spikelets per spike (Sk), in which immature spikelets at the spike base were excluded; ii) spike density (SD) (spikelets cm-1) measured as the ratio between the number of spikelets and the spike length in centimeters, measured from the base of the rachis to the top of the uppermost spikelet, excluding awns; iii) number of spikelets per node (SkNd), in which conventional spikes were always given a value of
1, while in branched spikes the information for each spike was collected from the mean of five nodes randomly chosen; iv) number of nodes per spike with supernumerary spikelets (NdSS), in which conventional spikes were always given a value of 0; v) number of nodes with extended rachillas (NdR), in which conventional spikes were always given a value of 0; and vi) number of nodes per spike with non-
SS (NdNonSS). In conventional spikes, NdNonSS was equivalent to the number of spikelets per spike excluding immature spikelets at the spike base, while in spikes with SS, NdNonSS was equivalent to the number of nodes bearing one spikelet excluding immature spikelets at the spike base. For each trait, the mean from the four spikes was calculated. In 2011, PSS assessment was performed in both locations, but it was not possible to collect other spike measurements because Carrington location was severely
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affected by hail, and the Prosper location was affected by flood. Consequently, for all traits except PSS, data was recorded in four environments designated as I (Prosper 2009), II (Carrington 2009), III (Prosper
2010) and IV (Carrington 2010).
2.3.4. Statistical analysis
Data from all traits were subjected to analyses of variance (ANOVA) for a lattice design using the
MIXED procedure of the Statistical Analysis System (SAS, 2004). The RILs, parental genotypes, and checks were considered fixed effects, whereas environments and blocks were considered as random effects. ANOVA was estimated for each environment separately and combined over locations to estimate genotype × environment interaction. A Fmax ratio less than 10-fold were tested to combine analysis of variance (Tabachnik and Fidell, 2001). The mean separation tests were conducted using an F-protected least significant difference (LSD) value. F-tests were considered significant at p<0.05.
The MIXED procedure of SAS/STAT was used to calculate broad-sense heritability for each trait on plot basis (Holland et al. 2003) excluding the means of parents and checks. All the sources of variation
were used as random component. = /[ + ( )+ ( )], where is the genotype variance, is the G
× E interaction variance and is the error variance. All the variances were directly obtained from the output of covariance parameter estimate obtained from the MIXED procedure.
2.3.5. Molecular marker analysis, map construction, QTL identification, and epitasis analysis
Lyophilized young leaves were used to isolate genomic DNA for each genotype at F12:13 using
DNeay® Plant Mini Kit (Quiagen, cat. no. 69106). From the 163 RILs considered in this study, DNA was isolated from 159 RILs, which were subsequently used for map construction and QTL mapping. DNA quality was assessed through visual observation on 0.8% agarose gel. DNA concentrations were measured with a NanoDrop 1000 spectrophotometer (NanoDrop Technologies Inc., Wilmington, DE,
USA), and dilutions from each genotype were prepared as per Triticarte Pty. Ltd (Camberra, Australia; http://www.triticarte.com.au) guidelines. The DNA belonging to 159 RILs and two parental genotypes was sent to Triticarte Pty. Ltd (Camberra, Australia; http://www.triticarte.com.au) to conduct Diversity Array
Technology (DArT) analysis (Akbari et al. 2006). An expanded version of the WHEAT 2.6 DArT array with
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increased genomic representation of the D-genome was used (Wenzl et al., 2010). Markers showing polymorphism between parental genotypes were used for construction of a genetic map.
The genetic map was constructed using a combination of MapMaker 3.0 (Lander et al., 1987) and
CarthaGène V.1.2.3R (de Givry et al., 2005) software with a minimum LOD score of 3.0 and maximum recombination frequency of 50% for each program. Public genetic maps available at GrainGenes 2.0
(http://wheat.pw.usda.gov/GG2/index.shtml) were used in the selection of DArT anchor markers.
MapMaker 3.0 was used to assign markers to linkage groups generated with anchor markers, while
CarthaGène V.1.2.3R was used to order the markers in each linkage group separately. The robustness of the maps of each linkage group was tested with the commands Flips and Annealing of CarthaGène
V.1.2.3R. Final genetic distances were obtained using the Kosambi‟s mapping function (Kosambi, 1944).
The final maps were compared with the DArT consensus maps (Huang et al., 2012) using the program
Autograph (Derrien et al., 2007; http://autograph.genouest.org/) to check the accuracy of the marker order.
Composite interval mapping (CIM) was conducted to identify main-effect QTL for each trait in each environment as well across environments (AE) using QTL Cartographer V2.5_011 (Wang et al.,
2012). In QTL Cartographer, model 6 (standard model), forward regression, five control markers
(cofactors), windows size of 10 cM, and walk speed of 1 cM was used. A minimum LOD score of 2.5 was used to declare a putative QTL. Permutation test with 500 permutations was used to determine critical
LOD threshold (experimental wide significance of 0.05). QTL explaining at least 15% of phenotypic variation (PV) in one environment were declared as major QTL. Confidence intervals (CI) were calculated using ±2 LOD (from the peak) method. QTL with overlapping CIs were considered as one QTL. When the same QTL was detected for more than one trait, the region overlapping across traits (ROAT) was determined. To calculate a ROAT, the genetic regions covered by the common QTL in each trait were aligned to determine shared (overlapping) regions. A ROAT was determined by the confidence intervals of the shared regions among traits. The QTL that were detected in at least three environments were called consistent QTL and were the only QTL considered in this study. However, when a QTL was detected for more than one trait, the QTL was reported in the present study even if it was consistent
(present in minimum three environments) for at least one trait. Multiple interval mapping (MIM), using
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default setting in Cartographer V2.5_011 (Wang et al., 2012), was also conducted to identify epistatic interaction between the QTL detected by CIM. Only those epistatic interactions which have r2>5% were reported in for this study. Graphical genotypes were prepared using the software GGT (van Berloo 2008).
The program MapChart 2.2 (Voorrips, 2002) was used to draw the linkage groups and QTL.
2.4. Results
2.4.1. Phenotypic analyses
The phenotypic data clearly showed segregation among the RILs for the presence/absence of spikes with SS. However, the prevalent spike type in the RIL population was conventional (non-SS). In branched RILs, SS were either observed as sessile spikelets at a rachis node or additional spikelets on extended rachillas or a combination of both (Fig. 2-1). The WCB617parent and the RILs with prevalence of the branched phenotype showed both classes of SS. The exception was the branched RIL 1021, which had only sessile SS (data not shown) in all the environments.
The heterobranching behavior (occurrence of branched spikes and conventional spikes on a plant), measured as PSS, was observed in most of the RIL with presence of spike bearing SS. Combined
ANOVA analysis showed that a total of 20 RILs had an estimate mean for PSS equal or greater than 1
(data not shown). For four RILs, the PSS score ranged from 1 to 2, demonstrating the predominance of conventional spikes in these genotypes. While, for 16 RILs the PSS score ranged from 2.5 to 4, showing the prevalence of the branched phenotype in these genotypes. RIL 1070 was the only line with an average score of 4, indicating the absence of heterobranching behavior in all the environments. A total of
31 RILs had a PSS score between 0.01 and 0.99 indicating the presence of at least one replicate with some branched spikes. While a total of 112 RIL did not showed spikes with SS in any of the environments.
The estimated means and ranges of all traits for the RILs, parents, and checks in all environments are presented in Appendix Table A1. In all environments as well as in the combined analysis of the environments, the traits Sk, SD, and NdNoSS showed transgressive segregation in both directions of the parental means. While, in all the environments as well as in the combined analysis of the environments, PSS, SkNd, and NdR had transgressive segregation in the direction to the parent with SS
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(WCB617). In all environments, NdSS had transgressive segregation in direction to the parent with SS, but did not show transgressive segregation in the combined analysis of the environments.
Fig. 2-1. Segregation in spike morphology in a spring wheat RIL population derived from the cross of an elite line (WCB414) with non-supernumerary spikelets (SS) and a PI with SS (WCB617). A. Conventional spike with non-SS, B. spike with sessile SS, C. spike with high expression of SS, D. spike with SS observed on an extended rachilla, E. two sessile spikelets attached to one node, F. spike with additional spikelets on extended rachilla (most of the spikelets were removed to get better detail), G. spike in whose node are observed sessile SS and SS extended on a rachilla, H. rachis and extended rachillas of a spike with high number of SS.
Coefficients of variation and lattice efficiency in each environment were calculated using the
ANOVA results (Appendix Table A2). The error variances among the environments were homogenous for
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all the traits except NdSS (Appendix Table A3). Combined ANOVA with six environments was conducted for PSS; with four environments for Sk, SD, SkNd, NdR, and NdNoSS; and with three environments for
NdSS (Table 1). Combined ANOVA analysis showed that all traits had significant genotype × environment interaction. For all traits, the sum of squares calculated from the combined ANOVA showed that genotypes (RILs, parents and checks) are the main source of variation (data not shown). For all traits, broad sense heritability ranged from 0.69 to 0.86 (Table 2-1). Correlations between the SS-related traits are presented in Appendix Table A4.
Table 2-1. Mean squares, coefficient of variation and heritabilities for supernumerary-spikelets-related traits of parents, RILs and checks evaluated in a maximum of six environments Mean Squares
Trait† NECAV‡ Genotype Environment G x E Error CV(%)¶ H# SE†† PSS 6 11.30** 0.75 0.24** 0.07 59.58 0.86 0.01 Sk 4 439.02** 288.25 21.56** 7.78 14.00 0.79 0.02 SD 4 3.79** 0.73 0.19** 0.08 13.41 0.79 0.02 SkNd 4 2.23** 0.22 0.12** 0.05 18.69 0.77 0.02 NdSS§ 3 38.88** 4.23 0.98** 0.49 77.59 0.90 0.01 NdR 4 6.79** 1.83 0.53** 0.23 163.62 0.69 0.03 NdNoSS 4 60.05** 65.63 2.90** 1.38 7.09 0.78 0.02 * Significance at P < 0.05 probability level ** Significance at P < 0.01 probability level. †PSS level of penetrance of supernumerary spikelets (scale from 0 to 4); Sk, number of spikelets (spikelets spike-1); SD spike density (Sk spike-longitude-1); SkNd spikelets per node (spikelet node-1); NdSS number of nodes with supernumerary spikelets per spike; NdR number of nodes with extended rachillas (extended rachillas spike-1);NdNoSS number of nodes with no supernumerary spikelets per spike. ‡Number Environments in combined analysis of variance. §The four environments were not homogeneous in NdSS. Combined analysis of variance for NdSS was performed with the three more homogenous environments (Carr09, Carr10, and Pros10). ¶Coefficient of variation #Broad sense heritability on plot-mean basis calculated in the RILs. ††Standard error for heritability.
Comparison of top-ranked genotypes in each environment showed that few lines with SS were consistent in the level of expression of Sk, SD, SkNd, NdSS, and NdR (data not shown). For SK, RILs
1070, 1097, and 1099 were always ranked in the 10 genotypes with highest SK in each of the four environments tested. For SD, RILs 1017, 1053, 1068, 1070, 1097 and 1099 were consistently ranked in the top 10 genotypes in each of the four environments. For SkNd, RILs 1053, 1070, 1099 and 1151 were always ranked among the top 10 genotypes in each environment. For NdSS, RILs 1053, 1070, 1097, and
63
1134 were consistently ranked in the top 10 genotypes in each of the four environments. Finally, for NdR,
RILs 1053, 1070, 1097, 1099, and 1151 were always ranked in the top 10 genotypes in each environment. The distribution of the RIL distribution across environments for the seven SS-related traits is showed in the Appendix Fig. A1.
2.4.2. Map construction
DArT analysis resulted in the identification of 1,004 markers polymorphic between parental genotypes. A total of 27 highly distorted markers were removed, and the remaining 977 markers were used for the construction of the framework genetic map. At a minimum LOD score of 3, a total 939 markers were assigned to 38 linkage groups. Thirty-eight markers could not be assigned to any linkage group. Comparison with a consensus map (Huang et al. 2012) showed that these 38 linkage groups represent 20 chromosomes of wheat. No linkage group was associated with chromosome 4D. Five chromosomes (1B, 3A, 3B, 7A, 7B) had three groups each; eight chromosomes (1A, 2B, 3D, 4B, 5A, 6A,
6B, 7D) showed 2 groups each, while seven chromosomes (1D, 2A, 2D, 4A, 5B, 5D, 6D) showed a single group. A total of 14 groups were assigned to the A-genome, 16 groups were assigned to the B-genome, while 8 groups were assigned to the D-genome.
The 939 markers mapped in the present study represented 671 unique loci; 268 markers co- segregated with other loci. The B-genome had the highest number of mapped loci (354 markers representing 285 loci), followed by A-genome (292 markers representing 222 loci) and D-genome (293 markers representing 164 loci). The number of markers on individual linkage groups ranged from three
(3A.1, 5A.1 and 5A.2) to 97 (2D), while for individual chromosomes, it ranged from four (chromosome 5D) to 97 (chromosome 2D).
The total genetic distance covered by these 939 markers (671 loci) was 3,114.2 cM, and the average distance between any two marker loci was 4.6 cM. The B genome chromosomes covered a total length of 1,530.9 cM with an average distance of 5.4 cM between two loci. The A genome had a total map length of 1145.9 cM with an average distance of 5.2 cM between two loci. The D genome covered a total map length of 437.4 cM with an average density of 2.7 cM between two loci. Individually, chromosome 5B markers have the maximum coverage (309.1 cM), while chromosome 5D markers had minimum coverage
64
(3.7 cM). Marker order was generally consistent with the DArT consensus map (Huang et al., 2012) with only few exceptions.
2.4.3. QTL analysis
Composite interval mapping was used to identify QTL associated with supernumerary spikelet and related traits. The trait-associated QTL, as well as their flaking markers, confidence intervals and phenotypic variation (r2) explained by each QTL are summarized in the Table 2. A total of 7 consistent
QTL located on five chromosomes (2D, 5B, 6A, 6B and 7B) were identified. Chromosomes 2D
(QSS.ndsu-2D), 5B (QSS.ndsu-5B) and 6A (QSS.ndsu-6A) had one QTL each, while chromosomes 6B
(QSS.ndsu-6B.1 and QSS.ndsu-6B.2) and 7B (QSS.ndsu-7B.1 and QSS.ndsu-7B.2) carried two QTL each (Fig. 2).
65
Table 2-2.. QTL identified for supernumerary-spikelet-related traits in WCB414 × WCB617 RIL population of hexaploid wheat QTL/Trait† Environment‡ Flanking Markers Pos. (cM) CI§(cM) LOD ¶Thresh. a# R2(%) QSS.ndsu-2D PSS I, II, III, IV, V, VI, AE wPt-8134-wPt-667266 33.4-50.6 27.2-51.3 8.4-14.9 10.8-63.1 -0.4_-0.6 13.9-24.4 Sk I, II, III, IV, AE wPt-3677-wPt-667266 39.7-50.6 32.9-51.1 10.2-15.3 3.5-30.8 -3.7_-4.9 19.0-25.2 SD I, II, III, IV, AE wPt-741029-wPt-667266 39.6-50.6 29.5-51.5 8.6-12.9 3.3-30.1 -0.3_-0.4 13.8-21.8 NdR I, II, III, IV, AE wPt-3677-wPt-667266 39.7-50.6 28.4-51.3 6.2-9.7 3.4-46.9 -0.4_-0.5 10.9-18.6 SkNd I, II, III, IV, AE wPt-5574-wPt-667266 41.5-50.6 27.6-51.5 5.8-10.8 3.1-43.3 -0.2_-0.3 11.5-18.3 NdSS I, II. III. IV, AE wPt-5574-wPt-667785 41.5 27.2-51.3 7.2-9.4 71.6-93.5 -1.1_-1.3 13.2-15.5 ROAT†† 32.9-51.1
QSS.ndsu-5B
PSS I, II, III, IV, VI, AE wPt-1348-wPt-744851 278.9-292.6 265.6-308.3 2.9-4.5 10.8-63.1 -0.2_-0.4 4.1-9.1 Sk III, AE wPt-3995-wPt-3569 291.1 270.1-307.1 3.4-4.4 3.5-30.8 -1.7_-2.7 4.3-6.7 SD III wPt-3995-wPt-3569 291.1 275-307.9 4.9 30.1 -0.3 7.9 NdR III wPt-3995-wPt-3569 290.1 278.6-307.5 5.3 33.63 -0.4 9.4 SkNd III, IV, AE wPt-3995-wPt-744851 289.1-303.6 270.1-307.9 2.9-5.4 25.6-43.3 -0.1_-0.2 4.8-8.8
66 NdNoSS II, III, IV, AE wPt-3569-wPt-744851 292.6-307.6 265.4-307.6 2.6-3.9 3.0-3.3 0.7-0.9 5.4-9.2
NdSS I, II, III, IV. AE wPt-3995-wPt-744851 291.1-292.6 267.8-308.0 3.1-4.3 71.6-93.5 -0.6_-0.8 4.8-6.7 ROAT†† 278.6-307.1
QSS.ndsu-6A
PSS I, II, III, IV, V, VI, AE wPt-671561-wPt-733976 43.6-46.7 32.4-52.9 5.1-7.4 10.8-63.1 -0.3_-0.4 7.3-9.6 Sk I, III, IV, AE wPt-671561-wPt-0562 43.6 32.3-53.8 3.2-5.9 3.5-30.8 -1.9_-3.0 5.1-8.6 SD I, III, IV, AE wPt-671561-wPt-732760 43.6-61.8 34.5-75.5 3.7-5.1 3.3-30.1 -0.2_-0.3 5.5-7.7 NdR I, III, IV, AE wPt-671561-wPt-732760 43.6-61.8 33.3-77.9 3.0-4.8 33.6-46.9 -0.3_-0.4 5.4-8.0 SkNd I, III, IV, AE wPt-671561-wPt-9679 43.6-53.8 32.3-72.3 4.1-6.1 25.6-43.3 -0.2_-0.2 6.9-9.6 NdNoSS I, III, IV,AE wPt-9687-666574 20.8-60.1 7.9-69.8 4.5-6.5 3.1-3.3 1.1-1.3 10.1-13.5 NdSS I, II, III, IV, AE wPt-671561-wPt-0562 43.6 33.3-53.9 3.1-5.5 71.6-93.5 -0.6_-0.9 5.4-8.5 ROAT†† 34.5-52.9
QSS.ndsu-6B.1
PSS II,III,V, AE wPt-6116-wPt-6878 5.5-21.8 0-26.4 2.7-3.3 30.6-63.1 -0.2_-0.3 3.3-5.6 NdNoSS IV wPt-9256-wPt-0406 12.2 3.3-26.1 3.1 3.1 0.7 6.0 NdSS III, IV, AE wPt-1264-wPt-3207 18.8-27.0 0.4-31.7 2.6-3.0 71.6-89.0 -0.5_-0.6 3.6-4.5 ROAT†† 3.3-26.1
(Continues)
Table 2-2. QTL identified for supernumerary-spikelet-related traits in WCB414 × WCB617 RIL population of hexaploid wheat (Continued) QTL/Trait† Environment‡ Flanking Markers Position (cM) CI§(cM) LOD ¶Thresh a# R2(%) QSS.ndsu-6B.2 PSS I, II, III, IV, V, VI, AE wPt-3284-wPt-3581 208.9-210.9 199.1-220.4 4.4-7.9 10.8-63.1 0.3-0.4 8.0-10.6 Sk I, III, IV,AE wPt-743099-wPt-3581 208.6-208.9 196.8-220.9 3.3-6.1 3.5-30.8 1.8-3.26 4.6-9.04 SD I, III, IV,AE wPt-743099-wPt-3581 207.6-208.9 195.8-220.1 2.9-4.8 3.3-30.1 0.2-0.27 5.0-8.1 NdR I, III, AE wPt-2564-wPt-3581 185.5-208.9 172.2-220.9 3.2-5.3 33.6-46.9 0.3-0.43 5.8-9.2 SkNd I, III, IV,AE wPt-743099-wPt-3581 207.6-208.9 198.1-219.3 4.5-6.6 25.6-43.3 0.2-0.24 7.9-11.1 NdNoSS III, IV, AE wPt-743099-wPt-3581 205.6-212.9 196.1-221.2 4.1-5.6 3.1-3.3 -1.0_-1.4 9.9-14.8 NdSS I, II, III, IV, AE wPt-743099-wPt-3284 206.6-208.6 195.6-221.2 3.1-6.2 71.6-93.5 0.7-1.07 6.5-11.4 ROAT†† 199.1-219.3
QSS.ndsu-7B.1
PSS I, II, III, IV, V, VI, AE wPt-0194-wPt-8615 97.7 84.6-111.5 2.8-4.6 10.8-63.1 0.2-0.3 3.9-5.8 SK I,III, IV, AE wPt-0194-wPt-8615 97.7 82.1-118.0 2.8-4.0 3.5-30.8 1.6-2.0 4.0-4.6 SD I,III, IV, AE wPt-0194-wPt-665428 97.7-115.0 93.4-123.7 2.6-5.5 3.3-30.1 0.2-0.2 4.8-8.1 NdR IV, AE wPt-0194-wPt-8615 97.7 84.9-120.9 2.7-3.2 42.1-46.9 0.2-0.2 4.4-4.8
67 SkNd I, III, IV,AE wPt-0194-wPt-8615 97.7 82.6-111.5 2.5-4.3 25.6-43.3 0.1-0.2 3.6-6.6
NdNoSS III, IV, AE wPt-0194-wPt-2407 97.7-101.3 81.2-115.4 2.6-2.9 3.1-3.3 -0.6_-0.8 4.6-5.9 NdSS I, II, III, IV, AE wPt-0194-wPt-8615 97.7 82.1-118.7 2.8-4.4 71.6-93.5 0.6-0.7 4.4-6.9 ROAT†† 93.4-111.5
QSS.ndsu-7B.2
PSS III, IV, VI wPt-3723-wPt-4258 72.6-73.6 59.8-78.9 2.9-4.7 30.6-41.3 -0.5_-1.3 10.4-25.3 SK AE wPt-3723-wPt-4258 74.6 64.4-79 3.2 30.8 -3.9 12.1 SD IV wPt-3723-wPt-4258 73.6 72.3-74.8 9.9 3.3 -1.1 33.5 SkNd I, IV, AE wPt-3723-wPt-4258 73.6-75.6 62.3-78.8 2.6-20.7 25.6-41.2 -0.3_-0.9 10.9-37.3 NdSS III, AE wPt-3723-wPt-4258 72.6 68.7-75.8 10.0-15.1 83.1-89.0 -4.0_-4.3 32.4-34.8 ROAT†† 72.3-74.8 †Traits were defined in table1 ‡PSS was studied in six environments; the other traits were studied in four environments §CI Confidence Interval ¶Thresold calculated by permutation test. #Additive Effect ††ROAT region overlapping across traits
5B 2D
0.0 wPt-2675 27.4 wPt-8134 36.2 wPt-2761 2D 38.6 wPt-741029 6A.2 wPt-3677 wPt-5683 0.0 wPt-2675 39.7 wPt-663767 wPt-732866 27.4 wPt-8134 wPt-734114 wPt-740855 36.2 wPt-2761 40.3 wPt-732705 38.6 wPt-741029 40.9 wPt-671410 wPt-3677 wPt-5683 41.5 wPt-5574 39.7 wPt-663767 wPt-732866 41.8 wPt-667785 wPt-734114 wPt-740855 wPt-3144 wPt-664967 40.3 wPt-732705 wPt-9802 wPt-730529 40.9 wPt-671410 wPt-730057 wPt-6609 wPt-7243 wPt-6096 41.5 wPt-5574 42.1 41.8 wPt-667785 wPt-671455 wPt-0936 wPt-3144 wPt-664967 wPt-4093 wPt-7426736A-1 2D2D 6A.2 5B wPt-9802 wPt-730529 wPt-21860.0wPt-6342wPt-730591 wPt-730057 wPt-6609 wPt-22941.5 wPt-2636 wPt-0864 0.0 wPt-9724 42.4 wPt-7149 wPt-664901 0.0 wPt-2675 wPt-7243 wPt-6096 3.7 wPt-4836 0.7 42.1wPt-8604 43.4 wPt-667843 27.4 wPt-8134 wPt-671455 wPt-0936 7.4 wPt-741630 11.0 wPt-7237 43.7 wPt-4144 wPt-666857 36.2 wPt-2761 wPt-4093 wPt-742673 20.8 wPt-9687 17.1 wPt-3085 44.1 wPt-740836 38.6 wPt-741029 wPt-2186 wPt-6342 22.3 wPt-1285 17.8 rPt-6127 wPt-3812 wPt-3677 wPt-5683 wPt-2294 45.5 24.5 wPt-669498 26.5 wPt-5737 47.3 wPt-667294 39.7 wPt-663767 wPt-732866 42.4 wPt-7149 wPt-664901 28.4 wPt-0228 34.8 tPt-8942 wPt-4736 wPt-666223 wPt-734114 wPt-740855 43.4 wPt-667843 48.8 32.2 wPt-1377 38.1 wPt-2041 49.5 wPt-741084 40.3 wPt-732705 43.7 wPt-4144 wPt-666857 37.8 wPt-9832 60.6 wPt-7418 50.1 wPt-666656 wPt-0085
40.9 wPt-671410 44.1 wPt-740836 43.6 wPt-671561
QSS.ndsu-2D QSS.ndsu-2D
QSS.ndsu-2D
QSS.ndsu-2D QSS.ndsu-2D 65.6 wPt-742141 wPt-5514 QSS.ndsu-2D 50.6 wPt-2652 wPt-6259 41.5 wPt-5574 45.5 wPt-3812 43.9 wPt-0562 wPt-741170 90.7 wPt-3049 51.1 wPt-667266 41.8 wPt-667785 47.3 wPt-667294 46.7 wPt-5964 wPt-6531 wPt-0650 wPt-3144 wPt-664967 106.2 48.8 wPt-666223 52.1 53.8 wPt-733976 106.7 wPt-8393 53.0 wPt-741302 wPt-9802 wPt-730529 0.0 wPt-730591 49.5 wPt-741084 wPt-9679 wPt-3524 109.0 wPt-3457 56.3 wPt-66767155.8 wPt-730057 wPt-6609 1.5 wPt-2636 wPt-086450.1 wPt-666656 wPt-0085 wPt-731934 109.7 wPt-1895 59.4 wPt-731489 wPt-7243 wPt-6096 3.7 wPt-4836 50.6 wPt-2652 wPt-6259 56.1 wPt-7027 42.1 112.3 wPt-1548 63.1 wPt-731406 wPt-671455 wPt-0936 7.4 wPt-741630 51.1 wPt-667266 56.6 tPt-2833 wPt-731842 135.8 wPt-6191 wPt-2707 65.0 wPt-664714 wPt-4093 wPt-742673 20.8 wPt-9687 52.1 wPt-0650 57.7 wPt-8719 158.7 wPt-6014 65.7 wPt-9900
wPt-2186 wPt-6342 22.3 wPt-1285 53.0 wPt-741302 58.1 rPt-9065
QSS.ndsu-2D QSS.ndsu-2D
QSS.ndsu-2D
QSS.ndsu-2D QSS.ndsu-2D 187.5 wPt-7006 QSS.ndsu-6A.2 66.1 wPt-733889 wPt-732277
wPt-2294 24.5 wPt-669498QSS.ndsu-2D 56.3 wPt-667671 wPt-666574 tPt-0877 QSS.ndsu-6A.2
QSS.ndsu-6A.2 61.8 207.2 wPt-1060 QSS.ndsu-6A.2 66.4 wPt-671859 42.4 wPt-7149 wPt-664901 28.4 wPt-0228 59.4 wPt-731489 wPt-6904 wPt-671855 wPt-4402 wPt-667406 wPt-664745 wPt-667843 wPt-1377220.7 63.1 wPt-731406 66.7 77.9 wPt-732760
43.4 32.2 QSS.ndsu-6A.2 221.7 wPt-0295 wPt-671737 wPt-671892
67.0QSS.ndsu-6A.2 43.7 wPt-4144 wPt-666857 37.8 wPt-9832 65.0 wPt-664714 QSS.ndsu-6A.2 78.2 wPt-3965 wPt-7623 229.1 wPt-7665 wPt-9116 wPt-668120 wPt-6704 44.1 wPt-740836 43.6 wPt-671561 65.7 wPt-9900 68.6 78.5 wPt-731010 236.1 wPt-0484 wPt-1554 45.5 wPt-3812 43.9 wPt-0562 wPt-74117066.1 wPt-733889 wPt-732277 79.7 wPt-667170 237.1 wPt-6465 68.9 wPt-6850 47.3 wPt-667294 46.7 wPt-5964 66.4 wPt-671859 82.1 wPt-4791 238.9 wPt-3922 70.5 wPt-731134 48.8 wPt-666223 53.8 wPt-733976 66.7 wPt-667406 wPt-664745 wPt-667780 wPt-2822 244.4 wPt-6971 72.1 wPt-667476 49.5 wPt-741084 wPt-9679 wPt-352467.0 wPt-671737 wPt-671892 82.4 wPt-664733 wPt-733764 55.8 251.8 wPt-2373 73.4 wPt-733932 50.1 wPt-666656 wPt-0085 wPt-731934 wPt-668120 wPt-6704 wPt-9584 263.3 68.6wPt-8449 74.0 wPt-733033 50.6 wPt-2652 wPt-6259 56.1 wPt-7027 wPt-1554 83.7 wPt-666208 2D 263.9 6B.1wPt-1348 74.5 wPt-732509 wPt-671669 51.1 wPt-667266 56.6 tPt-2833 wPt-73184268.9 wPt-6850 84.7 wPt-9131 wPt-8539 289.1 wPt-3995 74.8 wPt-730568 52.1 wPt-0650 57.7 wPt-8719 70.5 wPt-731134 85.0 wPt-671799 292.6 wPt-3569 75.1 wPt-671914 wPt-667124
53.0 wPt-741302 58.1 rPt-9065 72.1 wPt-667476 86.6 tPt-6278
QSS.ndsu-2D QSS.ndsu-2D
QSS.ndsu-2D wPt-2675 wPt-744851 wPt-5014
QSS.ndsu-2D 0.0 307.8
QSS.ndsu-6A2 76.8
QSS.ndsu-2D 73.4 wPt-733932 89.9 wPt-666988
QSS.ndsu-2D 56.3 wPt-667671 wPt-666574 tPt-0877
QSS.ndsu-5B
QSS.ndsu-5B
QSS.ndsu-5B
QSS.ndsu-5B
QSS.ndsu-5B
QSS.ndsu-6A2 QSS.ndsu-6A.2 27.4QSS.ndsu-6A2 wPt-8134 61.8 tPt-4875 wPt-0929 wPt-668017 QSS.ndsu-5B wPt-733033 79.3 tPt-7399 59.4 wPt-731489 QSS.ndsu-5B wPt-6904308.2wPt-67185574.0 94.8 36.2 wPt-2761 wPt-744750 82.8 wPt-671623 63.1 wPt-731406 77.9 wPt-732760 74.5 wPt-732509 wPt-671669 95.6 wPt-666074 38.6 QSS.ndsu-6A2 wPt-741029 309.1 wPt-1457 wPt-730568 87.7 wPt-667536 wPt-733115 QSS.ndsu-6A2 74.8 113.6 65.0 wPt-664714 QSS.ndsu-6A2 78.2 wPt-3965 wPt-7623 wPt-3677 wPt-5683 90.5 wPt-4242 65.7 wPt-9900 78.5 wPt-731010 75.1 wPt-671914 wPt-667124 114.5 wPt-731413 39.7 wPt-663767 wPt-732866 94.5 wPt-730613 66.1 wPt-733889 wPt-732277 79.7 wPt-667170 76.8 wPt-5014 114.9 wPt-730711 wPt-730109
68 wPt-734114 wPt-740855 98.3 wPt-732270 66.4 wPt-671859 82.1 wPt-4791 79.3 wPt-668017 115.4 wPt-3091 40.3 wPt-732705 105.4 wPt-730427 66.7 wPt-667406 wPt-664745 wPt-667780 wPt-282282.8 wPt-671623 115.9 wPt-0357
40.9 wPt-671410 wPt-667485 wPt-6064 67.0 wPt-671737 wPt-671892 82.4 wPt-664733 wPt-73376487.7 wPt-667536 117.7 wPt-0139 wPt-5574 107.8 wPt-741208 wPt-668120 wPt-6704 41.5 wPt-9584 90.5 wPt-4242 118.1 wPt-666224 68.6 41.8 wPt-667785 109.0 wPt-732923 wPt-1554 83.7 wPt-666208 94.5 wPt-730613 120.4 wPt-3308 wPt-3144 wPt-664967 wPt-665836 wPt-732052 68.9 wPt-6850 84.7 wPt-9131 wPt-853998.3 wPt-732270 122.4 wPt-7445 wPt-9802 wPt-730529 109.8 wPt-0153 wPt-667765 70.5 wPt-731134 85.0 wPt-671799 105.4 wPt-730427 124.8 wPt-729806 wPt-730057 wPt-6609 wPt-4329 wPt-7825 72.1 wPt-667476 86.6 tPt-6278 wPt-667485 wPt-6064 148.9 wPt-667618 wPt-7063 wPt-7243 wPt-6096 107.8 110.2 wPt-5106 73.4 wPt-733932 42.1 89.9 wPt-666988 wPt-741208 150.7 tPt-8557 wPt-671455 wPt-0936 111.2 wPt-733725 74.0 wPt-733033 94.8 tPt-7399 109.0 wPt-732923 153.7 wPt-729839 wPt-4093 wPt-742673 111.6 wPt-6514 74.5 wPt-732509 wPt-671669 95.6 wPt-666074 wPt-665836 wPt-732052 wPt-2186 wPt-6342 114.0 wPt-3757 74.8 wPt-730568 113.6 wPt-733115 109.8 wPt-0153 wPt-667765 wPt-2294 115.8 wPt-9848 75.1 wPt-671914 wPt-667124 114.5 wPt-7314136B-2 wPt-4329 wPt-7825 42.4 wPt-7149 wPt-664901 wPt-6116 125.7 wPt-3692 wPt-666518 76.8 wPt-5014 114.9 wPt-7307110.0 wPt-730109110.2 wPt-5106 43.4 wPt-667843 wPt-1541 131.9 wPt-6780 79.3 wPt-668017 115.4 wPt-30915.5 111.2 wPt-733725 43.7 wPt-4144 wPt-666857 wPt-0171 82.8 wPt-671623 115.9 wPt-03576.5 111.6 wPt-6514 44.1 wPt-740836 wPt-4164 87.7 wPt-667536 117.7 wPt-01398.7 114.0 wPt-3757 45.5 wPt-3812 wPt-732061 90.5 wPt-4242 118.1 wPt-6662249.5 115.8 wPt-9848 47.3 wPt-667294 wPt-9256 94.5 wPt-730613 120.4 wPt-330812.2 125.7 wPt-3692 wPt-666518 PSS SkNd 48.8 wPt-666223 wPt-0406 98.3 wPt-732270 122.4 wPt-744515.0 131.9 wPt-6780 49.5 wPt-741084 wPt-1264 wPt-3045 wPt-729806QSS.ndsu-6B.1 17.8 105.4 wPt-730427 124.8QSS.ndsu-6B.1 50.1 wPt-666656 wPt-0085 QSS.ndsu-6B.1 wPt-667485 wPt-6064 148.9 wPt-66761826.6 wPt-7063wPt-6878 107.8 50.6 wPt-2652 wPt-6259 Sk wPt-741208 150.7 tPt-855727.0 wPt-744795 wPt-9660 51.1 wPt-667266 wPt-3207 NdSS 109.0 wPt-732923 153.7 wPt-72983931.7 wPt-0650 wPt-665836 wPt-732052 52.1 53.0 wPt-741302
109.8 wPt-0153 wPt-667765 SD
QSS.ndsu-2D QSS.ndsu-2D QSS.ndsu-2D QSS.ndsu-2D wPt-667671 QSS.ndsu-2D 56.3 NdNoSS wPt-4329 wPt-7825 QSS.ndsu-2D 59.4 wPt-731489 110.2 wPt-5106 63.1 wPt-731406 111.2 wPt-733725 65.0 wPt-664714 111.6 wPt-6514 NdR 65.7 wPt-9900 114.0 wPt-3757 66.1 wPt-733889 wPt-732277 115.8 wPt-9848 66.4 wPt-671859 125.7 wPt-3692 wPt-666518 66.7 wPt-667406 wPt-664745 131.9 wPt-6780 67.0 wPt-671737 wPt-671892 wPt-668120 wPt-6704 Fig. 2-2. Genetic linkage maps bearing stable68.6 QTLwPt-1554 associated to seven supernumerary-spikelet-related traits in a RIL population of 159 individual 68.9 wPt-6850 derived from the cross between the elite line70.5 WCB414wPt-731134 and the PI with supernumerary spikelets (SS) WCB617 72.1 wPt-667476 73.4 wPt-733932 74.0 wPt-733033 74.5 wPt-732509 wPt-671669 74.8 wPt-730568 75.1 wPt-671914 wPt-667124 76.8 wPt-5014 79.3 wPt-668017 82.8 wPt-671623 87.7 wPt-667536 90.5 wPt-4242 94.5 wPt-730613 98.3 wPt-732270 105.4 wPt-730427 wPt-667485 wPt-6064 107.8 wPt-741208 109.0 wPt-732923 wPt-665836 wPt-732052 109.8 wPt-0153 wPt-667765 wPt-4329 wPt-7825 110.2 wPt-5106 111.2 wPt-733725 111.6 wPt-6514 114.0 wPt-3757 115.8 wPt-9848 125.7 wPt-3692 wPt-666518 131.9 wPt-6780 2D 7B.3
0.0 wPt-2675 27.4 wPt-8134 36.2 wPt-2761 38.6 wPt-741029 wPt-3677 wPt-5683 39.7 wPt-663767 wPt-732866 wPt-734114 wPt-740855 40.3 wPt-732705 40.9 wPt-671410 41.5 wPt-5574 2D 6B.2 41.8 wPt-667785 wPt-3144 wPt-664967 wPt-9802 wPt-730529 wPt-2675 0.0 wPt-730057 wPt-6609 wPt-8134 27.4 wPt-7243 wPt-6096 wPt-2761 36.2 42.1 wPt-671455 wPt-0936 wPt-741029 38.6 wPt-4093 wPt-742673 wPt-3677 wPt-5683 wPt-2186 wPt-6342 wPt-663767 wPt-732866 39.7 wPt-2294 wPt-734114 wPt-740855 7B-1 42.4 wPt-7149 wPt-664901 40.3 wPt-732705 6B-1 43.4 wPt-667843 0.0 wPt-4025 40.9 wPt-671410 43.7 wPt-4144 wPt-666857 2.1 wPt-0920 41.5 wPt-5574 0.0 wPt-1784 44.1 wPt-740836 2.5 wPt-1587 41.8 wPt-667785 10.5 wPt-6994 45.5 wPt-3812 3.4 wPt-8919 wPt-3144 wPt-664967 15.6 wPt-4706 47.3 wPt-667294 10.0 wPt-0312 wPt-9802 wPt-730529 17.4 wPt-7954 48.8 wPt-666223 18.9 wPt-2305 wPt-730057 wPt-6609 19.4 wPt-3800 49.5 wPt-741084 20.2 wPt-9516 wPt-7243 wPt-6096 19.8 wPt-0882 wPt-8239 42.1 50.1 wPt-666656 wPt-0085 26.0 wPt-6372 wPt-3873 wPt-671455 wPt-0936 21.1 wPt-743522 50.6 wPt-2652 wPt-6259 wPt-4093 wPt-742673 22.9 wPt-664276 34.6 wPt-8981 wPt-9665 51.1 wPt-667266 wPt-2186 wPt-6342 24.8 wPt-7207 39.1 wPt-2273 52.1 wPt-0650 wPt-2294 26.2 wPt-3130 53.0 wPt-741302
42.4 wPt-7149 wPt-664901 26.5 wPt-9990 wPt-8563
QSS.ndsu-2D QSS.ndsu-2D QSS.ndsu-2D QSS.ndsu-2D wPt-667671 QSS.ndsu-2D 56.3
43.4 wPt-667843 28.8 wPt-5066QSS.ndsu-2D 59.4 wPt-731489 54.6 wPt-3723 43.7 wPt-4144 wPt-666857 29.4 wPt-1089 wPt-9255 63.1 wPt-731406 44.1 wPt-740836 30.7 wPt-6674 65.0 wPt-664714 45.5 wPt-3812 33.3 wPt-4564 65.7 wPt-9900 47.3 wPt-667294 40.3 wPt-1756 66.1 wPt-733889 wPt-732277 48.8 wPt-666223 45.1 wPt-3116 79.1 wPt-4258 66.4 wPt-671859 49.5 wPt-741084 48.5 wPt-5234 89.3 wPt-744652
66.7 wPt-667406 wPt-664745
QSS.ndsu-7B.2 QPSS.ndsu-7B.2
50.1 wPt-666656 wPt-0085 53.4 wPt-2102 QSS.ndsu-7B.2 93.5 wPt-743502 QSS.ndsu-7B.2 67.0 wPt-671737 wPt-671892 QSS.ndsu-7B.2 wPt-2652 wPt-6259 wPt-6127 94.5 wPt-0266
50.6 57.8 wPt-668120 wPt-6704
QSS.ndsu-2D QSS.ndsu-2D
QSS.ndsu-2D
QSS.ndsu-2D QSS.ndsu-2D
QSS.ndsu-2D wPt-667266 wPt-1437 wPt-2095 96.0 wPt-4045 51.1 62.6 68.6 wPt-1554 52.1 wPt-0650 65.0 wPt-6594 96.9 wPt-8233 wPt-0635 68.9 wPt-6850 53.0 wPt-741302 67.3 wPt-4142 97.7 wPt-0194 70.5 wPt-731134 56.3 wPt-667671 67.7 wPt-4930 101.3 wPt-8615 72.1 wPt-667476 wPt-745074 QSS.ndsu-7B.1 wPt-2407
59.4 wPt-731489 72.3 QSS.ndsu-7B.1 106.2
QPSS.ndsu-7B.1 QSS.ndsu-7B.1
73.4 wPt-733932 QSS.ndsu-7B.1 wPt-731406 wPt-6667 112.0 wPt-9987 63.1 74.4 QSS.ndsu-7B.1 74.0 wPt-733033 65.0 wPt-664714 75.1 wPt-665017 wPt-3526 121.6 wPt-665428
74.5 wPt-732509 wPt-671669 QSS.ndsu-7B.1 65.7 wPt-9900 82.2 wPt-666793 123.7 wPt-9299 74.8 wPt-730568 66.1 wPt-733889 wPt-732277 85.4 wPt-5256 wPt-5975 wPt-5892 75.1 wPt-671914 wPt-667124 124.0 66.4 wPt-671859 86.7 wPt-8814 wPt-7887 76.8 wPt-5014 66.7 wPt-667406 wPt-664745 88.1 wPt-741515 131.7 wPt-1266 79.3 wPt-668017 67.0 wPt-671737 wPt-671892 92.3 wPt-1241 82.8 wPt-671623 wPt-668120 wPt-6704 101.3 wPt-3605 68.6 87.7 wPt-667536 wPt-1554 102.3 wPt-664250 90.5 wPt-4242 68.9 wPt-6850 108.6 wPt-2297 94.5 wPt-730613 70.5 wPt-731134 113.3 wPt-5408 98.3 wPt-732270 72.1 wPt-667476 113.7 wPt-8412 105.4 wPt-730427 wPt-733932 wPt-9971 73.4 130.1 wPt-667485 wPt-6064 wPt-733033 wPt-4742 74.0 139.0 107.8 wPt-741208 74.5 wPt-732509 wPt-671669 139.4 wPt-744396 SkNd QSS.ndsu-6B.2 109.0 wPt-732923 PSS wPt-730568 wPt-2218
74.8 141.4 wPt-665836 wPt-732052
QSS.ndsu-6B.2
QSS.ndsu-6B.2
QSS.ndsu-6B.2 QSS.ndsu-6B.2 75.1 wPt-671914 wPt-667124 QSS.ndsu-6B.2 141.8 wPt-743974 wPt-744302 QPSS.ndsu-6B.2 109.8 wPt-0153 wPt-667765 wPt-5014 wPt-0052 76.8 147.4 wPt-4329 wPt-7825 79.3 wPt-668017 155.6 tPt-1723 Sk NdSS 110.2 wPt-5106 82.8 wPt-671623 155.9 wPt-6247 111.2 wPt-733725 87.7 wPt-667536 167.4 wPt-741530 111.6 wPt-6514 90.5 wPt-4242 167.7 wPt-742929 wPt-6522 114.0 wPt-3757 SD NdNoSS 94.5 wPt-730613 184.1 wPt-7935 115.8 wPt-9848 98.3 wPt-732270 184.5 rPt-1040 125.7 wPt-3692 wPt-666518 105.4 wPt-730427 185.5 wPt-2564 131.9 wPt-6780 wPt-667485 wPt-6064 193.6 wPt-3168 107.8 NdR wPt-741208 195.6 wPt-743099 109.0 wPt-732923 208.9 wPt-3284 wPt-665836 wPt-732052 221.8 wPt-3581 109.8 wPt-0153 wPt-667765 222.1 wPt-9881 wPt-4329 wPt-7825 243.9 wPt-1048 110.2 wPt-5106 111.2 wPt-733725 111.6 wPt-6514 114.0 wPt-3757 115.8 wPt-9848 125.7 wPt-3692 wPt-666518 Fig. 2-2. Genetic linkage maps bearing stable QTL associated to seven supernumerary-spikelet-related 131.9 wPt-6780 traits in a RIL population of 159 individual derived from the cross between the elite line WCB414 and the PI with supernumerary spikelets (SS) WCB617 (Continued)
All seven QTL were associated with PSS and NdSS However, only six QTL were associated with
Sk, SD and SkNd (except QSS.ndsu-6B.1), while five QTL each were associated with NdR (except
QSS.ndsu-6B.1 and QSS.ndsu-7B.2) and NdNoSS (except QSS.ndsu-2D and QSS.ndsu-7B.2). The
phenotypic variation explained (PVE) by individual QTL in different environments ranged from 3.3-25.3%
for PSS, 4.0-25.2% for Sk, 4.6-14.8% for NdNoSS, 4.4-18.6% for NdR, 3.6-34.8% for NdSS, 4.8-33.5%
for SD and 3.6-37.3% for SkNd. The maximum PVE explained by all the QTL in individual environment
was 77.39, 43.27, 37.53, 82.41, 59.69, 73.86 and 81.78 for PSS, NdNoSS, NdR, NdSS, NSk, SD, and
SkNd, respectively.
Among the seven QTL, two QTL (QSS.ndsu-2D and QSS.ndsu-7B.2) have major effect (PVE>
15%), while five (QSS.ndsu-5B, QSS.ndsu-6A, QSS.ndsu-6B.1, QSS.ndsu-6B.2, QSS.ndsu-7B.1) have
minor effect (PVE<15%) on phenotypic variation. The QTL on chromosome 2D (QSS.ndsu-2D) was
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associated with all the traits except NdNoSS in all environments. This QTL explained up to 25.2% of the phenotypic variation. Another major QTL was identified on chromosome 7B (QSS.ndsu-7B.2) and was associated with five traits (PSS, Sk, SD, SkNd, NdSS). This QTL contributed up to 37.3% of the phenotypic variation for the associated traits.
Additive effects with a negative symbol indicate that alleles from the parent with SS (WCB617) are responsible for increased trait values, while the additive effects with a positive value indicate that the alleles from WCB414 are contributing towards increased trait values (Table 2-2). In the present study, both parents contributed alleles for increased trait values (Table 2-2). For traits with high phenotypic values in branched lines (PSS, Sk, SD, NdR, SkNd and NdSS), alleles from WCB617 contributed to increased trait values at QSS.ndsu-2D, QSS.ndsu-5B, QSS.ndsu-6A, QSS.ndsu-6B.1, and QSS.ndsu-
7B.2 loci, while alleles from WCB414 were responsible for increased trait values at QSS.ndsu-6B.2 and
QSS.ndsu-7B.1 loci. For NdNoSS, a trait whose expression is higher in conventional spikes, alleles from
WCB617 contributed to increased trait values at QSS.ndsu-6B.2 and QSS.ndsu-7B.1 loci, while alleles from WCB414 increase the trait values of NdNoSS at QSS.ndsu-5B, QSS.ndsu-6A, and QSS.ndsu-6B.1.
MIM showed that QSS.ndsu-2D had significant additive epistatic interactions with all other QTL
(Table 2-3). In addition, epistatic interactions were also observed between QSS.ndsu-5B and QSS.ndsu-
6B.2, QSS.ndsu-6Aand QSS.ndsu-6B.2, QSS.ndsu-6Aand QSS.ndsu-7B.1, and QSS.ndsu-7B.1 and
QSS.ndsu-7B.2 for several SS related traits (for details, see Table 2-3). All of the epistatic interactions, except between QSS.ndsu-5B and QSS.ndsu-6B.2, and QSS.ndsu-6Aand QSS.ndsu-7B.1, were detected for at least two SS-related multiple traits. These interactions also showed a differential phenotypic response (variation in R2) in different environments (Table 2-3). The maximum value of PV explained (70.4%) by these interactions was observed for interaction between QSS.ndsu-7B.2 and
QSS.ndsu-7B.1. However, excluding that particular case, the remaining interaction explained up to 22.4% of the PV for different traits in different environments (Table 2-3).
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Table 2-3. Additive Epistatic Interaction among QTL associated to SS-related traits Epistatic Interaction Trait† Environment Effect‡ Effect (%) QSS.ndsu-2D × QSS.ndsu-5B PSS VI 0.80 15.2 NdSS I 0.97 8.2
QSS.ndsu -2D × QSS.ndsu-6A PSS IV, V, AE 0.27 -0.58 5.4 -13.8 Sk I, III, IV 2.23-2.95 5.4-8.2 SD III, AE 0.21-0.30 5.6-5.8 SkNd I, III 0.15-0.20 5.3-6.2 NdR AE 0.38 8.9
QSS.ndsu -2D × QSS.ndsu-6B.1 PSS II 0.55 15.5 NdSS III, AE 2.16-2.31 14.5-18.9
QSS.ndsu -2D × QSS.ndsu-6B.2 Sk I - 2.45 5.2 SD I, IV -0.25_-0.53 5.6-18.1 SkNd IV -0.22 8.7 NdSS II -2.21 22.4
QSS.ndsu -2D × QSS.ndsu-7B.1 SD IV - 0.48 17.2 PSS III -0.31 6.3 Sk IV, AE -2.38_-3.19 7.4-9.2 SkNd I -0.35 13.3 NdR IV, AE -0.29_-0.40 6.5-9.4 NdSS IV -1.00 12.7
QSS.ndsu -2D × QSS.ndsu-7B.2 Sk AE 3.08 8.1 SD IV 0.42 12.2 SkNd I 0.33 7.9
QSS.ndsu-5B × QSS.ndsu-6B.2 NdSS I -0.88 9.4
QSS.ndsu-6A× QSS.ndsu-6B.2 Sk I -1.77 6.4 SD AE -0.13 4.5 SkNd IV -0.20 9.2 NdR III -0.36 6.4 NdNoSS III-AE 1.70-1.82 10.6-13.9
QSS.ndsu-6A× QSS.ndsu-7B.1 NdNoSS III, IV, AE 0.49-0.75 5-5.7 (Continues)
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Table 2-3. Additive Epistatic Interaction among QTL associated to SS-related traits (Continued) Epistatic Interaction Trait† Environment Effect‡ Effect (%) QSS.ndsu-7B.2 × QSS.ndsu-7B.1 PSS IV -0.37 12.1 SD IV -0.14 6.7 SkNd AE -0.74 70.4 NdSS III, AE -1.78_-2.11 7.2-7.8 †Traits were defined in Table 1 ‡additive by additive interaction between two loci, positive value indicate that epistatic interaction with parental two locus combination has increased trait values, negative value indicate that epistatic interaction with recombinant two locus combinations has increased trait values
In order to identify the highly significant marker from each QTL interval, single marker regression analysis was conducted using all the markers present in each ROAT/QTL confidence interval. For
QSS.ndsu-2D, the closest linked marker with SS-related traits was wPt-667785, which was present at a distance of about 0.3 cM away from the QTL peak. The closest markers associated with SS related traits for QSS.ndsu-5B, QSS.ndsu-6A, QSS.ndsu-6B.1, QSS.ndsu-6B.2, QSS.ndsu-7B.1 and QSS.ndsu-7B.2 were wPt-3569, wPt-671561, wPt-0406, wPt-3284, wPt-0194 and wPt-4258, respectively. All these markers were present at distances ranging from 0.0 to 5.5 cM away from the respective QTL peaks
(Table 2-2; Fig. 2-2).
2.5. Discussion
Previous studies have consistently shown that increased seeds per spike results in increased yield (McNeal, 1960; Hucl and Baker, 1987; Feil, 1992; Calderini et al., 1995; Wang et al., 2002; Green et al., 2012). Other studies have reported a positive association between seeds per spikelet and yield
(McNeal, 1960; Siddique et al., 1989; Feil, 1992) and between kernel weight and yield (McNeal, 1960;
McNeal et al., 1978; Hucl and Baker, 1987; Wang et al., 2002). Based on these studies, branched spikes with SS can affect directly the number of kernel per spike and therefore, it should be of prime interest for wheat breeders. Additionally, SS can affect the kernel size and shape which may affect kernel weight, another yield component, and kernel weight volume. Mapping SS and eventually developing markers for these QTL would potentially be of great help to improve kernel yield and quality.
Our study showed that about 29% of RILs expressed SS phenotype in at least one experimental replicate across the environments. The presence of a heterobranching behavior observed in most of the
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RILs bearing SS is in agreement with previous studies (Percival, 1921; Denčić, 1988; Huang and Yen,
1988; Martinek and Bernard, 1998; Klindworth et al., 1990a) and it is a challenge in the phenotyping of the SS trait. Considering that this phenomenon could be confused with seed admixtures, in the present research, the seeds for each genotype were obtained from individual head-rows, thus avoiding any chances of admixture. The cause of heterobranching is still not fully understood; however, the influence of the environmental conditions has been suggested as a reason for this phenomenon (Denčić, 1988;
Huang and Yen, 1988; Klindworth et al., 1990a). In case of genotypes showing heterobranching behavior, predominant spike types were collected for data recording to avoid mis-phenotyping. When PSS screening resulted in a score of 2 (ratio 1:1) both types of spikes were collected; but the data on SS- related traits was recorded for the dominant phenotype, determined through the replicates and environments, to control a possible bias produced by the visual screening of PSS.
High values of heritability observed for all SS traits (Table 2-1) suggest that the phenotypic variation observed among the RILs was mainly due to genetic factors. Indeed, most of the RIL were stable in the presence/absence of the SS trait. The presence of significant G × E interaction for all traits was most likely caused by differences in magnitude rather than the difference in rank. Prosper 2010 was the most conducive environment for Sk (Table Appendix A1). Interestingly, among the four environments in which this trait was studied, Prosper 2010 had the highest values for air temperature and rain fall (190C and 80.8 mm respectively) (NDAWN, 2012). Contrarily, Pennell and Halloran (1984a, 1984b) suggest that low temperature, strong vernalization response, and short photoperiods are conductive for the expression of SS.
In the past, variable number of DArT markers have been mapped in different genetic populations in hexaploid wheat; 339 by Akbari et al. (2006), 189 by Semagn et al. (2006), 1,348 by Sorrells et al.
(2011), 246 by Bennett et al. (2012). Mapping of a large number of DArT markers by Sorrells et al. (2011) compared to other studies was due to the fact that the DH population used in that study was developed from the divergent cross of Synthetic W7984 (Altar84/Aegilops tauschii (219) CIGM86.940) and Opata
M85. Similar to the study of Sorrells et al. (2011), a much higher number of markers (939 DArT markers) were mapped in the present study relative to previous studies in wheat (Akbari et al., 2006; Semagn et al., 2006; Bennett et al., 2012). This is likely due to the reason that one of the parental genotype (WCB
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617) used in the development of this population was an exotic line. This population, developed from an exotic and an elite wheat genotype, also resulted in a better coverage across the whole genome. This is evident from the fact that a large number of markers (almost similar to that of A and B-genome) were also mapped to the D-genome chromosomes which generally shows very low level of polymorphism in cultivated germplasm pool. This is because D-genome is a recent evolutionary addition to the hexaploid wheat genome (> 10,000 years old), and there has been limited gene flow from Ae. tauschii (Dubcovsky and Dvorak, 2007), thus, decreasing the rate of polymorphism. The only chromosome for which a framework genetic map could not be established due to lack of polymorphic markers, was chromosome
4D. Similar to the present study, several studies in the past have also reported a low level of polymorphism for wheat chromosome 4B, 4D, 5A, 5D (Akbari et al., 2006; Semagn et al., 2006; Bennett et al., 2012; Kumar et al., 2013). The length of the framework map (3,114.2 cM) developed in the present study is in agreement with the previous studies (Akbari et al., 2006; Semagn et al., 2006; Bennett et al.,
2012; Kumar et al., 2013) and also suggests a good representation of the whole wheat genome. The average distance between any two markers was 4.6 cM which is high enough for any QTL mapping study.
A total of seven consistent QTL (detected in at least three environments for one trait) were identified for SS related traits. Except for QSS.ndsu-6B.1, all the other QTL reported in this study had a
LOD larger than the empirical LOD threshold (calculated through permutation test) for at least one trait in one environment. The presence of QTL with LOD below of the empirical LOD threshold could be caused by the skewed distributions of the phenotypic data. Previous studies have reported that this type of distributions tend to produce large significance thresholds and increases Type II error (Manichaikul et al.
2007). Therefore, we believe our approach based on declaring the existence of a consistent putative QTL across environments is a valid approach to avoid ignoring real QTL as indicated by previous reports
(Börner et al. 2002; Tsilo et al. 2010).
Four QTL (QSS.ndsu-5B, QSS.ndsu-6A, QSS.ndsu-6B.2, and QSS.ndsu-7B.1) were involved in the control of all SS-related traits. Of the other three QTL, QSS.ndsu-2D was associated with all traits except NdNoSS; QSS.ndsu-6B.1 was associated only with PSS, NdNoSS, and NdSS; and QSS.ndsu-
7B.2 was associated with all traits except NdR and NdNoSS (Table 2-2). These results demonstrates that
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wheat SS is regulated by several genes as suggested in earlier studies based on progeny and monosomic analysis (Huang and Yen, 1988; Klindworth et al., 1990a; Peng et al., 1998). At the same time, these results in terms of inheritance patterns are different from few other studies in hexaploid branched wheat which suggested that only two genes control the formation of branched spikes, while another gene suppresses the expression of branched spikes (Koric, 1973; Pennell and Halloran, 1983;
Denčić, 1988). The differences in the number of genes for SS phenotype reported in present and previous studies could be the result of the different methodologies used for the genetic analysis (QTL mapping, monosomic analysis, progeny analysis) or even differences among the material used for these studies. Indeed, the different approaches used to classify branched spikes in triticum (Sharman, 1967;
Koric, 1973; Pennell and Halloran, 1983, 1984a, 1984b; Martinek and Bednár, 1998; Aliyeva and Aminov,
2011; Yang et al., 2005) may also lead to different conclusions. However, it is worth mentioning that in contrast to this study, most of the previous studies did not use the recently developed genomic tools and/or were limited to only few chromosomes of the wheat genome.
The partitioning of the SS-trait into seven components and the use of RIL population contrasts with the methodologies used in previous studies to genetically dissect the SS phenotype in wheat. Earlier studies disregarded the heterobranching behavior and scored the SS trait as present/absent (qualitative evaluation) in individual plants belonging to F1, and F2 generations (Peng et al., 1998; Dobrovolskaya et al., 2009). In this study too, in addition to QTL analysis of the categorical data for SS, we also attempted to identify the genes using presence/absence data of SS in RIL population. The PSS scores across six environments were used to classify the RILs into two discrete categories, branched (score ≥0.5) and non- branched (score<0.5). Mapping for these binary qualitative data resulted in the identification of only four of the seven QTL detected in this study (QSS.ndsu-2D, QSS.ndsu-6A, QSS.ndsu-6B.1, and QSS.ndsu-
6B.2). Three QTL QSS.ndsu-5B, QSS.ndsu-7B.1 and QSS.ndsu-7B.2 could not be detected in this analysis (data not shown), suggesting that the scale (0-4) used in this study was more informative for QTL detection than a binary scale (present/absent).Therefore, we concluded that it is important to make use of all available variation for any trait and that it is not appropriate to arbitrarily classify quantitatively varying data into discrete categories.
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The traits for which all the detected QTL explained higher percentage of phenotypic variation in a determined environment was NdSS (PVE = 82.41%) While, the traits for which QTL explained the lower percentage of phenotypic variation in a determined environment was NdNoSS (PVE = 6.28%).
Considering the high heritability observed for all the traits (Table 2-1), the differences in phenotypic variation explained by same QTL in different environments and for different traits could be due to environmental factors.
The QTL on 2D (QSS.ndsu-2D) was associated with all traits except NdNoSS in all the environments and explained the major proportion of the phenotypic variation for these traits. The graphical genotype of the RILs with SS and non-SS phenotype clearly shows the impact of this major gene in controlling the SS phenotype (Fig. 2-3). All the RILs except one with LPSS score ≥ 1 showed the presence of WCB617 (parental genotype with SS phenotype) allele at QSS.ndsu-2D, while the alleles of
WCB414 (parental genotype with non-SS phenotype) were present in RILs showing non-SS phenotype with only very few exceptions which could be the result of double recombination occurring between the gene and the markers and also may be due to experimental errors (Fig. 2-3). Previous studies conducted using monosomic analysis also reported the presence of a major gene controlling SS on 2D (Millet 1986,
1987; Peng et al. 1998). Using molecular markers, Dobrovolskaya et al. (2009) also identified a gene,
Mrs1, on 2D which was responsible for an SS-like trait (multirow spike) in hexaploid wheat. However, in contrast to other studies on SS, including the present, it was reported that MRS is controlled by only one recessive gene located on chromosome 2D. Mrs1 co-segregate with the microsatellite locus Xwmc453 and is located in a gene rich region (2S0.8) of the distal half of the short arm of chromosome 2D
(Erayman, 2004; Dobrovolskaya et al., 2009). Comparative study shows that Mrs1was mapped at 73.4 cM (total 2D map length= 119.6 cM), whereas, QSS.ndsu-2D was mapped at 41.5 cM (total 2D map length= 131.6 cM). This suggests that Mrs1and QSS.ndsu-2D could be different genes involved in spike morphology. Moreover, multirow spike studied by Dobrovolskaya et al. (2009) and the phenotype evaluated in the present study have been classified into different groups (Martinek and Bednar, 1988).
However, only future studies involving detailed characterization or cloning of these genes will be able to answer the question if Mrs1and QSS.ndsu-2D represent same gene or not.
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Fig. 2-3. Graphical genotypes of RILs with SS phenotype (All RILs with LPSS score ≥1) and non-SS phenotype (20 random RILs with LPSS score=0) for the 2DS region harboring major QTL (QSS.ndsu-2D)
In tetraploid wheat, it was reported that branched head is controlled by one major gene (bh) located on 2A and one minor gene located on 2B. However, these genes are inhibited when the chromosome 2D from “Chinese spring” (conventional spikes) is incorporated into 2D monosomic additional lines (Klindworth et al., 1990b). Considering that the D-genome is not shared between hexaploid and tetraploid wheat, it was suggested that bh could be an orthologue to Mrs1 (Dobrovolskaya et al., 2009). Major role played by homoeologous chromosomes 2A, 2B and 2D in controlling spike related traits has also been reported in several studies in the past. For common wheat with no-SS, Li et al. (2002) and Kumar et al (2007) identified major QTL associated with spikelet number in a gene rich region of the short arm of chromosome 2D; while Shitsukawa et al. (2006) reported that the chromosomes 2A, 2B and 2D of “Chinese Spring” carry three homoeologous copies of the gene WFL
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(Wheat FLORICULA/LEAFY) involved in the spikelet formation. In addition, chromosome 2DS has been reported to harbor QTL/genes for a number of other traits including yield, threshability-related traits, days to heading (PpD-1), growth related traits (Sourdille et al., 2000, 2003; Börner et al., 2002; Groos et al.,
2003; Jantasuriyarat et al., 2004; Hanocq et al., 2004; Kumar et al., 2007). It would be interesting to know if this region on 2DS represents a cluster of genes controlling different genes for different traits or a single gene having pleiotropic effect on several traits.
In addition to the major QTL on chromosome 2D, a QTL on chromosome 7B (QSS.ndsu-7B.2) also explained a high percentage of the phenotypic variation for some of the traits (Table 2-2). However, the detection of QSS.ndsu-7B.2 in only some environments suggests that its expression is influenced by environmental conditions.
A gene on chromosome 7B controlling SS in hexaploid wheat has not been reported previously, suggesting that this novel QTL could be related to the specific material used in the present study. But several studies in the past have mapped genes for heading or ear emergence date and earliness per se on chromosome 7B of wheat (Kuchel et al., 2006; Maccaferri et al., 2008; Griffiths et al., 2009; Bennett et al., 2012) and 7H in barley (Laurie et al., 1995). However, all these studies mapped genes/QTL on the short arm of the chromosome 7B or 7H, while the two QTL detected in the present study were located on long arm of 7B chromosome, suggesting that QSS.ndsu-7B.1 and QSS.ndsu-7B.2 are most likely different than the already reported QTL/genes for heading date and earliness per se on 7B.
The remaining five QTL (QSS.ndsu-5B, QSS.ndsu-6A, QSS.ndsu-6B.1, QSS.ndsu-6B.2 and
QSS.ndsu-7B.1) explained minor portions of phenotypic variation for SS-related traits (Table 2-2), which suggests that chromosomes 5B, 6A, 6B and 7B possess minor genes for the SS phenotype. The location of these minor QTL differs with the findings of Peng et al. (1998), which suggested the presence of minor genes associated with branched spikes on chromosomes 4A, 4B and 5A through monosomic analysis.
The identification of different minor genes in both studies could be attributed to the differences in germplasm as well as methodologies (monosomic analysis vs. genetic mapping) used in each investigation. Another reason for different results could be differences in genetic polymorphism in the parental genotypes of the populations.
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The five minor QTL (QSS.ndsu-5B, QSS.ndsu-6A, QSS.ndsu-6B.1, QSS.ndsu-6B.2 and
QSS.ndsu-7B.1) were also associated with NdNoSS; while the two major QTL QSS.ndsu-2D and
QSS.ndsu-7B.2 were not associated to this trait. These results suggest that these minor genes could be related to the formation of the non-SS spikelets. Although, for SS phenotype, these five minor QTL are being reported for the first time, QTL for several other related traits have been reported in these regions in the past studies. For example, on 6AS where QSS.ndsu-6A was identified, several studies have reported
QTL for spikelets per spike, spike compactness, grains per spike and days to heading (Kumar et al.,
2007, Jantasuriyarat et al., 2004; Huang et al., 2003; Huang et al., 2004). Similarly, on long arm of 6B where QSS.ndsu-6B.1 and QSS.ndsu-6B.2 were detected, QTL have been reported for grains per spike and spike fertility (Li et al., 2007).
The QTL QSS.ndsu-5B was mapped in the telomeric region of long arm of chromosome 5B (Fig
2-2). The long arm of 5BL also harbors vernalization gene Vrn-B1 which control the flowering time in wheat. This gene has been physically mapped in the bin 5BL18-0.66-0.79 (Timonova et al. 2013).
However, comparison of the maps shows that marker wPt-1348 which is associated with QSS.ndsu-5B and is located proximal to the QTL, is present in the 5BL18-0.79-1.00 deletion bin (Francki et al. 2009) suggesting that this QTL is present in the telomeric bin of 5BL.This also means that QSS.ndsu-5B is most likely a different gene other than Vrn-B1.
The additive effects observed for QSS.ndsu-6B.2 and QSS.ndsu-7B.1 indicate that non-branched parent contributed the alleles that modify the phenotypic values of SS-related traits. The presence of an
SS-related gene in commercial varieties was previously observed by Pennell and Halloran (1983).
According to this study, a recessive inheritance pattern for SS was observed in the F1 of crosses between the commercial variety “Phoenix” (non-SS) and the branched line “AUS15910”. However, a progeny with
SS phenotype was observed in the first backcross using “Phoenix” as recurrent parent. Pennell and
Halloran (1983) suggested the presence of a suppressor in “Phoenix” which inhibits the production of SS, but no further experiments were conducted to characterize the gene action. Therefore, the contribution of positive alleles at QSS.ndsu-6B.2 and QSS.ndsu-7B.1 by the non-branched parent constitute new evidence in this direction. Considering that these QTL also were associated with NdNonSS, it is possible that they have a positive effect on spikelet production in varieties with non-SS. Therefore, they could be
79
useful to breeding programs for increasing grain yield. Further genetic and molecular experiments will be necessary to characterize these QTL in hexaploid wheat.
Previous studies have suggested a role of epistatic interaction in the expression of the SS trait
(Koric, 1973; Klindworth et al., 1990a; Denčić, 1988). In view of this, all the seven main effect QTL were tested for di-genic epistatic interactions (Table 2-3), using multiple interval mapping. The results reported in the present study are the first evidence at molecular level to demonstrate the role of epistatic interactions in the expression of SS phenotype. The previous studies using progeny analyses have suggested that a complementary action of two dominant genes control SS phenotype in wheat line 51885
(Sun et al., 2009). Other studies also suggested complementary action of the factors Rm-ramifera and Ts- tetrastichon in absence of an inhibitor gene called N (Koric, 1973, Denčić ,1988). The inhibitor was later located on chromosome 2D (Klindworth et al., 1990a). In the present study, the major and consistent QTL
QSS.ndsu-2D, had additive epistatic interaction with all the other QTL, which explain up to 22.4% of the
PV (Table 2-3). The identified interaction involve both parental two locus combinations (interactions with positive additive effect) as well as recombinant two locus combinations (interactions with positive additive effect), suggesting that specific allele combinations among QSS.ndsu-2D and the other loci can increase or decrease the expression of the SS trait in a particular genotype. Indeed, a few of RILs had the
QSS.ndsu-2D allele derived from the branched parent but never expressed the branched phenotype
(data not shown). Nevertheless, epistatic interactions were not limited to locus QSS.ndsu-2D. All other identified QTL were also involved in epistatic interactions (Table 2-3), including the interaction between
QSS.ndsu-7B2 and QSS.ndsu-7B1 which explained up to 70.4 of PV.
It may also be noted that most of the identified QTL for SS were involved in two or more digenic interactions (Table 2-3) suggesting a network which may represent higher order interactions. It has been suggested that the metabolic pathways that presumably underlie quantitative traits involve multiple interacting gene products and regulatory loci that could generate higher-order epistatic interactions
(McMullen et al., 1998). However, most of the software dealing with epistasis includes only two-locus interaction. This is partly because including higher order interactions requires too many parameters in the genetic model, which would be difficult to estimate properly except in extremely large populations. For example, a three-locus model may need a population size of over 1000 lines to enable reasonably
80
reliable estimations for all parameters. As a result, it would be very difficult to work with an epistatic model involving more than three loci (Jannink and Jansen, 2001; Xu and Crouch, 2008). However, as this is the first study which used an RIL population to uncover the genetic basis of SS phenotype, future studies using larger populations, and suitable statistical methods may be able to identify the role of higher order interactions in the genetic control of SS.
Unlike wheat, in other grass species, the molecular basis behind branched phenotype is better understood (for a review, see Sreenivasulu and Schnurbusch, 2012). In maize (Zea maize), it is known that mutations in the genes RAMOSA1 (RM1), RM2, RM3 and RAMOSA1 ENHANCER produces branched phenotypes in the tassel and the ear (Vollbrecht et al., 2005; Bortiri et al., 2006; Satoh-
Nagasawa et al., 2006; Gavalloti et al., 2010 ). These genes encode transcription factors (EAR-containing zinc-finger transcription factor and the REL2 transcriptional co-repressor) which works as repressors of the indeterminate fate of spikelet-pair meristem (Gavalloti et al., 2010). Although ramose genes are mainly observed in the tribe Andropogoneae which includes maize, sugar cane (Saccharum officinarum) and sorghum (Sorghum bicolor) (Kellogg, 2007), similar genes playing an important role in inflorescence development have also been identified in other grasses. For instances, a sister gene of RA3 called
SISTER OF RAMOSA (SRA) was found in rice (Oryza sativa) (Satoh-Nagasawa et al., 2006); while a gene called six-rowed spike4 (Vrs4), an ortholog of RAMOSA2 of maize, was recently isolated in barley
(Hordeum vulgare L.) (Koppolu et al., 2013). It would be interesting to find out, if genes controlling SS phenotype in wheat also belong to the same gene families as reported in maize, rice and barley. An initial step in this direction would be to design markers from the available wheat sequences orthologous to the above mentioned genes in maize, rice and barley and to map them in wheat. The new information reported in this paper provides an excellent basis for future studies directed at gaining additional functional and evolutionary knowledge about this important phenotype.
2.6. Conclusions
In this study, a whole genome genetic map of an RIL population derived from a cross between a branched spike genotype and conventional spike genotype in hexaploid wheat was reported and used for identification of QTL associated with branched spike phenotype. Whole genome QTL analysis for
81
branched spikes and related traits resulted in the identification of seven QTL. Consistent to the previous finding, a major QTL for branched spike phenotype was identified on chromosome 2D (QSS.ndsu-2D).
However, another major QTL on 7B (QSS.ndsu-7B.2), several other novel minor QTL, and epistatic interactions involved in the genetic control of branched spike phenotype were also identified in the present study. The major QTL on chromosome 2D could be the initial target for future studies aimed at fine mapping and ultimately cloning the underlying gene. Finally, as spike-related traits are the major components of final yield of wheat crop, the identified gene network for SS phenotype can play an important role in increasing wheat yield since it allows the formation of more spikelets in the spike.
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CHAPTER 3. GENOME-WIDE MAPPING OF SPIKE-RELATED AND AGRONOMIC TRAITS IN A
COMMON WHEAT POPULATION DERIVED FROM A SUPERNUMERARY PARENT AND AN
ELITE PARENT
3.1. Abstract
Commonly grown wheat (Triticum aestivum L) cultivars are characterized by spikes with fusiform shape, and spikelets evenly distributed. However, exotic genotypes express a broad range of spike- related traits and could be used as a source of new genes to enrich the germplasm of wheat breeding programs. In the present study, a population of 163 recombinant inbred lines derived from an cross between an elite line (WCB414) and an exotic line with supernumerary spikelet (SS) (WCB617) was evaluated over four to six environments and used to identify QTL associated with ten spike-related, and ten agronomic traits. Composite interval mapping identified a total of 145 QTL which were located on 17 wheat chromosomes and included 37 consistent QTL. The QTL identified for individual traits ranged from two for apical awnleted expression to 14 in number of spikes per m-2. The phenotype variation explained
(PVE) by individual QTL ranged from 0.61% to 91.8%. The major QTL for glume pubescences was located in a 1AS QTL cluster and was associated with other traits such as kernels per spike and spike length. Likewise, the major QTL for SS was co-located in a cluster of QTL on 2DS with a yield-related
QTL with 40.3% of PVE and other QTL for agronomic and spike-related traits. Consistent and major QTL may be used in marker assisted breeding programs to transfer the desirable alleles into other germplasm.
Desirable QTL alleles were also contributed by the exotic line, suggesting the possibility of enriching the breeding germplasm with alleles from exotic genotypes.
3.2. Introduction
Spikes (also named ears) are the most symbolic benchmark of common wheat (Triticum aestivum
L.) and are the host of the main crop product: the grain. They are a photosynthetic site which provides carbohydrates accumulated in the mature grain impacting yield components (Grundbacher, 1963; Li et al,. 2006). Spike-related traits define the spike architecture and determine the phenotype of the wheat subspecies. For instance, the spikes of spelt wheat (T. aestivum ssp. spelta) have a fragile and log
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rachis, lax density with spikelets well separated from each other on the rachis, and are not free threshing
(Percival, 1921; Winzeler et al., 1994; Bertin et al., 2001; Jantasuriyarat et al.,2004).The spikes of club wheat (T. aestivum ssp. compactu), however have a tough rachis, short length, laterally compressed with spikelets closely packed and free-threshing (Percival, 1921; Zwer et al., 1995; Jantasuriyarat et al.,2004).
The cultivars of common wheat (T. aestivum ssp. aestivum) are characterized as having spikelets evenly distributed, tough rachis and free-threshing (Percival, 1921; Jantasuriyarat et al., 2004).
Variations in spike length, number of spikelets per spike, and density are also observed. Spikes with fusiform, oblong, and clavate shape are the result of changes in these traits (Briggle and Ritz, 1963).
The impact of spike-dimension traits on grain yield has prompted the study of the genetic basis of spike architecture. Three major complexes of genes, Q, C, and S1, play an important role in the genetic control of the spike dimensions of most of the wheat subspecies (Miller, 1987; Jantasuriyarat et al., 2004). In addition, the evidence collected during the last couple of decades suggests that variation in spike- dimensions traits in common wheat is caused by multiple QTL distributed throughout the wheat genome
(Sourdille et al., 2000b; Li et al., 2002; Börner et al., 2002; Jantasuriyarat et al., 2004; Marza et al., 2006;
Kumar et al., 2007; Ma et al., 2007; Chu et al., 2008; Cui et al., 2012).
QTL mapping studies typically using elite × elite populations discovered QTL and associated molecular markers suitable for breeding programs (Würschum, 2012). However, elite × elite populations often show low polymorphism, which results in non-saturated maps and the identification of fewer QTL.
Moreover, the QTL identified in such studies are mostly the ones which are already fixed in the cultivated germplasm. The use of populations derived from exotic × elite crosses could be a way forward to identify new desirable QTL and/or alleles. In the past, synthetic lines derived from the cross of tetraploid T. turgidum ssp. and diploid T. taushii have been used to map QTL for agronomic (Börner et al., 2002;
Kumar et al., 2007), morphologic (Li et al., 2002; Jantasuriyarat et al., 2006) and quality traits (Nelson et al., 2006).
Exotic wheat germplasm exhibits broad spike diversity for spike-related traits (Sharman, 1967;
Martinek and Bednár, 1998). Branched spikes, also known as supernumerary spikelets (SS), are one of the most well-known exotic variations in spike architecture (Sharman, 1944; Sharman, 1967; Rawson and
Ruwali, 1972; Koric, 1973; Klindworth et al., 1990a, 1990b; Pennel and Halloran, 1983; Yang et al., 2005;
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Dobrovolskaya et al., 2009; Sun et al., 2009; Haque et al., 2012; Sreenivasulu and Schnurbusch, 2012;
Zhang et al., 2012). These spikes have more than one spikelet per rachis node and have been suggested as a means to increase the yield of wheat through increasing the number of fertile florets where the grains are developed (Pennell and Halloran, 1983, 1984a, 1984b; Hucl and Fowler, 1992; Peng et al., 1998).
Several studies suggest that the SS trait is under control of at least three genes (Koric, 1973; Pennel and
Halloran, 1983; Klindworth et al., 1990a; Peng et al., 1998) (Chapter 2) with a major gene located on chromosome 2D (Klindworth et al., 1990b; Peng et al., 1998; Dobrovolskaya et al., 2009) (Chapter 2).
Apparently this gene is a suppressor of the expression of SS gene in commercial wheat varieties (Pennell and Halloran, 1983) (Chapter 2).
The impact of SS on other spike-related traits is not well known; but in terms of SS impact on agronomic traits, it is known that branched genotypes are mostly associated with reduced number of tillers, fewer grains per spike, low kernel weight, sterility in several florets, delayed days to heading and maturity, and low grain yield (Percival, 1921; Saluke and Asana, 1971; Rawson and Ruwali, 1972; Koric,
1973; Millet, 1986, 1987; Hucl and Fowler, 1992; Zhang et al., 2012). However, considering the broad genetic diversity of branched spikes in Triticum (Sharman, 1967; Martinek and Bednár, 1998), some branched genotypes break this trend. For instance, some lines in Tibetan Triple-Spikelet wheat have excellent spike grain production (>120 seeds per spike) with adequate thousand kernel weight (Yang et al., 2005). Moreover, it has also been suggested that the grain yield of branched genotypes can be increased if the fertility of the florets is improved (Rawson and Ruwali, 1972; Pennel and Halloran, 1984a,
1984b).
Past studies have predominantly investigated the association of agronomical important traits in branched genotypes using traditional methods. Few studies have, however, used molecular markers to identify genetic regions controlling the SS trait (Dobrovolskaya et al., 2009; Li et al., 2011; Haque et al.,
2012) (Chapter 2). However, those studies did not investigate the association of these loci with other agronomical important traits. In the present study, a recombinant inbred line (RIL) population derived from a cross between elite white wheat line (WCB414) and a wheat line with SS and pubescences on glumes
(WCB617) was develop to 1) detect QTL for 10 spike-related traits and 10 important agronomical traits of spring wheat, 2) identify if the QTL/genes for SS-related and other spike-related traits share common
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genomic region with important agronomic loci and 3) identify novel genes and/or alleles with potential use in breeding programs.
3.3. Material and Methods
3.3.1. Plant material
A population of 163 RIL developed from a cross between the hexaploid hard white wheat (HWW) elite line WCB414 with the exotic hexaploid hard red wheat (HRSW) line WCB617 was evaluated in this study. The RIL population was assessed atF8:9, F10:11 andF12:13 during the years 2009, 2010 and 2011, respectively. At the end of the growing seasons of 2009 and 2010 one spike from each experimental unit was collected and sent to New Zealand to grown as head-row. The bulked seed from each head row were planted in the next season. More details about the mapping population have been described elsewhere in Chapter 2. WCB414 is an elite line with fusiform architecture, awned, and glabrous glumes, while WCB617 is an exotic line with heterobranching behavior, awns, and pubescence on the glumes.
Seven checks, “Alsen” (PI 615543) (Frohberg et al., 2006), “Steele-ND” (PI 634981) (Mergoum et al.,
2005), “Glenn” (PI 639273) (Mergoum et al., 2006), “Faller” (PI 648350) (Mergoum et al., 2008), “Barlow”
(PI 658018) (Mergoum et al., 2011), and “Briggs” (PI 632970) (Devkota et al., 2007), and “Alpine”
(Agripro® wheat variety, USA) were also included. All the checks are HRSW except “Alpine” which is a
HWW.
3.3.2. Field experiment
The parents, RILs and checks were planted in a 13 × 13 partially balanced square lattice design with two replicates at Prosper and Carrington, North Dakota, USA, during the years 2009, 2010 and 2011.
The environments were designated as I-VI (I=Prosper 2009, II= Carrington 2009, III= Prosper 2010, IV=
Carrington 2010, V= Prosper 2011, and VI= Carrington 2011). Each genotype was planted in plots comprised of seven rows 2.44 m length, and 12.7 cm apart with a seeding rate of 113 kg ha-1. Due to a limited number of seeds in 2009, the parent WCB617 was planted in two rows plot in each environment.
The remaining five rows were planted to the commercial variety “Glenn”. The two rows of WCB617 were harvested by hand in 2009.
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3.3.3. Spike-related traits
Penetrance of pubescence (PP) measuring glabrous spikes and penetrance of clavate architecture (PC) measuring fusiform architecture were assessed using a scale of 0 to 4, where 4 indicate that all spikes in the experimental unit were pubescent or clavate, and 0 indicates that all spikes were glabrous or fusiform. An experimental unit with a ratio of 3:1 (Pubescent: glabrous spike type; or, clavate: fusiform spike type) corresponded to a score of 3, a ratio 1:1 was scored as 2, and a ratio 1:3 was scored as 1. Experimental units with apical awnleted expression (Aless) were scored as 1, and awned experimental units were scored as 0.
Four spikes were taken randomly from the primary tillers from each experimental unit to measure spike-related traits. The spikes of prevalent phenotype were collected in plots with penetrance of supernumerary spikelets (PSS) (Chapter 2), PP and PC scores of 3 and 1 following methodology used in
Chapter 2. When two phenotypes were present in equal proportions (ratio of 1:1; score 2) in any particular replicate of any environment, the prevalent phenotype for that particular genotype was decided based on the phenotype of other replicates and environments. The other spike-related traits for which data were recorded include spike length (SL) in centimeters, measured from the base of the rachis to the top of the uppermost spikelet, excluding awns; the number of nodes per spike (Nd); nodes density (NdD)
(nodes cm-1) calculated as Nd/SL; number of nodes with immature spikelets at the spike base (NNdISk); awn length at the bottom of spike (ALB), determined from the mean length of three awns collected randomly in the first three fully developed spikelets located at the spike base; awn length at the top of spike (ALT), calculated from the mean length of three awns collected in the last four fully developed spikelets at the spike top; awn length at middle of the spike (ALM), obtained from the average length of three awns collected in the spikelets located between the first three fully developed spikelets at the spike base and the last four full-developed spikelets at the spike top; and awn length total averaged (AAL), calculated as the mean of all the awn lengths measured per spike. In all the measurements of awns, the awns were chosen randomly from all the lemmas in specific spikelets; awns with evidence of damage were discarded. For each trait, the mean from the four spikes was calculated.
In 2011, PSS, PP, and PC assessments were performed in both locations, but other spike measurements could not be collected because Carrington was severely affected by hail and Prosper by
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flood. Consequently, for all traits except PSS, PP and PC, data were recorded in four environments. The evaluations of PSS reported in Chapter 2 were correlated with the spike-related and agronomic traits to identify effect of SS on other traits.
3.3.4. Agronomic traits
The agronomic traits assessed in this study were days to heading (DH), determined as the number of days from planting to heading; plant height (PH), measured (cm) at maturity from the soil surface to the tip of the spike, excluding the awns; days to maturity (DM) determined as the number of days from planting to maturity (appearance of a yellow peduncle at the base of the spikes); number of spikes (NS) (spikes m-2), determined by counting the number of stems in two 61-cm-long rows per plot and subsequently converted to stems per square meter; percentage of lodging (Ld), visually estimated by the degree (angle) and number of plants that lodged; kernels per spike (KS), determined from ten random spikes per plot collected before the harvesting of each experimental unit; kernels per spikelet (KSk) calculated from the means of five fully-developed spikelets per spike randomly selected; and kernels per node (KNd) obtained from the mean of five randomly selected nodes per spikes with fully-developed spikelets. In the case of spike with non-SS, the mean obtained for KSk was used for KNd. The four spikes used in the spike-related measurements were used to collect information of KSk and KNd. Therefore, a total of 20 measurements of KSk and KNd per genotype were collected and then averaged. After harvesting, the grain samples were cleaned using a clipper grain cleaner before grain yield (GY) was measured. The GY (kg ha-1) of each genotype was determined by the weight of the total seed harvested from each plot. Considering that the parent WCB617 was only planted in two rows in 2009, an estimated
GY for the entire plot was determined based on grain yield from the two rows.
Plant height was scored in all six environments, and NS was also collected in all the environments except Prosper 2011. Similarly, DH was collected in all environments except Carrington
2011 and DM was collected in all environments except Carrington 2009. Lodging was scored in the environments Prosper 2009, 2010, and 2011; and Carrington 2011. Data on the traits GY and KS were collected at Carrington and Prosper in 2009 and 2010.
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3.3.5. Statistical analysis
Analysis of variance (ANOVA) for a lattice design was carried out for every trait using the single environment data. The MIXED procedure of the Statistical Analysis System (SAS, 2004) was used for these analyses, where the RILs, parental genotypes, and checks were considered as fixed effects, and the environments and blocks were considered as random effects. The F-tests were considered significant at P≤0.05. Combined ANOVA analysis over environments was performed to estimate genotype × environment interaction, if the ratio between the largest and smallest experimental error of each environment was less than 10-fold (Fmax ratio) (Tabachnik and Fidell, 2001). The mean separation tests were conducted using an F-protected least significant difference (LSD) value. Pearson‟s correlation between traits was calculated in each environment. Correlations coefficients were considered significant at P≤0.05. Significant correlation coefficients obtained in each environment were assessed for homogeneity across environments at P≤0.005, following the steps described by Gomez and Gomez
(1984). Pooled correlations were calculated among homogenous correlations and considered as significant at P≤0.05.
Broad sense heritability was estimated on a plot basis (Holland et al., 2003), excluding the parent
and check means. = /[ + ( )+ ( )], where is the genotype variance, is the G × E interaction
variance, and is the error variance.
3.3.6. Molecular marker analysis, map construction, QTL identification, and genotypic analysis
Molecular map construction for this population was described in Chapter 2. The software, QTL
Cartographer V2.5_011 (Wang et al., 2012), was used to conduct composite interval mapping (CIM) using forward regression method and default conditions. The QTL analysis was conducted for each trait in each environment as well across environments (AE). A minimum LOD score of 2.5 was used to declare putative QTL. Permutation test with 500 permutations was used to determine critical LOD threshold
(α=0.05). Putative QTL below this critical threshold and identified in only one environment were not considered. In this study, to avoid obvious Type II error, if a putative QTL was identified in more than one environment (including AE), the QTL was reported regardless their significance in the permutation test. A
QTL was declared consistent when it was detected in at least 50% of the environments in which it was studied. Confidence intervals (CI) were calculated using ±2 LOD (from the peak) method (Lander and
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Bostein, 1989). QTL with overlapping CIs were considered as one QTL. A QTL was declared as major when explained at least 15% of phenotypic variation (PV) in one environment. The program MapChart 2.2
(Voorrips, 2002) was used to draw the linkage groups and QTL.
3.4. Results
3.4.1. Phenotypic variation
The RIL population segregated for presence/absence of SS, pubescences, clavate/fusiform architecture, and awned/apical-awnleted spikes (Fig. 3-1). However, the most prevalent spike phenotype in the RIL population was non-branched, glabrous, fusiform, and awned. For some RIL with presence of pubescences, glabrous spikes were also observed. This phenomenon was called hetero-pubescent and was assessed through the PP trait. Similarly, for RILs with presence of clavate architecture, fusiform spikes were also observed. This phenomenon was referred as hetero-clavate and was assessed by PC trait. Appendix Fig. B1 and Appendix Fig. B2 show the distribution of the RIL across the environments for spike-related and agronomic traits, respectively. The estimated means and ranges of all traits for the
RILs, parents, and checks in all environments as well in the combined analysis are presented in Appendix
Tables B1 and B2.
The traits had significant genotype × environment interaction (Table 3-1). For spike-related traits, broad sense heritability ranged from 0.43 for ALT to 0.89 for PP (Table 3-1). For agronomic traits, broad sense heritability ranged from 0.13 for KS to 0.67 for NNdISk (Table 3-1). The significant correlations among all traits including PSS are summarized in Table 3-2, Table 3-3 and Table 3-4. Coefficients of variation and lattice efficiency for each environment are presented in Appendix Table B3; while Appendix
Table B4 shows that error variances among the environments were homogenous for the traits.
In most of the environments the parental line WCB617 was pubescent. The exception was observed in Carrington 2009, where WCB617 had a hetero-pubescent phenotype. Through combined
ANOVA analysis, we observed that 86 RIL were classified as glabrous (PP=0), 29 RIL were hetero- pubescent with prevalence of glabrous type (0.1≤PP≤1.4), 24 RIL were hetero-pubescent with prevalence of pubescent type (1.4≤PP≤ 3.9), and 24 RILs had a PP mean of four. Non-transgressive segregation was
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observed for PP in all the environments except Carrington 2009 where we observed transgressive segregation toward the mean of the exotic parent (WBC617) (Appendix Table B1).
Fig. 3-1. Types of spikes identified in the RIL population derived from the cross of the elite line WCB414 and the exotic line with supernumerary spikelets WCB617. A. Spike with supernumerary spikelets. B. Spike apically awnleted. C. Spike with fusiform architecture at the top of the spike. D. Spike with clavate architecture at the bottom of the spike. E. Spike with glabrous glumes. F. Spike with pubescences on the glumes.
In all the environments, both parents were fusiform at the apical region of the spike (PC=0) (Fig.
3-1). Through combined ANOVA we observed that a total of 136 RIL were fusiform (PC=0), 21 RIL were hetero-clavate with prevalence of fusiform spikes (0.1≤PC≤1.9), and 6 RIL were hetero-clavate with prevalence of clavate spikes (2.3≤PC≤ 3.8) (Fig 3-1).
The RIL 1069, 1087, 1131 and 1134 were apically awnleted (Fig 3-1) across all replicates and environments. For other spike-related traits, SL, NdD, ALB, ALT, ALM, and AAL showed transgressive segregation in both directions of the parental means in all environments (Appendix Table B1 and
Appendix Fig. S1). Nd had transgressive segregation similarly in both directions of the parental means, but when the data was pooled across environments transgressive segregation only was observed toward the mean of the elite parent (WCB414) (Appendix Table B1and Appendix Table B2). In Carrington 2010,
Prosper 2009 and 2010, and AE, NNdISk had transgressive segregation in both directions of the parental means, however in Carrington 2009, it showed transgressive segregation in direction of the mean of the elite parent (WCB414) only (Appendix Table B1).
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Table 3-1. Mean squares, coefficient of variation and heritabilities for spike-related and agronomic traits NECAV‡ Mean squares CV(%)§ H¶ SE# Trait † Genotype Environment G x E Error Spike-related Traits PP 6 29.63** 0.22 0.53** 0.06 22.3 0.89 0.01 PC 6 1.96** 3.54 0.38** 0.1 185.4 0.57 0.03 SL 4 4.67** 19.10 0.42** 0.22 4.9 0.62 0.03 Nd 4 31.50** 239.61 2.46** 0.71 4.4 0.69 0.03 NdD 4 0.23** 0.77 0.02** 0.01 4.9 0.63 0.03 ALB 4 3.13** 93.83 0.44** 0.26 17.5 0.52 0.03 ALT 4 3.29** 143.89 0.60** 0.31 17.3 0.43 0.04 ALM 4 10.67** 19.39 0.56* 0.46 10.9 0.73 0.02 AAL 4 4.42** 61.09 0.29** 0.17 9.9 0.72 0.03 Agonomic Traits DH 5 78.61** 15683 8.55** 1.28 2.0 0.60 0.03 PH 6 761.75** 14462 51.64** 25.37 5.8 0.63 0.03 DM 5 54.17** 19931 10.05** 2.22 1.7 0.44 0.04 NS 6 13587** 319930 2204.96** 1623.44 16.0 0.26 0.03 Ld 4 1640.19** 286472 885.09** 209.99 33.7 0.14 0.04 KS 4 81.99** 13657 43.57** 18.06 12.7 0.13 0.03 KSk 4 14.66** 1.08 0.12** 0.07 10.3 0.59 0.03 KNd 4 0.5** 17.07 0.16** 0.08 10.8 0.26 0.04 NNdISk 4 6.03** 75.18 0.44** 0.3 37.0 0.67 0.03 GY 4 2086113** 74853141 391215** 99222 11.72 0.40 0.04 *, ** Significance at P < 0.05, 0.01, respectively; ns not significant at P < 0.05 †PP, penetrance of pubescences; PC, penetrance of clavate architecture; SL, spike length; Nd, number of nodes; NdD, node density; ALB, awns length at the bottom of spike; ALT, awns length at the top of spike; ALM, awns length at middle of the spike; AAL, awns length total averaged, DH, days to heading; PH, plant height; DM, days to maturity; NS, number of spikes; Ld, lodging susceptibility; KS, kernel spike; KSk, kernels per spikelet; KNd, kernels per node; NNdISk, number of nodes with immature spikelets at the spike base; GY, grain yield. ‡Number Environments in combined analysis of variance. §Coefficient of variation. ¶Broad sense heritability on plot basis calculated for the RILs. #Standard error for heritability.
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Table 3-2. Correlations coefficients between spike-related traits Traits† PP PC SL Nd NdD ALB ALT ALM AAL Aless PSS
PP 1 Ns 0.39**†† 0.47**†† 0.24**# -0.16*§ -0.33**# -0.19**¶ -0.23**¶ ns 0.38**‡‡
PC 1 -0.22**†† 0.22**§ 0.38**†† 0.15**§ -0.17**§ ns ns ns ns
0.64**# SL 1 -0.31**# Ns -0.33**¶ ns ns -0.19**¶ 0.29**†† 0.35**§
Nd 1 0.64**†† Ns -0.49**# ns -0.23**¶ -0.19*§ 0.52**††
NdD 1 Ns -0.33**# -0.19**§ -0.21**¶ -0.16*§ 0.32**††
0.87**# ALB‡ 1 0.61**†† 0.76**†† -0.54**†† -0.33**†† 0.91**§
0.83**# ALT 1 0.60**†† -0.35**†† -0.45**†† 0.70**§
102 ALM 1 0.94**†† -0.65**†† -0.42**††
ALTA 1 -0.61**†† -0.45**††
Aless 1 ns
*, ** Significance at P < 0.05, 0.01, respectively; ns not significant at P < 0.05 †Traits defined in Table 3.1 ‡Alternative pooled correlations were observed between the traits ALB-AAL. The lowest pooled r value is presented. §r from one environment; ¶r pooled from two environments; #r pooled from three environments; ††r pooled from four environments; ‡‡r pooled from six environments.
Table 3-3. Correlations coefficients between agronomic traits Trait† DH PH DM NS Ld KSk KNd KS NNdISk GY 0.23**¶ 0.73**†† -0.27**‡‡ 0.55**¶ 0.17**¶ 0.30**†† -0.32**†† DH‡ 1 -0.29**†† 0.23**# -0.24**¶ 0.51**¶ -0.50**¶ -0.50**¶ -0.33**¶ -0.40**¶ -0.78**¶ 0.46**†† PH§ 1 0.26**†† -0.24**†† ns 0.16*¶ 0.25**†† ns 0.21**¶ 0.17**¶ -0.17**†† -0.33**¶ -0.18**# DM 1 -0.26**# 0.23**# 0.17*¶ -0.35**# 0.22**¶ 0.24**¶ 0.15*¶ -0.20**†† NS 1 -0.39**# 0.20**†† 0.20*¶ -0.22**# 0.53**‡‡ 0.28**¶ 0.16*¶ Ld 1 -0.24**# -0.20**¶ -0.51*¶ 0.21**# -0.69**¶ 0.33**†† KSk 1 0.28**# -0.76**‡‡ 0.33**‡‡ 0.70**¶ †† 0.51** ¶ #
103 KNd 1 -0.23** 0.26** 0.71**¶
KS 1 -0.19*¶ 0.58**#
NNdISk 1 -0.27**‡‡ *, ** Significance at P < 0.05, 0.01, respectively; ns not s ignificant at P < 0.05 † Traits defined in Table 3.1 ‡Alternative pooled correlations were observed between the traits DH-NS. The lowest pooled r value is presented. §Alternative pooled correlations were observed between the traits PH-LD. The lowest pooled r value is presented. ¶r from one environment; #r pooled from two environments; ††r pooled from three environments; ‡‡r pooled from four environments.
Table 3-4. Correlations coefficients between spike-related traits and agronomic traits Traits† Agronomic traits DH PH DM NS LD KSk KNd KS NNdISk GY 0.25**¶ -0.21**¶ 0.25**†† PP 0.43**§§ -0.26**¶ 0.28**†† -0.23**‡‡ -0.31**‡‡ 0.31**‡‡ -0.39**‡‡ -0.27**¶ 0.23**¶ -0.18**¶
PC 0.29**‡‡ ns 0.24**# -0.18**# 0.20*¶ ns -0.18**¶ 0.16*# ns -0.15**¶
-0.33**†† SL‡ 0.41**‡‡ 0.30**‡‡ 0.37**# -0.38**‡‡ 0.42**¶ -0.22**# -0.28**# 0.46**†† 0.30**†† -0.55**¶ †† †† ‡‡ # ‡‡ ¶ ‡‡ # 0.29** ‡‡ -0.45** Nd 0.62** ns 0.48** -0.39** 0.49** -0.57** -0.31** ¶ 0.58** ¶ -0.34** -0.73** -0.27**†† raits NdD§ 0.36**‡‡ -0.28**†† 0.27**# -0.21**# 0.22**¶ -0.53**‡‡ -0.37**¶ 0.42**‡‡ -0.27**‡‡ -0.53**¶
ALB 0.21**# -0.20**# 0.23**# 0.23**# ns 0.33**†† -0.29**¶ ns -0.30**# 0.16*#
relatedt -
104 ¶
0.16* ALT -0.25**†† -0.23**†† -0.18*¶ 0.36**‡‡ ns 0.41**‡‡ -0.27**¶ -0.34**‡‡ 0.29**‡‡ Spike -0.18*¶
ALM 0.16*¶ -0.19**# 0.18*¶ ns ns 0.43**‡‡ ns 0.15*¶ -0.34**‡‡ ns
AAL -0.18*¶ -0.21**†† 0.18**# 0.22**‡‡ -0.15*¶ 0.43**‡‡ ns ns -0.35**‡‡ 0.19**††
Aless -0.16*¶ ns -0.19**# ns ns ns -0.22**¶ -0.22**†† -0.18*¶ ns
¶ # §§ ‡‡ 0.22** ‡‡ †† 0.29** ‡‡ ‡‡ PSS 0.24** ns ns -0.21** ¶ -0.70** 0.25** ¶ 0.75** -0.32**
-0.17** -0.17**
*, ** Significance at P < 0.05, 0.01, respectively; ns not significant at P < 0.05 †Traits defined in Table 3.1. ‡Alternative pooled correlations were observed between the traits SL-GY. The lowest pooled r value is presented. §Alternative pooled correlations were observed between the traits NdD-KNd. The lowest pooled r value is presented. ¶r from one environment; #r pooled from two environments; ††r pooled from three environments; ‡‡r pooled from four environments; §§ r pooled from five environments.
The agronomic traits NS, PH, and DH had transgressive segregation in both directions of the parental means in all environments as well as when the data was pooled across environments (Appendix
Table B2 and Appendix Fig. B2). In Carrington 2009, Prosper 2009, and AE, the trait GY showed transgressive segregation toward the elite parent (WCB414), while in Carrington 2010 and Prosper 2010,
GY showed transgressive segregation in both directions of the parental means (Appendix Table B2). In
Carrington 2009 and 2010, Prosper 2010, and AE the traits DM, KS and KNd had transgressive segregation in both directions of the parental means (Appendix Table B2 and Appendix Fig. B2).
However, in Prosper 2009 these traits only showed transgressive segregation in direction of the elite line
(WBCB414) (Appendix Table B2). In Carrington 2011, Prosper 2010, and AE the trait Ld had transgressive segregation in both directions of the parental means (Appendix Table B2 and Appendix Fig.
B2); however, in Prosper 2009 the trait Ld only showed transgressive segregation in direction of the PI
(WBCB617), whereas in Prosper 2011 only showed transgressive segregation in direction of the elite line
(WBCB414) (Appendix Table B2). The trait KSk segregated transgresivelly in both directions of the parental means in Carrington 2010 and Prosper 2009 (Appendix Table B2); however, in Carrington 2009,
Prosper 2010 and AE, the trait KSk only showed transgressive segregation in direction of the elite line
(WBCB414) (Appendix Table B2 and Appendix Fig. B2).
3.4.2. QTL for spike size and pubescences
Composite interval mapping for SL resulted in the identification of three consistent QTL
(QSL.ndsu.1A.1, QSL.ndsu.2D and QSL.ndsu.4B) and three putative QTL (QSL.ndsu.1A.2,
QSL.ndsu.2B.2, and QSL.ndsu.4A) (Table 3-5, Fig 3-2). The QTL QSL.ndsu.4A explained more than 15%
PV and was considered as major QTL; while QSL.ndsu.1A.1and QSL.ndsu.4B explained up to14.90% of
PV, a close value to 15%, the threshold used for major QTL. The QTL QSL.ndsu.1A.2, QSL.ndsu.2B, and
QSL.ndsu.2D are minor QTL which explained between 5.1% to 9.8% of PV of SL. Depending of the environments, these QTL together explained 19.93 to 27.46 of PV. Alleles from the branched parent
(WCB617) were the only responsibly to increase the length of the spike in the population
The QTL analysis for Nd data resulted in the identification of three consistent QTL
(QNd.ndsu.1A.1; QNd.ndsu.1D; and QNd.ndsu.2D) and four putative QTL (QNd.ndsu.1A.2;
QNd.ndsu.2A, QNd.ndsu.4A, and QNd.ndsu.4B.2) (Table 3-5, Fig 3-2). QNd.ndsu.2D had major effect
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and explained up to 55.9% of PV for Nd. This QTL was observed in all the environments as well as AE.
The other six QTL associated to Nd (QNd.ndsu.1A.1, QNd.ndsu.1A.2, QNd.ndsu.1D, and
QNd.ndsu.4B.2) are minor QTL which explaining between 3.63- 6.65% of PV. These QTL explained
16.77-67.42% of PV for Nd in different environments. Additives effects observed in all the QTL associated to Nd demonstrated that WCB617 has alleles that increase the phenotypic values of this trait.
For NdD a total of five QTL including one consistent QTL (QNdD.ndsu.2D) were identified (Table
3-5, Fig 3-2) and together explained 20.5-47.0% of PV depending of the environments. The consistent
QTL QNdD.ndsu.2D had major effect and explained up to 35.1% of PV, while the remaining QTL had minor effects, with PVE ranging from 5.3 to 8.6% for NdD. Alleles from WCB617 were responsible for increased values of NdD at QNdD.ndsu.2D, QNdD.ndsu.5B, and QNdD.ndsu.7A.2; while alleles from
WCB614 at QNdD.ndsu.7A.1 and QNdD.ndsu.7B contributed to increased values of NdD.
Data analysis revealed a total of seven QTL located on six chromosomes for PC. These seven
QTL include one consistent QTL (QLPC.ndsu.4A) and six putative QTL (QLPC.ndsu.1B,
QLPC.ndsu.3B.1, QLPC.ndsu.3B.2, QLPC.ndsu.5B.1, QLPC.ndsu.5B.2 and QLPC.ndsu.6B.1) (Table 3-
5, Fig 3-2). None of the QTL explained more than 15% of PV, therefore these genetic regions were considered to harbor minor QTL. However, the QTL located on chromosome 4A (QLPC.ndsu.4A) was identified in five environments as well as AE and was the only QTL for PC where alleles from the elite parent (WCB414) contributed to the increase of expression of this trait. Together, the QTL associated to
PC explained 13.4-27.5% of PV in different environments.
A total of seven QTL located on four chromosomes (1A, 4A, 5B, and 6B) (Table 3-5, Fig. 3-2) were identified for variation in PP and together explained 60.4-95.0% of PV in different environments. The
QTL QPP.ndsu.1A.1 showed major effect and explained 56.8- 91.8% of PV for PP. The PV explained by other six minor QTL ranged from 0.6% to 5.3%. Two of the QTL (QLPP.ndsu.5B, and QPP.ndsu.6B.1) were consistent QTL (Table 3-5). Alleles that increased PP at QPP.ndsu.1A.1, QPP.ndsu.1A.3,
QPP.ndsu.4A, QLPP.ndsu.5B, and QPP.ndsu.6B.2 loci were derived from the branched and pubescent parent WCB617; while at QLPP.ndsu.1A.2, and QLPP.ndsu.6B.1, alleles from the glabrous parent
WCB414 increased PP.
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3.4.3. Awn-related QTL
QTL analysis for ALB resulted in the detection of two consistent QTL (QALB.ndsu.1B and
QALB.ndsu.7B) and five putative QTL (QALB.ndsu.1D, QALB.ndsu.2A, QALB.ndsu.3A, QALB.ndsu.4A, and QALB.ndsu.6A) (Table 3-5, Fig 3-2). QALB.ndsu.2A and QALB.ndsu4A were major QTL and explained more than 15% of PV in some environments. While the remaining QTL, each contributed less than 15% of PV for ALB. Together these QTL explained 31.5 38.3% of PV in the different environments studied. Alleles from the branched parent (WCB617) increased ALB values at QALB.ndsu.1B,
QALB.ndsu.1D, QALB.ndsu.2A, and QALB.ndsu.4A; while alleles from the elite parent (WCB414) contributed to increase ALB values at QALB.ndsu.3A, QALB.ndsu.6A, and QALB.ndsu.7B loci.
A total of three consistent QTL (QALT.ndsu.1A, QALT.ndsu.2D, and QALT.ndsu.5B) and four putative QTL (QALT.ndsu.2A, QALT.ndsu.5A, QALT.ndsu.6A, and QALT.ndsu.7B) were associated to
ALT (Table 3-5, Fig 3-2). The PV explained by all these QTL varied from 15.8% to 61.4% in different environments. The QTL QALT.ndsu.1A, QALT.ndsu.2A and QALT.ndsu.2D had major effect and explained up to 17.1%, 15.5% and 20% of phenotypic variation, respectively. The PVE by other minor
QTL ranged from 6.5% to 12.3%. Only the elite parent (WCB14) contributed alleles for increased ALT values at each of these QTL
The QTL analysis for ALM in the RIL population studied resulted in the detection of one consistent QTL (QALM.ndsu.5B) and five putative QTL (QALM.ndsu.4B QALM.ndsu.5A, QALM.ndsu.6A,
QALM.ndsu.6B.1, QALM.ndsu.6B.2, QALM.ndsu.7B.1 and QALM.ndsu.7B.2) (Table 3-5, Fig 3-2). Only
QALM.ndsu.5B explained more than 15% of PV in one of the environment and can be considered as major QTL. The PVE by other QTL ranged from 6.1% to 10.6%. Together, these QTL explained 6.1-
40.9% of PV in different environments. The branched parent (WCB617) contributed the alleles that increase the phenotypic values of ALM at QALM.ndsu.4B and QALM.ndsu.6B.2, while WCB414 contributed towards increased phenotypic values of this trait at QALM.ndsu.5A, QALM.ndsu.5B,
QALM.ndsu.6A, QALM.ndsu.6B.1, QALM.ndsu.7B.1 and QALM.ndsu.7B.2.
The genetic analysis for AAL resulted in the detection of two consistent QTL (QAAL.ndsu.5B1 and QAAL.ndsu.7B.3) and two putative QTL (QAAL.ndsu.5B.2 and QAAL.ndsu.6A,) (Table 3-5, Fig 3-2).
The PVE explained for these QTL ranged from 6.1% to 13.3%. However, these QTL explained together
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8.0-20.2% of PV in different environments and the elite parent (WCB414) contributed positive alleles for increased phenotypic values at all AAL loci.
Composite interval mapping of data for Aless revealed two QTL, both located on chromosome 2A
(QAless.ndsu.2A.1 and QAless.ndsu.2A.2) (Table 3-5, Fig 3-2). The PVE by these QTL ranged from
22.4% to 33.4% and together explained 55.6-57.0% of PV in different environments. Additive effects of these QTL showed that the alleles that produced the apical awnleted phenotype were derived from the elite parent (WCB414).
3.4.4 .QTL for yield components
The CIM analysis for KSk resulted in the detection of three consistent (QKSk.ndsu.2B,
QKSk.ndsu.2D, and QKSk.ndsu.6B) and two putative QTL (QKSk.ndsu.1B and QKSk.ndsu.7B) (Table3-
6. Fig. 3-2). Only QKSk.ndsu.2D is a major QTL that explain between 21.5% and 38.8% of PV. The other
QTL had minor effects and explain 3.14% to 13.48% of PV. However, these QTL explained together 34.7-
48.6% of PV in different environments. The elite parent (WCB414) contributed the positive alleles to increase KSk at all the loci except QKSk.ndsu.7B.
A total of eight QTL were detected for the trait KNd, one of which was consistent (QKNd.ndsu.1A) and seven were putative (QKNd.ndsu.1D, QKNd.ndsu.2B, QKNd.ndsu.3A, QKNd.ndsu.4A,
QKNd.ndsu.5B, QKNd.ndsu.6B, and QKNd.ndsu.7A) (Table 3-6, Figure 3-2). All these QTL were minor
QTL explaining between 6.93% and 13.09% of PV. Depending of the environment, these QTL together explained 7.1-20.2 of PV of KNd. Additive effects showed that the branched parent (WCB617) alleles contributes the positively to increase KNd at QKNd.ndsu.1A, QKNd.ndsu.4A, and QKNd.ndsu.6B, while the elite parent (WCB414) contributed the positive alleles for increased trait values at QKNd.ndsu.1D,
QKNd.ndsu.2B, QKNd.ndsu.3A, QKNd.ndsu.5B, and QKNd.ndsu.7A loci.
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Table 3-5. QTL identified for spike-related traits in a RIL population of hexaploid wheat derived from the cross of WCB414 and WCB617 QTL Environment† Flanking Markers Pos‡. (cM) CI§(cM) LOD Thresh.¶ a# R2(%) Spike Length
QSL.ndsu.1A.1 I, II, IV, AE wPt-664586-wPt-667180 14.0-26.6 7.5-33.2 4.0-7.9 3.1-3.3 -0.3_-0.3 7.2-14.9 QSL.ndsu.1A.2 IV wPt-733588-wPt-665613 95.9 85.7-109.9 4.7 3.2 -0.3 8.0 QSL.ndsu.2B IV wPt-7859-wPt-1133 2.0 0-12.2 5.4 3.2 -0.3 9.8 QSL.ndsu.2D I, III wPt-730568-wPt-667536 74.8-82.8 62.6-93.9 2.7-2.8 3.1 -0.2_-0.3 5.1-5.5 QSL.ndsu.4A III wPt-4645-wPt-0798 83.7 72.7-94.4 5.1 3.1 -0.4 17.8 QSL.ndsu.4B I, II, AE wPt-74434-wPt-1101 31.0 23.9-37.2 3.4-7.3 3.1-3.3 -0.2_-0.3 15.0 ††RTPVE 19.9-27.5
Number of Nodes
QNd.ndsu.1A.1 I, III wPt-664586-wPt-664772 14.0-39.0 9.5-54.5 2.6-4.3 3.32-3.40 -0.6_-0.69 4.6-6.7 QNd.ndsu.1A.2 IV, AE wPt-8172-wPt-9429 106.9-113.9 79.4-127.3 4.4-4.6 3.28-3.36 -0.5_-0.51 5.8-6.1 QNd.ndsu.1D I, III, AE wPt-730475-wPt-665360 131.9 105.4-146.2 2.8-3.9 3.28-3.40 -0.4_-0.62 3.0-5.1
109 QNd.ndsu.2A II wPt-2372-wPt-2850 48.2 39.8-60.6 3.5 3.20 -0.6 5.2
QNd.ndsu.2D I, II, III, IV, AE wPt-671859-wPt-671914 66.4-74.8 66.0-78.0 6.5-37.5 3.20-3.40 -1.2_-1.7 8.6-56.0 QNd.ndsu.4A IV wPt-2946-tPt-9400 6.9 0-18 4.3 3.36 -0.4 4.1 QNd.ndsu.4B III, AE wPt-8892_wPt-1101 29.6-31.0 11.8-37.6 2.9-3.9 3.28-3.32 -0.4_-0.6 3.6-4.5 ††RTPVE 16.8-67.4
Nodes density QNdD.ndsu.2D I, II, III, IV, AE wPt -2675-wPt-666223 21.0 -48.3 10.1 -51.0 6.6 -16.6 3.14 -3.4 - 0.1_-0.1 20.5 -35.1 QNdD.ndsu.5B I wPt-1548-wPt-6191 133.3 117.0-152.7 3.8 3.23 -0.1 7.9 QNdD.ndsu.7A1 II wPt-1706-wPt-5524 40.9 35.3-41.2 4.0 3.28 0.1 6.6 QNdD.ndsu.7A2 II wPt-0971-wPt-2083 68.2 64.7-80.2 5.0 3.28 -0.1 8.6 QNdD.ndsu.7B III, AE wPt-6665428-wPt-5975 123.6-123.7 112.0-131.7 3.3-3.7 3.14 0.0-0.1 5.3-6.6 ††RTPVE 20.5-47.0 (Continues)
Table 3-5. QTL identified for spike-related traits in a RIL population of hexaploid wheat derived from the cross of WCB414 and WCB617 (continued) QTL Environment† Flanking Markers Pos. ‡(cM) CI§(cM) LOD Thresh.¶ a# R2(%) Penetrance of clavate architecture
QPC.ndsu.1B III, VI wPt-0308-wPt-8240 22.5 16.3-32.2 3.4-5.4 2.8-3.4 -0.2_-0.3 7.6-11.7 QPC.ndsu.3B.1 V wPt-6945-wPt-1159 108.9 99.1-124.0 3.0 2.4 -0.1 6.9 QPC.ndsu.3B.2 I, II, AE wPt-4412-wPt-1311 77.2 66.3-80.6 3.0-5.4 3.1-54.3 -0.2_-0.3 6.4-11.5 QPC.ndsu.4A I, II, III, IV, VI, AE wPt-0798-wPt-3250 90.0-124.2 79.7-127.1 3.5-4.3 2.8-54.3 0.2-0.3 7.0-9.6 QPC.ndsu.5B.1 IV wPt-6191-wPt-6014 155.8 135.7-182.4 3.4 3.4 -0.2 10.3 QPC.ndsu.5B.2 V wPt-0295-wPt-7665 221.7 212.0-227.9 4.4 2.4 -0.2 10.1 QPC.ndsu.6B.1 IV, V wPt-6116-wPt-1541 0.0-1.0 0-17.2 3.4-3.8 2.4-3.4 -0.1_-0.2 7.6-9.1 RTPVE†† 13.4-27.5
Penetrance of pubescences
QPP.ndsu.1A.1 I, II, III, IV, V, VI, AE wP1924-wPt-667180 18.4-23.6 17.0-25.4 51.6-134.4 4.0-84.8 -1.6_-1.9 56.8-91.8 QPP.ndsu.1A.2 V, VI wPt-3347-wPt-666424 2.6 0-3.5 2.6-3.7 4.3-84.8 0.3-0.4 0.8-1.2 QPP.ndsu.1A.3 V, VI wPt-665784-wPt-9429 98.0-108.9 95.2-119.2 3.2-4.0 4.3-84.8 -0.2 1.0-1.1
110 QPP.ndsu.4A IV wPt-7926-wPt-9833 62.1 48.3-81.2 6.2 3.7 -0.3 2.4 QPP.ndsu.5B I, V, VI rPt6127-tPt8942 22.8-27.5 16.7-38.2 2.5-4.1 4.3-84.8 -0.2_-0.4 0.7-5.3 QPP.ndsu.6B.1 III, IV, V wPt-742929-wPt-7935 167.7-178.7 157.0-189.1 2.6-4.2 3.7-84.8 0.2-0.4 0.6-3.9 QPP.ndsu.6B.2 VI, AE wPt-1784-wPt-4706 5.0-13.5 0-17.6 2.6-5.1 4.1-4.3 -0.2_-0.4 0.7-2.3 RTPVE†† 60.4-95.0
Awn length at the bottom of spike
QALB.ndsu.1B II, III, IV, AE wPt-671415-wPt-665480 1.5-10.9 0-14.6 2.9-4.1 3.1-3.2 -0.1_-0.3 5.7-9.3 QALB.ndsu.1D IV wPt-672077-wPt-730172 100.0 93.7-109.2 4.2 3.2 -0.3 8.6 QALB.ndsu.2A IV, AE wPt-798339-wPt-2372 41.0-43.0 34.1-52.6 2.7-4.5 3.1-3.2 -0.4_-0.4 7.9-16.8 QALB.ndsu.3A III wPt-667640-tPt-1079 35.9 22.0-43.4 3.9 3.1 0.2 9.7 QALB.ndsu.4A II wPt-4645-wPt-0798 80.7 66.6-96.2 4.3 3.1 -0.3 17.6 QALB.ndsu.6A IV, AE rPt9065-wPt-666574 58.1 54.3-71.1 3.1-4.2 3.1-3.2 0.2-0.3 6.5-8.6 QALB.ndsu.7B II, III,AE wPt-2305-wPt-6372 18.9-20.2 6.6-30.1 3.5-6.5 3.1-3.1 0.2-0.2 7.9-14.9 RTPVE†† 31.5-38.3
(Continues)
Table 3-5. QTL identified for spike-related traits in a RIL population of hexaploid wheat derived from the cross of WCB414 and WCB617 (continued) QTL Environment† Flanking Markers Pos‡.(cM) CI§ cM) LOD Thersh.¶ a# R2(%) Awn length at the top of spike
QALT.ndsu.1A I, II, III wPt-6654-wPt-3272 125.0-131.01 112.0-142.6 3.5-4.5 3.1-3.3 0.1-0.4 7.5-17.1 QALT.ndsu.2A I wPt-0003-wPt-4201 131.4 113.0-144.9 3.6 3.3 0.4 15.50 QALT.ndsu.2D I, II. III, IV, AE wPt-6850-wPt-667476 68.9-70.51 62.1-82.0 3.9-9.9 3.1-3.4 0.2-0.4 8.3-20.0 QALT.ndsu.5A IV, AE wPt-1038-wPt-1903 0.0 0-8.5 3.4-3.8 3.3-3.4 0.2-0.3 6.9 QALT.ndsu.5B I, II wPt-742141-wPt-3049 65.6 60.9-82.5 3.1-5.9 3.2-3.3 0.2-0.4 6.4-12.3 QALT.ndsu.6A IV wPt-666208-wPt-9131 83.7 65.2-94.3 3.6 3.3 0.3 7.1 QALT.ndsu.7B AE wPt-2305-wPt-9516 18.9 12.8-25.6 3.7 3.4 0.2 6.5 RTPVE††¶ 15.8-61.4
Awn length at middle of the spike
QALM.ndsu.4B IV, AE wPt-7412-wPt-5303 103.51 91.2-111.6 4.2-4.8 3.0-3.1 -0.4_-0.5 8.6-10.4 QALM.ndsu.5A IV wPt-1038-wPt-1903 0.01 0-5 5.0 3.0 0.5 10.6 QALM.ndsu.5B I, II, AE wPt-2041-wPt-3049 49.11-65.61 38.2-91.4 2.6-4.6 3.0 0.4-0.7 9.7-21.0
111 QALM.ndsu.6A IV wPt-2636-wPt-4836 1.51 0-6.7 3.3 3.0 0.3 6.6 QALM.ndsu.6B.1 IV wPt-0171-wPt-4164 7.51 5.6-11.9 3.5 3.0 0.4 7.8 QALM.ndsu.6B.2 II wPt-9990-wPt-5066 26.51 17.6-32.7 3.8 3.0 -0.4 8.5 QALM.ndsu.7B.1 III, AE wPt-2305-wPt-9516 18.91 9.7-33.4 2.9-3.0 3.1-3.2 0.3-0.4 6.1-6.3 QALM.ndsu.7B.2 IV wPt-2273-wPt-3723 52.11 38.9-73.7 3.2 3.0 0.4 7.3 RTPVE††¶ 6.1-40.9
Awn length total averaged
QAAL.ndsu.5B.1 I, II, AE wPt-742141-wPt-3049 65.61 61.7-89.4 2.9-5.1 3.0-3.3 0.2-0.4 6.2-11.9 QAAL.ndsu.5B.2 III wPt-1348-wPt-3995 280.91 267.7-299.8 3.6 3.2 0.3 13.3 QAAL.ndsu.6A IV, AE rPt9065-wPt-666574 58.11 46.7-72.5 2.8-3.7 3.2-3.3 0.2-0.3 6.1-8.0 QAAL.ndsu.7B II, III, AE wPt-2305-wPt-6372 18.91-20.21 9.9-32.5 3.2-3.8 3.0-3.3 0.2-0.2 6.9-8.3 RTPVE††¶ 8.0-20.2
(Continues)
Table 3-5. QTL identified for spike-related traits in a RIL population of hexaploid wheat derived from the cross of WCB414 and WCB617 (continued) QTL Environment† Flanking Markers Pos‡.(cM) CI§ (cM) LOD Thersh.¶ a# R2(%) Apical awnleted expression
QAless.ndsu.2A.1 I, II, III, IV, V, VI, AE wPt-798339-wPt-2372 39.0 35.8-44.9 9.8 2.3-2.5 0.1 33.2-33.4 QAless.ndsu.2A.2 I, II, III, IV, V, VI, AE wPt-668027-wPt-798459 30.5 26.4-32.7 2.8-3.3 2.3-2.5 0.1 22.4-23.6 RTPVE††¶ 55.6-57.0 †I, Prosper 2009; II, Carrington 2009; III, Prosper 2010; IV, Carrington 2010; V, Prosper 2011; VI, Carrington 2011. ‡Position §Confidence Interval ¶Thresold calculated by permutation test. #Additive effects ††rank of phenotypic variation explained per environment
112
1A-1 1A-2 1B-1
1B-2 1B-3 1D 1A-1 1A-2 1B-1
1D 0.0 wPt-730136 1A-1 1B-1 0.0 wPt-0966 1.6 wPt-3347 1.0 wPt-667500 3.1 wPt-666424 0.0 wPt-730136 0.0 wPt-741297 15.0 wPt-0413 wPt-4671 1.6 wPt-3347 3.8 wPt-8320 16.3 wPt-666832 wPt-668040 7.3 wPt-667458 3.1 wPt-666424 0.0 wPt-741297 21.0 wPt-9380 wPt-4666 wPt-6122 7.3 wPt-667458 4.5 3.8wPt-1560wPt-8320 49.6 wPt-743857 8.3 wPt-1560 wPt-4666 wPt-6122 22.5 4.5wPt-0308 50.6 wPt-742859 wPt-731412 8.3 22.5 wPt-0308 wPt-731412 wPt-743310 9.0 wPt-664586 9.0 wPt-664586 39.3 39.3wPt-8240wPt-8240 51.6 0.0 wPt-3950 0.0 wPt-3950 wPt-8744 wPt-0441 82.5 wPt-732602 15.4 wPt-1924 wPt-66948415.4 wPt-1924 wPt-669484 0.042.1wPt-8744wPt-6714151B.2(L)wPt-0441 1B.3 1D(S+L) 2.4wPt-734000 wPt-732520wPt-7343142.4 wPt-734314 42.1 wPt-744960 wPt-1726 wPt-733835 wPt-667287 wPt-734000 wPt-732520 wPt-664703 1.0 wPt-744960wPt-664989wPt-1726 83.5 20.6 wPt-666537 wPt-730618 7.9 46.8 wPt-740897 wPt-3743 7.9 wPt-6647038.9 tPt-6091 wPt-1786 wPt-665204 20.6 wPt-666537 wPt-730618 wPt-6709 wPt-3198 0.0 wPt-2389 46.8 1.547.4wPt-740897wPt-6012 100.0 wPt-672077 8.9wPt-667180 wPt-4177tPt-6091 wPt-178610.2 wPt-664593 2.048.0 wPt-665037wPt-3103 1A.1(S+L) QDM.ndsu.1A 1A.2(L) 27.4 1B.1(S)
QPP.ndsu.1A.2 wPt-3451 wPt-6012 wPt-6709 wPt-3198 1.115.6 wPt-1770 47.4 125.1 wPt-730172 33.4 wPt-734027 QPH.ndsu.1B.1 3.950.3 wPt-664735wPt-8682
QGY.ndsu.1A 10.2 wPt-6645935.5 wPt-1573 QDH.ndsu.1A QKS.ndsu.1A wPt-667180 wPt-4177 48.0 wPt-3103 125.8 wPt-669681
QDM.ndsu.1A 27.4 wPt-2597 QPP.ndsu.1A.2 QSL.ndsu.1A.1 57.4
wPt-7872 QPC.ndsu.1B QPP.ndsu.1A.1 34.0 15.6 wPt-177019.6 wPt-2315 14.7 wPt-665480 wPt-734132 wPt-664772 QPH.ndsu.1B.1 50.3 58.9wPt-8682wPt-3819 129.5
QNd.ndsu.1A.1 54.8 33.4 wPt-734027 QKS.ndsu.1B QGY.ndsu.1A 24.1 wPt-666986
wPt-1247 QALB.ndsu.1B
QDH.ndsu.1A 38.5 QKS.ndsu.1A wPt-1862 wPt-669118 QPH.ndsu.1B.2 61.7wPt-2597wPt-1684 130.9 wPt-3945
QSL.ndsu.1A.1 57.4
wPt-7872 QPC.ndsu.1B QPP.ndsu.1A.1 34.0 57.6 24.4 wPt-4971 wPt-4647 wPt-667558 wPt-743329 38.8 wPt-1818 65.3 wPt-5899 131.2 wPt-731020 wPt-732575 wPt-664772 58.9 29.2 wPt-3819wPt-6979wPt-1876 QNd.ndsu.1A.1 54.8 59.3 wPt-8749 43.3 rPt-7906 66.0 QLd.ndsu.1A wPt-4427 wPt-668079 wPt-1862 wPt-66911861.1 wPt-5776 62.9 wPt-6975 61.7 30.667.1wPt-1684wPt-730758wPt-731722wPt-730783 131.5 wPt-665749 57.6 67.7 wPt-4729 wPt-667558 wPt-74332966.1 wPt-729832 78.5 wPt-9809 65.3 wPt-5899 131.9 wPt-730475 76.5 wPt-3698 wPt-730408 76.1 wPt-1116 QSL.ndsu.1A.2 2A 84.3 wPt-50612B-1 66.0 77.1wPt-1876wPt-43432B-2 wPt-665360 wPt-4687 59.3QNd.ndsu.1A.2 wPt-8749 94.0 wPt-2872 QLd.ndsu.1A 132.8 QPP.ndsu.1A.3 85.4 wPt-5034 77.4 wPt-5740 wPt-671947 wPt-0966 QKNd.ndsu.1A.1 94.4 wPt-1906 0.0
QNdISk.ndsu.1A wPt-731722 QNS.ndsu.1A 61.1 wPt-5776 67.1 95.9 wPt-733588 97.3 rPt-9074 82.8 wPt-4811 QNS.ndsu.1D 133.1 wPt-729788
QALT.ndsu.1A wPt-667500 66.1 wPt-729832 QKSk.ndsu.1B 67.7 wPt-4729 1.0 wPt-665613 wPt-666269 133.4 wPt-8545 wPt-730718 15.0 wPt-0413 wPt-4671 76.5 wPt-3698 wPt-73040896.6 wPt-734301 76.1 wPt-1116 QALB.ndsu.1D 134.4 wPt-666963 wPt-666832 wPt-668040 QSL.ndsu.1A.2 16.3 97.0 wPt-665784 77.1 wPt-4343 QGY.ndsu.1D.1 QNd.ndsu.1A.2 94.0 wPt-2872 134.7 wPt-7057 21.0 wPt-9380 QPP.ndsu.1A.3 100.9 wPt-8172 wPt-5740 QPH.ndsu.1D
77.4 QKNd.ndsu.1D wPt-730605 wPt-7092 QNd.ndsu.1D
QKNd.ndsu.1A.1 94.4 wPt-1906 QNdISk.ndsu.1A 117.1 wPt-9429 wPt-743857 QNS.ndsu.1A 49.6 95.9 wPt-733588 123.0 wPt-6654 82.8 wPt-4811 wPt-7697 wPt-7957 50.6 wPt-742859 QALT.ndsu.1A 135.0 0.0 wPt-730136 150.3 wPt-3272 wPt-4988 wPt-665327
wPt-665613 wPt-666269 QNS.ndsu.1D 51.6 wPt-743310 1.6 wPt-3347 176.9 wPt-0432 QGY.ndsu.1D.2 wPt-4711 96.6 wPt-734301 82.5 wPt-732602 3.1 wPt-666424 177.2 0.0 wPt-4065wPt-741297 0.0 wPt-671415 wPt-7711 wPt-6503 191.9 wPt-5167 1B.2(L) 1B.3 1B-2 135.4 1D(S+L) wPt-733835 wPt-667287 7.3 wPt-667458 97.0 wPt-665784 3.8 wPt-8320 1.0 wPt-664989 wPt-1263 83.5 194.0 wPt-667814wPt-1560 wPt-3743 wPt-4666 wPt-6122 100.9 wPt-8172 4.5 wPt-7784 1.5 wPt-665204137.7 wPt-8866 8.3 113 200.1 0.0 wPt-2389 100.0 wPt-672077 wPt-731412 117.1 wPt-9429 210.922.5 wPt-4658wPt-0308 wPt-665037 wPt-5721 wPt-5915 1.1 wPt-3451 2.0 138.6 125.1 wPt-730172 9.0 wPt-664586 123.01A-2 wPt-6654 215.439.3 wPt-5316wPt-8240 3.9 wPt-664735 wPt-2968 0.0 wPt-3950 219.6 wPt-8016 5.5 wPt-1573 125.8 wPt-669681 wPt-8744 wPt-0441 140.8 wPt-730879 15.4 wPt-1924 wPt-669484 wPt-3272 42.1 14.7 wPt-665480 2.4150.3 wPt-734314 wPt-744960 wPt-1726 19.6 wPt-2315 146.1 wPt-1865 129.5 wPt-734132 wPt-734000 wPt-732520 QKS.ndsu.1B 24.1 wPt-666986
wPt-0432 38.5 wPt-1247 QALB.ndsu.1B
176.9 QPH.ndsu.1B.2 wPt-666537 wPt-730618 7.9 wPt-664703 46.8 wPt-740897 151.1 wPt-4497 130.9 wPt-3945 20.6 38.8 wPt-1818 24.4 wPt-4971 wPt-4647 wPt-6709 wPt-3198 8.9177.2 tPt-6091wPt-4065wPt-1786 47.4 wPt-6012 153.0 wPt-5253 131.2 wPt-731020 wPt-732575 43.3 rPt-7906 29.2 wPt-6979 wPt-667180 wPt-4177 10.2191.9 wPt-664593wPt-5167 48.0 wPt-3103 wPt-4427 wPt-668079 QDM.ndsu.1A 27.4
QPP.ndsu.1A.2 wPt-730758 wPt-730783 131.5 15.6 wPt-1770 62.9 wPt-6975 30.6 wPt-665749 33.4 wPt-734027 194.0 wPt-667814 QPH.ndsu.1B.1 50.3 wPt-8682 0.0 wPt-9190
QGY.ndsu.1A 78.5 wPt-9809 131.9 wPt-730475
QKS.ndsu.1A1 QSL.ndsu.1A1 QDH.ndsu.1A.1 57.4 wPt-2597
wPt-7872 wPt-7784 QPC.ndsu.1B QPP.ndsu.1A.1 34.0 200.1 84.37.8 wPt-5061tPt-4125 wPt-664772 58.9 wPt-3819 wPt-665360 wPt-4687 QNd.ndsu.1A.1 54.8 210.9 wPt-4658 85.412.0 wPt-5034wPt-665550 132.8 wPt-1862 wPt-669118 61.7 wPt-1684 0.0 wPt-0966 wPt-671947 57.6 215.4 wPt-5316 2A 97.313.0 rPt-9074wPt-7350 wPt-667500 1.0 QNS.ndsu.1D.1 133.1 wPt-729788 wPt-667558 wPt-743329 65.3 wPt-5899 QKSk.ndsu.1B 219.6 wPt-8016 0.0 wPt-7859 14.4 wPt-9336 wPt-0413 wPt-4671 59.3 wPt-8749 0.066.0 wPt-734106wPt-1876 15.0 133.4 wPt-8545 wPt-730718
QLd.ndsu.1A 3.0 wPt-1133 15.3 tPt-5438 QALB.ndsu.1D wPt-666832 wPt-668040 QGY.ndsu.1D 61.1 wPt-5776 11.567.1 wPt-5251wPt-731722 16.3 134.4 wPt-666963 23.3 wPt-9274 16.7 wPt-744181 wPt-9380 66.1 wPt-729832 19.567.7 wPt-668027wPt-4729 21.0 134.7 wPt-7057 30.7 wPt-0100 18.9 wPt-2397 wPt-743857 QPH.ndsu.1D
49.6 QKNd.ndsu.1D wPt-730605 wPt-7092 QNd.ndsu.1D
76.1 wPt-1116 QKNd.ndsu.2B 76.5 wPt-3698 wPt-730408 33.1 wPt-798459 QSL.ndsu.2B wPt-732386 wPt-0948
QSL.ndsu.1A.2 wPt-8760 77.1 wPt-4343 42.4 19.4 Consistent50.6 wPt-742859 QTL wPt-7697 wPt-7957 QNd.ndsu.1A.2 94.0 wPt-2872 35.0 wPt-798339 1B-3 135.0 QPP.ndsu.1A.3 45.6 wPt-6158 wPt-3565 wPt-741382 51.6 wPt-743310 wPt-4988 wPt-665327 77.4 wPt-5740 QGY.ndsu.1D
QKNd.ndsu.1A.1 94.4 wPt-1906 47.2 wPt-2372 wPt-10003
QKSk.ndsu.2B QNdISk.ndsu.2B2 46.6 wPt-1813 22.0 wPt-744808 QNS.ndsu.1D.2 QNS.ndsu.1A.1 wPt-732602 wPt-4711
QLd.ndsu.2A.2 82.5 QNdISk.ndsu.2B1
QNdImSk.ndsu.1A1 95.9 wPt-733588 82.8 wPt-4811 0.0 wPt-671415
QAless.ndsu.2A.2 QGY.ndsu.2A.1
QALT.ndsu.1A1 49.4 wPt-4368 wPt-665613 wPt-666269 wPt-733835 wPt-667287 wPt-7711 wPt-6503
QALB.ndsu.2A 1.0 wPt-664989 Putative83.5 QTL 135.4 QNS.ndsu.2A 96.6 QAless.ndsu.2A.1 73.7 wPt-2850 74.8 wPt-8404 50.2 wPt-2425 wPt-3743 wPt-1263 wPt-734301 QNNd.ndsu.2A 1.5 wPt-665204 0.0 wPt-2389 83.8 wPt-8398 51.7 wPt-5736 100.0 wPt-672077 137.7 wPt-8866 97.0 wPt-665784 94.7 wPt-8068 wPt-665037 1.1 wPt-3451 91.2 wPt-9070 2.0 52.5 wPt-0049 125.1 wPt-730172 wPt-5721 wPt-5915 100.9 wPt-8172 95.8 wPt-6711 wPt-0568 wPt-664735 138.6 5.5 wPt-1573 3.9 55.1 wPt-4917 SS125.8-RelatedwPt-669681 QTL wPt-2968 117.1 wPt-9429 wPt-740658 wPt-8490 14.7 wPt-665480 98.8 tPt-9405 19.6 wPt-2315 129.5 wPt-734132 140.8 wPt-730879 123.0 wPt-6654 QKS.ndsu.1B
QLd.ndsu.2A.1 24.1 wPt-666986
38.5 wPt-1247 QALB.ndsu.1B QPH.ndsu.1B.2 130.9 wPt-3945 146.1 wPt-1865 150.3 wPt-3272 QKS.ndsu.2A 108.5 wPt-664128 24.4 wPt-4971 wPt-4647 38.8 wPt-1818 131.2 wPt-731020 wPt-732575 151.1 wPt-4497 176.9 wPt-0432 QGY.ndsu.2A.2 122.1 wPt-7721 43.3 rPt-7906 29.2 wPt-6979 wPt-4427 wPt-668079 177.2 wPt-4065 122.4 wPt-0003 153.0 wPt-5253 62.9 wPt-6975 30.6 wPt-730758 wPt-730783 131.5 wPt-665749 191.9 wPt-5167 QALT.ndsu.2A 149.8 wPt-4201 78.5 wPt-9809 wPt-730475 194.0 wPt-667814 131.9 QNNdISk.ndsu.2A 84.3 wPt-5061 wPt-665360 wPt-4687 200.1 wPt-7784 85.4 wPt-5034 132.8 wPt-671947 210.9 wPt-4658 97.3 rPt-9074
QNS.ndsu.1D.1 133.1 wPt-729788 215.4 wPt-5316 QKSk.ndsu.1B wPt-8545 wPt-730718
219.6 wPt-8016Fig. 3-2. Genetic map and QTL for 10 spike-related, and 10 agronomic traits of a RIL population derived from the cross of133.4 the elite line WCB414 QALB.ndsu.1D QGY.ndsu.1D 134.4 wPt-666963
and the exotic lineWCB617 with SS trait, over four to six locations in ND, USA during 2009, 2010, and 2011 134.7 wPt-7057 QPH.ndsu.1D
QKNd.ndsu.1D wPt-730605 wPt-7092 QNd.ndsu.1D wPt-7697 wPt-7957
135.0 wPt-4988 wPt-665327 QGY.ndsu.1D
QNS.ndsu.1D.2 wPt-4711 wPt-7711 wPt-6503 135.4 wPt-1263 137.7 wPt-8866 wPt-5721 wPt-5915 138.6 wPt-2968 140.8 wPt-730879 146.1 wPt-1865 151.1 wPt-4497 153.0 wPt-5253 2A(S+L) 2B.1(L) 2B.2(S)
3A.3(S+L) 3B.1(1L) 3B.2(S+L)
0.0 wPt-9190 2D 7.8 2A(S+L)tPt-4125 2B.1(L) 2B.2(S) 0.0 wPt-7225 12.0 wPt-665550 2B-1 3A-1 0.4 wPt-8446 13.0 wPt-7350 2D 3A-1wPt-741078 1.4 wPt-1620 14.4 wPt-9336 0.0 wPt-7859 0.0 0.0 wPt-734106 2.4 wPt-671711 wPt-741848 3.8 wPt-744251 15.3 tPt-5438 3.0 wPt-1133 wPt-2675 11.5 wPt-5251 0.0 3.0 wPt-743858 5.7 wPt-3260 16.7 wPt-744181 23.3 wPt-9274 27.4 wPt-8134 19.5 wPt-668027 3.6 wPt-741816 7.8 wPt-10972
18.9 wPt-2397 30.7 wPt-0100 36.2 wPt-2761 18.2 wPt-666139 QKNd.ndsu.2B
33.1 wPt-798459 QSL.ndsu.2B wPt-741029 wPt-743909 wPt-732386 wPt-0948 42.4 wPt-8760 38.6 6.1 35.0 wPt-798339 19.4 wPt-3677 wPt-5683 wPt-742486 30.7 wPt-741331 wPt-741413 wPt-741382 45.6 wPt-6158 wPt-3565 6.7 0.0 wPt-8096 wPt-5906 47.2 wPt-2372 wPt-10003 39.7 wPt-663767 wPt-732866 8.1 wPt-742039 32.3 wPt-0267 wPt-741750
QKSk.ndsu.2B 22.0 wPt-744808 46.6 wPt-1813 wPt-734114 wPt-740855 8.8 wPt-5769 QLd.ndsu.2A.2 QNNdISk.ndsu.2B2 33.9 wPt-5916
QNNdISk.ndsu.2B1 8.7 wPt-742665 QAless.ndsu.2A.2 QGY.ndsu.2A.1 49.4 wPt-4368 40.3 wPt-732705 37.3 wPt-3536 wPt-0302
QALB.ndsu.2A 40.9 wPt-671410 9.2 wPt-741986 wPt-742118 QNS.ndsu.2A QAless.ndsu.2A.1 wPt-8404 73.7 wPt-2850 50.2 wPt-2425 74.8 QGY.ndsu.3A
QNNd.ndsu.2A wPt-4842 41.5 wPt-5574 13.5 wPt-9303 34.6 wPt-7301 43.7 94.7 wPt-8068 51.7 wPt-5736 83.8 wPt-8398 44.0 tPt-6487 41.8 wPt-667785 QKNd.ndsu.3A wPt-0049 91.2 wPt-9070 13.9 wPt-740730 wPt-6711 wPt-0568 52.5 QLd.ndsu.3A 95.8 wPt-3144 wPt-664967 35.9 wPt-667640 wPt-2299 wPt-0013 wPt-740658 wPt-8490 55.1 wPt-4917 wPt-9802 wPt-730529 QALB.ndsu.3A wPt-732330 wPt-733544 98.8 36.3 tPt-1079 tPt-9405 wPt-730057 wPt-6609 69.6 wPt-2766 wPt-0895 QLd.ndsu.2A.1 wPt-7243 wPt-6096 48.2 wPt-1464
QKS.ndsu.2A wPt-664128 42.1 108.5 wPt-671455 wPt-0936 48.9 wPt-9928 wPt-2298
QGY.ndsu.2A.2 wPt-7721 122.1 wPt-4093 wPt-742673 50.40.0 wPt-9634wPt-741078 95.2 wPt-6216 wPt-0003 2B-2 wPt-2186 wPt-6342
122.4 2D(S+L) 3A.1(1L) 3A.2(2L) 57.12.4 wPt-664488wPt-671711 wPt-741848 96.3 wPt-9579 QNS.ndsu.3A QALT.ndsu.2A wPt-4201 0.0 wPt-9190 wPt-2294 3.0 wPt-743858 149.8 59.9 wPt-7217 96.7 wPt-2936 7.8 tPt-4125 42.4 wPt-7149 wPt-664901 3.6 wPt-741816 98.9 wPt-1191 QNNdISk.ndsu.2A wPt-1655 wPt-2938 0.0 wPt-2675 wPt-665550 43.4 wPt-667843 79.86.1 wPt-743909 12.0 43.7 wPt-4144 wPt-666857 6.7 wPt-742486 108.9 wPt-6945 wPt-8134 86.4 wPt-7890 QPC.ndsu.3B.1 27.4 13.0 wPt-7350 44.1 wPt-740836 QDH.ndsu.3A 8.1 wPt-742039 114.8 wPt-1159 wPt-10130
36.2 wPt-2761 QDM.ndsu.3A1 87.1 wPt-1353 14.4 wPt-9336 wPt-7859 45.5 wPt-3812 8.7 wPt-742665 wPt-741029 0.0 wPt-6854 wPt-9369 124.1 wPt-732044 38.6wPt-734106 QNS.ndsu.3B 0.0 15.3 tPt-5438 wPt-1133 47.3 wPt-667294 96.59.2 wPt-741986 wPt-742118 3.0 QGY.ndsu.3A tPt-1759 wPt-6973 11.5 wPt-5251wPt-3677 wPt-5683 wPt-666223 wPt-7992 16.7 wPt-744181 23.3 wPt-9274 48.8 13.5 wPt-9303 124.8 tPt-1366 19.5 39.7wPt-668027wPt-663767 wPt-732866 49.5 wPt-741084 QKNd.ndsu.3A 119.213.9 tPt-0242wPt-740730 wPt-2397 QLd.ndsu.3A 18.9 30.7 wPt-0100 QDM.ndsu.3A.2 125.3 wPt-7015
QNdD.ndsu.2D 50.1 wPt-666656 wPt-0085 35.9 wPt-667640 QALB.ndsu.3A wPt-734114 wPt-740855 QKNd.ndsu.2B 148.4 wPt-0286 33.1 wPt-798459 QSL.ndsu.2B wPt-732386 wPt-0948 QSS.ndsu-2D 42.4 wPt-8760QNdISk.ndsu.2D 50.6 wPt-2652 wPt-6259 tPt-1079 125.8 wPt-9170 wPt-664393 35.0 40.3wPt-798339wPt-732705 19.4 149.436.3 wPt-1562 wPt-3816 wPt-741382 45.6 QDH.ndsu.2D wPt-6158 wPt-3565 51.1 wPt-667266 48.2 wPt-1464 134.3 wPt-743661 wPt-11032 47.2 40.9wPt-2372wPt-671410wPt-10003 165.2 wPt-2740
QKSk.ndsu.2B wPt-0650 wPt-9928
QKSk.ndsu.2D 52.1
22.0 wPt-744808 QPH.ndsu.2D 46.6 wPt-1813 48.9 134.6 rPt-5396 QLd.ndsu.2A.2 2D(S+L) 3A.1(1L) 3A.2(2L) QNNdISk.ndsu.2B2 QNd.ndsu.2D wPt-744743
wPt-5574 QNNdISk.ndsu.2B1 165.9
41.5 QALT.ndsu.2D 53.0 wPt-741302 50.4 wPt-9634
QAless.ndsu.2A.2 QSL.ndsu.2D QGY.ndsu.2A.1 49.4 wPt-4368
56.3 wPt-667671 57.1 wPt-664488 148.1 wPt-731910 wPt-4364
QNS.ndsu.3A QALB.ndsu.2A
41.8 wPt-667785 QGY.ndsu.2D.1 QNS.ndsu.2A QAless.ndsu.2A.1 73.7 wPt-2850 50.2 wPt-2425 74.8 wPt-8404
QNNd.ndsu.2A wPt-2675 59.4 wPt-731489 59.9 wPt-7217 0.0 wPt-3144 wPt-664967 83.8 wPt-8398 wPt-813494.7 wPt-8068 51.7 wPt-5736 63.1 wPt-731406 79.8 wPt-1655 wPt-2938 27.4 QNS.ndsu.2D 114 wPt-9802 wPt-730529 91.2 wPt-9070 wPt-664714 wPt-7890
wPt-276195.8 wPt-6711 wPt-0568 52.5 wPt-0049 65.0 86.4 QDH.ndsu.3A 36.2 wPt-730057 wPt-6609 QKS.ndsu.2D wPt-9900 55.1 wPt-4917 65.7 QDM.ndsu.3A1 87.1 wPt-1353 wPt-740658 wPt-8490 QGY.ndsu.2D.2 38.6 wPt-741029 wPt-7243 wPt-6096 66.1 wPt-733889 wPt-732277 wPt-6854 wPt-9369 98.8 96.5 wPt-3677 wPt-568342.1tPt-9405 66.4 wPt-671859 wPt-7992 QLd.ndsu.2A.1 wPt-671455 wPt-0936
QKS.ndsu.2A 39.7 wPt-663767108.5 wPt-732866wPt-664128wPt-4093 wPt-742673 66.7 wPt-667406 wPt-664745 119.2 tPt-0242 wPt-671737 wPt-671892 QDM.ndsu.3A.2 QGY.ndsu.2A.2 wPt-734114122.1 wPt-740855wPt-7721 67.0 148.4 wPt-0286 wPt-2186 wPt-6342 wPt-668120 wPt-6704 149.4 wPt-1562 wPt-3816 122.4 wPt-0003 68.6 40.3 wPt-732705 wPt-2294 wPt-1554 165.2 wPt-2740 QALT.ndsu.2A 149.8 wPt-4201 40.9 wPt-671410 42.4 wPt-7149 wPt-664901 3A-2 68.9 wPt-6850 165.9 wPt-744743 wPt-731134 QNNdISk.ndsu.2A 41.5 wPt-5574 43.4 wPt-667843 70.5 0.0 wPt-1681 wPt-667476 41.8 wPt-667785 43.7 wPt-4144 wPt-666857 0.0 tPt-7492 72.1 1.3 wPt-2755 73.4 wPt-733932 wPt-3144 wPt-66496744.1 wPt-740836 1.7 wPt-730156 2.7 wPt-7608 74.0 wPt-733033 wPt-9802 wPt-73052945.5 wPt-3812 74.5 wPt-732509 wPt-671669 wPt-730057 wPt-660947.3 wPt-667294 28.7 wPt-1339 74.8 wPt-730568 wPt-7243 wPt-6096 75.1 wPt-671914 wPt-667124 42.1 48.8 wPt-666223 wPt-671455 wPt-093649.5 wPt-741084 76.8 wPt-5014 wPt-4093 wPt-742673 54.5 wPt-9160 wPt-0398 79.3 wPt-668017
QNdD.ndsu.2D 50.1 wPt-666656 wPt-0085 59.0 wPt-5263 82.8 wPt-671623 wPt-2186QSS.ndsu-2D wPt-634250.6 wPt-2652 wPt-6259 87.7 wPt-667536 QNdImSk.ndsu.2D wPt-2294 wPt-4242 QDH.ndsu.2D 51.1 wPt-667266 90.5 42.4 wPt-7149 wPt-664901 wPt-0650 94.5 wPt-730613
QKSk.ndsu.2D 52.1 QPH.ndsu.2D 3A-3 98.3 wPt-732270 Consistent QTL 43.4 QNd.ndsu.2D wPt-667843 QALT.ndsu.2D 53.0 wPt-741302 QSL.ndsu.2D 0.0 wPt-1681 105.4 wPt-730427 43.7 wPt-4144 wPt-66685756.3 wPt-667671 0.0 tPt-7492 QGY.ndsu.2D.1 1.3 wPt-2755 wPt-667485 wPt-6064 44.1 wPt-740836 59.4 wPt-731489 1.7 wPt-730156 107.8 wPt-741208 2.7 wPt-7608 45.5 wPt-3812 63.1 wPt-731406 109.0 wPt-732923 Putative QTL QNS.ndsu.2D 47.3 wPt-667294 65.0 wPt-664714 wPt-1339 wPt-665836 wPt-732052 28.7 109.8 wPt-0153 wPt-667765 QKS.ndsu.2D wPt-666223 48.8 65.7 wPt-9900 wPt-4329 wPt-7825 QGY.ndsu.2D.2 49.5 wPt-741084 66.1 wPt-733889 wPt-732277 54.5 wPt-9160 wPt-0398 110.2 wPt-5106 SS-Related QTL
QNdD.ndsu.2D 50.1 wPt-666656 wPt-0085 66.4 wPt-671859 59.0 wPt-5263 111.2 wPt-733725 QSS.ndsu-2D 50.6 wPt-2652 wPt-625966.7 wPt-667406 wPt-664745 111.6 wPt-6514 QNdImSk.ndsu.2D 114.0 wPt-3757 QDH.ndsu.2D 51.1 wPt-667266 67.0 wPt-671737 wPt-671892 wPt-0650 115.8 wPt-9848
QKSk.ndsu.2D 52.1 QPH.ndsu.2D wPt-668120 wPt-6704
68.6 125.7 wPt-3692 wPt-666518 QNd.ndsu.2D QALT.ndsu.2D 53.0 wPt-741302 wPt-1554 131.9 wPt-6780 QSL.ndsu.2D 56.3 wPt-667671 wPt-6850 QGY.ndsu.2D.1 68.9 59.4 wPt-731489 70.5 wPt-731134 63.1 wPt-731406 72.1 wPt-667476
QNS.ndsu.2D Fig. 3-2. Genetic map and QTL for 10 spike-related, and 10 agronomic traits of a RIL population derived from the cross of the elite line WCB414 65.0 wPt-664714 73.4 wPt-733932 QKS.ndsu.2D 65.7 wPt-9900 74.0 wPt-733033and the exotic lineWCB617 with SS trait, over four to six locations in ND, USA during 2009, 2010, and 2011 (Continued) QGY.ndsu.2D.2 66.1 wPt-733889 wPt-73227774.5 wPt-732509 wPt-671669 66.4 wPt-671859 74.8 wPt-730568 66.7 wPt-667406 wPt-66474575.1 wPt-671914 wPt-667124 67.0 wPt-671737 wPt-67189276.8 wPt-5014 wPt-668120 wPt-6704 68.6 79.3 wPt-668017 wPt-1554 82.8 wPt-671623 68.9 wPt-6850 87.7 wPt-667536 70.5 wPt-731134 90.5 wPt-4242 72.1 wPt-667476 94.5 wPt-730613 73.4 wPt-733932 98.3 wPt-732270 74.0 wPt-733033105.4 wPt-730427 74.5 wPt-732509 wPt-671669 wPt-667485 wPt-6064 74.8 wPt-730568107.8 wPt-741208 75.1 wPt-671914109.0wPt-667124 wPt-732923 76.8 wPt-5014 wPt-665836 wPt-732052 79.3 wPt-668017109.8 wPt-0153 wPt-667765 82.8 wPt-671623 wPt-4329 wPt-7825 87.7 wPt-667536110.2 wPt-5106 90.5 wPt-4242 111.2 wPt-733725 94.5 wPt-730613111.6 wPt-6514 98.3 wPt-732270114.0 wPt-3757 105.4 wPt-730427115.8 wPt-9848 wPt-667485 wPt-6064 107.8 125.7 wPt-3692 wPt-666518 wPt-741208131.9 wPt-6780 109.0 wPt-732923 wPt-665836 wPt-732052 109.8 wPt-0153 wPt-667765 wPt-4329 wPt-7825 110.2 wPt-5106 111.2 wPt-733725 111.6 wPt-6514 114.0 wPt-3757 115.8 wPt-9848 125.7 wPt-3692 wPt-666518 131.9 wPt-6780 3A.3(S+L) 3B.1(1L) 3B.2(S+L)
4A 4B-1 4B-2
3B.3(2L) 3D.1(L) 3D.2(S)
4A 3B-1 3D-1 0.0 wPt-7225 0.0 wPt-740945 0.0 wPt-2247 0.4 wPt-8446 1.3 rPt-1806 wPt-0965 3.1 wPt-8841 wPt-741078 1.4 wPt-1620 wPt-740590 wPt-741484 4.9 wPt-2946 0.0 2.6 2.4 wPt-671711 wPt-741848 3.8 wPt-744251 wPt-729938 wPt-741190 11.6 tPt-9400 3.0 wPt-743858 5.7 wPt-3260 2.9 wPt-742255 18.4 wPt-743165 3.6 wPt-741816 7.8 wPt-10972 3.2 wPt-730115 wPt-741102 19.8 wPt-0817 6.1 wPt-743909 18.2 wPt-666139 3.8 wPt-733405 wPt-740976 21.6 rPt-7285 30.7 wPt-741331 wPt-741413 22.7 wPt-6728 6.7 wPt-742486 wPt-8096 wPt-5906 6.1 wPt-742264 0.0 32.3 wPt-0267 wPt-741750 23.3 wPt-744614 8.1 wPt-742039 wPt-5769 7.7 wPt-741252 wPt-743515 8.8 33.9 wPt-5916 23.6 wPt-3810 8.7 wPt-742665 0.0 wPt-1615 wPt-2858 8.1 wPt-9810 37.3 wPt-3536 wPt-0302 wPt-1091 wPt-744595 wPt-4280 9.2 wPt-741986 wPt-742118 wPt-9586 9.0 wPt-5163 24.9 0.0 QGY.ndsu.3A 4.8 13.5 wPt-9303 34.6 wPt-7301 43.7 wPt-4842 9.6 wPt-741500 26.9 wPt-5951 wPt-1272 8.1 wPt-741584 44.0 tPt-6487 0.0 tPt-0602 QKNd.ndsu.3A 30.3 wPt-5857 2.3 wPt-734310 wPt-732448 13.9 wPt-740730 8.6 wPt-669355 wPt-741201 10.6 wPt-734051 wPt-732423
QLd.ndsu.3A 12.0 35.9 wPt-667640 wPt-2299 wPt-0013 wPt-742530 wPt-733412 42.1 wPt-7926 5.5 wPt-5559 QALB.ndsu.3A 12.6 wPt-7739 23.6 wPt-8892 wPt-732330 wPt-733544 wPt-742329 wPt-742443 62.4 wPt-9833 6.8 wPt-1046 36.3 tPt-1079 18.0 wPt-667895 69.6 31.0 wPt-744434 wPt-1464 wPt-2766 wPt-0895 wPt-742171 wPt-741733 62.7 wPt-4645 QNS.ndsu.4B
48.2 QKS.ndsu.4A wPt-6834 QNNd.ndsu.4A 40.1 QKNd.ndsu.4A 42.9 wPt-1101 wPt-6016 48.9 wPt-9928 3B-30.0 wPt-2298wPt-731103 3D-1 wPt-740619 wPt-741216 3D-290.0 wPt-0798
47.6 wPt-0912 QLd.ndsu.4B 10.9 QNd.ndsu.4B 53.8 wPt-6209
95.2 0.8 wPt-6216wPt-667148 QPH.ndsu.4B wPt-9634 wPt-741037 wPt-6965 100.2 wPt-0150 QSL.ndsu.4B 50.4 54.8 wPt-8959 58.6 wPt-9067 57.1 wPt-664488 96.3 2.0 wPt-9579wPt-0732 wPt-2464 wPt-9335 wPt-742056 wPt-2951 QNS.ndsu.3A 56.1 wPt-6856 wPt-10349 4A(S+L) 4B.1(S) 4B.2(S+L) 59.7 wPt-7062 59.9 wPt-7217 96.7 3.6 wPt-2936wPt-741510 wPt-733983 wPt-742148 wPt-740873 103.9 wPt-744256 wPt-664749 56.4 wPt-3342 98.9 3.9 wPt-1191wPt-742339 wPt-2814 4A 4B-1 4B-2 70.7 wPt-2214 79.8 wPt-1655 wPt-2938 rPt-0996 wPt-2119 wPt-741495 wPt-742463 wPt-672107 59.4 QPP.ndsu.4A 86.4 wPt-744934 QLd.ndsu.3D 108.9 22.1 wPt-6945wPt-740580 wPt-741157
86.4 wPt-7890 60.1 wPt-731500 QPC.ndsu.3B.1 11.4 104.3 wPt-6867 wPt-0447 QNNdISk.ndsu.3D
QDH.ndsu.3A 3B-3 114.83D-133.2 wPt-1159wPt-665093wPt-10130 3D-2 QNS.ndsu.3D 11.9 wPt-742030 wPt-742020 wPt-2631 103.5 wPt-7412 tPt-8905
QDM.ndsu.3A1 87.1 wPt-1353 105.6 QSL.ndsu.4A wPt-8845 QALB.ndsu.4A 62.0 QDH.ndsu.3D wPt-6854 wPt-9369 124.1 43.7 wPt-732044wPt-6169 0.015.8 wPt-740945wPt-741522 108.7 wPt-9675 wPt-1961 64.2 wPt-3760 QNS.ndsu.3B 119.6 wPt-5303 96.5 QDH.ndsu.3B.1 46.9 tPt-1759wPt-9258wPt-6973 wPt-7992 QDM.ndsu.3D 19.9 rPt-1806wPt-9401wPt-0965 110.5 wPt-8967 wPt-3845 124.8 1.3 QPC.ndsu.4A 65.8 wPt-2685 tPt-1366 QALM.ndsu.4B 119.2 tPt-0242 66.3 wPt-731663 22.8 wPt-740590wPt-740703 wPt-741484 112.3 wPt-1007 QDM.ndsu.3A.2 148.4 wPt-0286 125.3 wPt-7015 2.624.4 wPt-741829 wPt-733640 113.0 wPt-669526 77.2 wPt-4412 125.8 wPt-9170 wPt-664393 wPt-729938 wPt-741190
149.4 QDM.ndsu.3B wPt-1562 wPt-3816 wPt-740662 wPt-741800 113.6 wPt-2836 QDH.ndsu.3B.2 QPC.ndsu.3B.2 79.8 wPt-1311 134.3 wPt-743661 wPt-11032 2.9 wPt-742255 165.2 wPt-2740 wPt-7266 wPt-8396 wPt-740803 wPt-741333 114.9 wPt-6390 wPt-744743 134.6 wPt-740945rPt-5396 3.224.7 wPt-730115wPt-741656 wPt-741102wPt-741987 rPt-7987 165.9 wPt-4370 wPt-0365 0.0 0.0 wPt-2247 119.9 148.1 wPt-731910 wPt-4364 3.8 wPt-733405wPt-742431 wPt-740976wPt-741558 wPt-734161 wPt-0764 80.2 wPt-0405 wPt-1822 1.3 rPt-1806 wPt-0965 0.0 wPt-2247 3.1 wPt-8841 120.2 wPt-7956115 wPt-0438 wPt-740590 wPt-741484 6.1 3.1 wPt-742264wPt-741767wPt-8841wPt-7406404.9 wPt-2946 wPt-8657 wPt-4487 wPt-741521 11.6 tPt-9400 wPt-8141 wPt-5825 2.6 3B-3wPt-729938 wPt-741190 7.725.0 4.9 wPt-741252wPt-2946wPt-743515 121.7 wPt-731166 25.3 wPt-740665 wPt-74198418.4 wPt-743165 123.0 wPt-5642 80.6 wPt-4576 8.1 11.6 wPt-9810tPt-9400 19.8 wPt-0817
3A.3(S+L) 3B.1(1L) 3B.2(S+L) 2.90.0 wPt-742255wPt-1615 wPt-2858 25.7 wPt-742156 wPt-1161 wPt-4680 9.0 18.4 wPt-5163wPt-743165 21.6 rPt-7285 123.8 3.24.8 wPt-730115wPt-9586 wPt-741102 26.4 19.8 wPt-1968wPt-0817 22.7 wPt-6728 124.2 wPt-6757 9.6 wPt-741500 3.88.1 wPt-733405wPt-741584wPt-740976 26.9 21.6 wPt-1516rPt-7285 23.3 wPt-744614 127.1 wPt-3250 4B-2 10.627.2 wPt-734051wPt-1336 23.6 wPt-3810 6.18.6 wPt-742264wPt-669355 wPt-741201 22.7 wPt-6728 wPt-1091 wPt-744595 wPt-4280 30.7 wPt-669255 24.9 0.0 23.3 wPt-742530wPt-744614wPt-73341226.9 wPt-5951 wPt-1272 12.67.7 wPt-741252wPt-7739 wPt-743515 0.0 tPt-0602 42.3 23.6 wPt-3094wPt-3810 30.3 wPt-58574B-1 2.3 wPt-734310 wPt-732448 wPt-742329 wPt-742443 12.0 wPt-732423 18.08.1 wPt-9810wPt-667895 42.1 wPt-7926 wPt-744595 wPt-42805.5 wPt-5559 0.0 wPt-1615 wPt-2858 24.9 wPt-742171wPt-1091wPt-741733 23.6 wPt-8892 wPt-6834 62.4 wPt-98330.0 6.8 wPt-1046 40.19.0 wPt-5163 26.9 wPt-5951 wPt-1272 31.0 wPt-744434
wPt-731103 QNS.ndsu.4B
4.8 wPt-9586 wPt-740619 wPt-74121662.7 wPt-4645 0.0 0.0 tPt-0602
QKS.ndsu.4A QNNd.ndsu.4A 47.69.6 wPt-741500wPt-0912 30.3 wPt-5857QKNd.ndsu.4A 2.3 wPt-734310 wPt-732448 42.9 wPt-1101 wPt-6016 8.1 wPt-741584 10.9 90.0 wPt-0798 0.8 wPt-667148 wPt-732423
12.0 QLd.ndsu.4B wPt-741037 wPt-6965 QNd.ndsu.4B 53.8 wPt-6209 wPt-8959 42.1 wPt-7926 5.5 wPt-5559 QPH.ndsu.4B 54.8 wPt-734051 100.2 wPt-0150 QSL.ndsu.4B wPt-669355 wPt-741201 10.6 2.0 wPt-0732 23.6 wPt-889258.6 wPt-9067 8.6 62.4 wPt-2464wPt-9833wPt-9335 wPt-7420566.8 wPt-1046wPt-2951 56.1 wPt-742530wPt-6856 wPt-10349wPt-733412 31.0 wPt-74443459.7 wPt-7062 103.9 wPt-744256 wPt-6647493.6 wPt-741510QNS.ndsu.4B wPt-733983 12.6 wPt-7739 62.7 wPt-742148wPt-46453DwPt-740873-2 wPt-2214 QKS.ndsu.4A 70.7 wPt-3342 QNNd.ndsu.4A 56.4 wPt-742329 wPt-742443 QKNd.ndsu.4A wPt-672107 42.9 wPt-1101 wPt-6016 18.0 wPt-667895 90.0 wPt-741495QPP.ndsu.4A wPt-0798wPt-742463 3.9 wPt-742339 wPt-2814 86.4 wPt-744934
104.3 wPt-6867 wPt-0447 QLd.ndsu.4B 59.4 rPt-0996 wPt-2119 QNd.ndsu.4B 53.8 wPt-6209
wPt-742171 wPt-741733 QPH.ndsu.4B QSL.ndsu.4B 40.1 wPt-6834 100.2 wPt-0150 105.6 wPt-742020QLd.ndsu.3D wPt-263122.1 wPt-740580 103.5 wPt-7412 tPt-8905 QSL.ndsu.4A wPt-741157 11.4 QALB.ndsu.4A 60.1 wPt-740619wPt-731500wPt-741216 0.0 wPt-731103 QNdISk.ndsu.3D 58.6 wPt-9067 wPt-0912 wPt-742056 wPt-2951108.7 wPt-9675 wPt-1961 33.2 wPt-665093 wPt-5303 47.6 QNS.ndsu.3D wPt-742030 wPt-7062119.6
10.9 wPt-8845 11.9 0.8 wPt-667148110.5 wPt-8967 wPt-3845 59.7 QPC.ndsu.4A 62.0 wPt-744256 wPt-664749 QDH.ndsu.3D wPt-741037 wPt-6965 103.9 0.0 QALM.ndsu.4B wPt-7225 54.8 wPt-8959 15.8 wPt-741522 112.3 wPt-1007 43.7 wPt-6169 70.7 wPt-2214
64.2 wPt-2464wPt-3760wPt-9335 2.0wPt-672107wPt-0732 0.4 wPt-8446 QPP.ndsu.4A QDH.ndsu.3B.1 113.0 wPt-669526 46.9 wPt-9258 86.4 wPt-744934 56.1 wPt-6856 wPt-10349 19.9 104.3 wPt-9401wPt-6867 wPt-0447 QDM.ndsu.3D 65.8 wPt-742148wPt-2685 wPt-740873 3.60.0 wPt-741510wPt-741078113.6 wPt-733983wPt-2836 1.4 wPt-1620 56.4 wPt-3342 22.8 105.6 wPt-740703wPt-742020 wPt-2631114.9 wPt-6390 103.5 wPt-744251wPt-7412 tPt-8905 QSL.ndsu.4A 3.8 66.3 wPt-731663 QALB.ndsu.4A 3.92.4 wPt-742339wPt-671711wPt-2814wPt-741848 wPt-741495 wPt-742463 108.7 wPt-9675 wPt-1961119.9 rPt-7987 59.4 rPt-0996 wPt-2119 24.4 wPt-7418293.0 wPt-733640wPt-743858 5.7119.6 wPt-3260wPt-5303 QLd.ndsu.3D wPt-740580 11.477.2 wPt-741157wPt-4412 110.5 22.1wPt-8967 wPt-3845 wPt-734161 wPt-0764 Consistent QTL QNdISk.ndsu.3D wPt-10972
QPC.ndsu.4A 120.2 7.8 QALM.ndsu.4B
60.1 wPt-731500 QDM.ndsu.3B wPt-7406623.6 wPt-741800wPt-741816 wPt-8657 wPt-4487 QDH.ndsu.3B.2 QPC.ndsu.3B.2 79.8 wPt-1311 33.2 wPt-665093 3B-2
QNS.ndsu.3D 112.3 wPt-1007 wPt-8845 11.9 wPt-742030 wPt-7408036.1 wPt-741333wPt-743909121.7 wPt-731166 18.2 wPt-666139 62.0 wPt-7266 wPt-8396 113.0 QDH.ndsu.3D 43.7wPt-669526wPt-6169 15.8 wPt-741522 6.7 wPt-742486123.0 wPt-5642 30.7 wPt-741331 wPt-741413 64.2 wPt-3760 24.7 wPt-741656wPt-2836wPt-741987 wPt-1161 wPt-46800.0 wPt-8096 wPt-5906 QDH.ndsu.3B.1 wPt-4370 wPt-0365 113.6 46.9 wPt-9258123.8 32.3 wPt-0267 wPt-741750 19.9 wPt-9401 QDM.ndsu.3D 8.1 wPt-742039 Putative QTL 65.8 wPt-2685 114.9 wPt-742431wPt-6390wPt-741558124.2 wPt-6757 8.8 wPt-5769 80.2 wPt-0405 wPt-1822 8.7 wPt-742665 33.9 wPt-5916 22.8 wPt-740703 119.9 wPt-741767rPt-7987wPt-740640127.1 wPt-3250 66.3 wPt-731663 wPt-7956 wPt-0438 9.2 wPt-741986 wPt-742118 37.3 wPt-3536 wPt-0302 24.4 wPt-741829 wPt-733640 QGY.ndsu.3A wPt-734161 wPt-0764 77.2 wPt-4412 25.0 wPt-74152113.5 wPt-9303 34.6 wPt-7301 43.7 wPt-4842 wPt-8141 wPt-5825 120.2 wPt-8657 wPt-4487 QDM.ndsu.3B wPt-740662 wPt-741800 44.0 tPt-6487
QKNd.ndsu.3A SS-Related QTL QDH.ndsu.3B.2 QPC.ndsu.3B.2 79.8 wPt-1311 wPt-4576 25.3 wPt-74066513.9 wPt-741984wPt-740730 80.6 QLd.ndsu.3A 121.7 wPt-731166 wPt-740803 wPt-741333 wPt-74215635.9 wPt-667640 wPt-2299 wPt-0013 wPt-7266 wPt-8396 25.7 QALB.ndsu.3A 123.0 wPt-5642 24.7 wPt-741656 wPt-741987 36.3 tPt-1079 wPt-732330 wPt-733544 wPt-4370 wPt-0365 26.4 123.8 wPt-1968wPt-1161 wPt-4680 69.6 48.2 wPt-1464 wPt-2766 wPt-0895 Fig. 3-2. Genetic map andwPt-742431 QTL wPt-741558for 10 spike-related,26.9 124.2 andwPt-1516 wPt-675710 agronomic traits of a RIL population derived from the cross of the elite line WCB414 80.2 wPt-0405 wPt-1822 48.9 wPt-9928 wPt-2298 wPt-7956 wPt-0438 wPt-741767 wPt-740640 27.2 127.1 wPt-1336wPt-3250 95.2 wPt-6216 and the exotic lineWCB6wPt-74152117 with SS trait, over four to six locations50.4 wPt-9634 in ND, USA during 2009, 2010, and 2011 (Continued) wPt-8141 wPt-5825 25.0 30.7 wPt-66925557.1 wPt-664488 96.3 wPt-9579 QNS.ndsu.3A wPt-2936 80.6 wPt-4576 25.3 wPt-740665 wPt-741984 42.3 wPt-309459.9 wPt-7217 96.7 25.7 wPt-742156 79.8 wPt-1655 wPt-2938 98.9 wPt-1191 108.9 wPt-6945 26.4 wPt-1968 86.4 wPt-7890 QPC.ndsu.3B.1
QDH.ndsu.3A 114.8 wPt-1159 wPt-10130 26.9 wPt-1516 QDM.ndsu.3A1 87.1 wPt-1353 wPt-6854 wPt-9369 124.1 wPt-732044 27.2 wPt-1336 96.5 QNS.ndsu.3B tPt-1759 wPt-6973 wPt-7992 124.8 30.7 wPt-669255 119.2 tPt-0242 tPt-1366 42.3 wPt-3094 QDM.ndsu.3A.2 148.4 wPt-0286 125.3 wPt-7015 149.4 wPt-1562 wPt-3816 125.8 wPt-9170 wPt-664393 165.2 wPt-2740 134.3 wPt-743661 wPt-11032 165.9 wPt-744743 134.6 rPt-5396 148.1 wPt-731910 wPt-4364 5B
5A-1 5A-2 5D 6A-1
6A-1 5B 0.0 wPt-730591 0.0 wPt-9724 1.5 wPt-2636 wPt-0864 0.7 wPt-8604 3.7 wPt-4836 11.0 wPt-7237 7.4 wPt-741630 17.1 wPt-3085 20.8 wPt-9687 17.8 rPt-6127 22.3 wPt-1285 26.5 wPt-5737 24.5 wPt-669498 34.8 tPt-8942 wPt-4736 28.4 wPt-0228
QNS.ndsu.5B.1 38.1 wPt-2041 32.2 wPt-1377 QPP.ndsu.5B 60.6 wPt-7418 37.8 wPt-9832 65.6 wPt-742141 wPt-5514 43.6 wPt-671561 90.7 wPt-3049 43.9 wPt-0562 wPt-741170 106.2 wPt-6531 46.7 wPt-5964 QALM.ndsu.5B 5A.1(S) QALT.ndsu.5B 5A.2(L)106.7 wPt-8393 5D 6A.1(L) 53.8 wPt-733976 5A-1 QAAL.ndsu.5B.1 109.0 5A-2 wPt-3457 5D 6A-1 wPt-9679 wPt-3524 109.7 wPt-1895 55.8 wPt-731934 112.3 wPt-1548 wPt-0596 wPt-5870 56.1 wPt-7027 135.8 wPt-6191 wPt-2707 0.0 0.0 wPt-9094 0.0 wPt-1038 3.3 wPt-667413 56.6 tPt-2833 wPt-731842 158.7 wPt-6014 57.7 wPt-8719 187.5 13.1wPt-7006wPt-1903 wPt-3334 3.7 wPt-665027 5A.1(S) 23.2 QNdD.ndsu.5B 5A.2(L)wPt-800131 5D 6A.1(L) QPH.ndsu.5B 58.1 rPt-9065 wPt-8226 207.2 wPt-1060
24.3 QALM.ndsu.5A QALT.ndsu.5A QALM.ndsu.6A wPt-666574 tPt-0877 220.7 wPt-4402 0.0 wPt-73059161.8
QNS.ndsu.5A wPt-6904 wPt-671855 QPC.ndsu.5B.1 QLPC.ndsu.5B.1 221.7 wPt-0295 1.5 wPt-2636 wPt-0864 77.9 wPt-732760 229.1 wPt-7665 wPt-9116 3.7 wPt-4836 wPt-3965 wPt-7623 QKNd.ndsu.5B 78.2 236.1 wPt-0484 7.4 wPt-741630 237.1 wPt-6465 QSS.ndsu-6A.2 78.5 wPt-731010 5A-1 5A-2 5D 6A-1 20.8 QNdISk.ndsu.6A.2 wPt-968779.7 wPt-667170
238.9 wPt-3922 QAAL.ndsu.6A 22.3QALB.ndsu.6A wPt-128582.1 wPt-4791 244.4 wPt-6971 24.5 wPt-669498
QPC.ndsu.5B.2 wPt-667780 wPt-2822 QLPC.ndsu.5B.2 251.8 wPt-2373 QALT.ndsu.6A wPt-0228
QNS.ndsu.5B.2 28.4 263.3 wPt-8449 82.4 wPt-664733 wPt-733764 32.2 wPt-1377 263.9 wPt-1348 wPt-9584 37.8 wPt-983283.7 wPt-666208 QNS.ndsu.5B.3 289.1 wPt-3995 116 292.6 wPt-3569 0.0 wPt-730591 43.6 wPt-67156184.7 wPt-9131 wPt-8539 307.8 wPt-744851 1.5 wPt-2636 wPt-0864 43.9 wPt-056285.0wPt-741170wPt-671799 QSS.ndsu-5B 3.7 wPt-4836 46.7 wPt-5964 tPt-6278
QAAL.ndsu.5B.2 tPt-4875 wPt-0929 86.6 5A-1 308.2 wPt-744750 7.4 wPt-741630 0.0 wPt-66677353.8 wPt-73397689.9 wPt-666988 wPt-9687 3.2 wPt-731592 309.1 wPt-1457 0.0 wPt-059620.8 wPt-5870 wPt-967994.8wPt-3524 tPt-7399 5A-2 wPt-1285 wPt-9089 55.8tPt-4178 0.0 wPt-9094 0.0 wPt-1038 3.3 wPt-66741322.3 3.6 wPt-73193495.6 wPt-666074 24.5 wPt-669498 wPt-2880 wPt-1903 wPt-3334 3.7 wPt-665027 56.1 wPt-7027113.6 wPt-733115 13.1 0.0 wPt-0596 wPt-5870wPt-0228 6.0 wPt-7809 wPt-666964 23.2 wPt-8001310.0 wPt-9094 0.0 wPt-1038 28.4 6A-2 56.6 tPt-2833114.5wPt-731842wPt-731413 wPt-8226 3.3 32.2wPt-667413 wPt-1377 11.1 wPt-732951
24.3 QALM.ndsu.5A QALT.ndsu.5A wPt-6650270.0 QDH.ndsu.6A wPt-666773 57.7 wPt-8719114.9 wPt-730711 wPt-730109 QNS.ndsu.5A 13.1 wPt-1903 wPt-3334 3.7 37.8 wPt-9832 12.7 wPt-6696 23.2 wPt-800131 3.2 wPt-731592 58.1 rPt-9065115.4 wPt-3091 wPt-8226 0.0 wPt-0596 wPt-5870 43.6 wPt-671561 115.9 wPt-0357
24.3 QALM.ndsu.5A QALT.ndsu.5A wPt-9089QALM.ndsu.6A tPt-4178 wPt-666574 tPt-0877 0.0 wPt-9094 0.0 wPt-1038 43.9 3.6wPt-0562 wPt-741170 61.8 QNS.ndsu.5A 3.3 wPt-667413 117.7 wPt-0139 wPt-5964wPt-2880 wPt-6904 wPt-671855 13.1 wPt-1903 wPt-3334 3.7 wPt-665027 46.7 118.1 wPt-666224 23.2 wPt-800131 53.8 6.0wPt-733976wPt-7809 wPt-666964 77.9 wPt-732760 wPt-8226 11.1 wPt-732951 wPt-3965120.4wPt-7623 wPt-3308
24.3 QALM.ndsu.5A 78.2 QALT.ndsu.5A
QDH.ndsu.6A wPt-9679 wPt-3524 55.8 122.4 wPt-7445 QNS.ndsu.5A 5D wPt-6696 12.7wPt-731934 QSS.ndsu-6A.2 78.5 wPt-731010
QNdISk.ndsu.6A.2 124.8 wPt-729806 0.0 wPt-0596 wPt-5870 56.1 wPt-7027 79.7 wPt-667170 QAAL.ndsu.6A 148.9 wPt-667618 wPt-7063 0.0 wPt-9094 0.0 wPt-1038 56.6 tPt-2833 wPt-731842QALB.ndsu.6A 82.1 wPt-4791 3.3 wPt-667413 150.7 tPt-8557Consistent QTL 13.1 wPt-1903 wPt-3334 3.7 wPt-665027 57.7 wPt-8719 wPt-667780 wPt-2822 QALT.ndsu.6A 153.7 wPt-729839 23.2 wPt-800131 58.1 rPt-9065 82.4 wPt-664733 wPt-733764 wPt-8226
24.3 QALM.ndsu.5A QALT.ndsu.5A QALM.ndsu.6A wPt-666574 tPt-0877 61.8 wPt-9584 QNS.ndsu.5A Putative QTL wPt-6904 wPt-671855 83.7 wPt-666208 77.9 wPt-732760 84.7 wPt-9131 wPt-8539 78.2 wPt-3965 wPt-7623 85.0 wPt-671799 QSS.ndsu-6A.2 78.5 wPt-731010 SS-Related QTL
QNdISk.ndsu.6A.2 tPt-6278
79.7 wPt-667170 86.6 QAAL.ndsu.6A Fig. 3-2. Genetic map and QTL for 10 spike-related, and 10 QALB.ndsu.6A agronomic82.1 traitswPt-4791 of a RIL population 89.9derivedwPt-666988 from the cross of the elite line WCB414 wPt-667780 wPt-2822 94.8 tPt-7399 and the exotic lineWCB617 with SS trait, over four to sixQALT.ndsu.6A locations in ND,82.4 USAwPt-664733 duringwPt-733764 2009, 2010,95.6 and 2011wPt-666074 (Continued) wPt-9584 113.6 wPt-733115 83.7 wPt-666208 114.5 wPt-731413 84.7 wPt-9131 wPt-8539 114.9 wPt-730711 wPt-730109 85.0 wPt-671799 115.4 wPt-3091 86.6 tPt-6278 115.9 wPt-0357 89.9 wPt-666988 117.7 wPt-0139 94.8 tPt-7399 118.1 wPt-666224 95.6 wPt-666074 120.4 wPt-3308 113.6 wPt-733115 122.4 wPt-7445 114.5 wPt-731413 124.8 wPt-729806 114.9 wPt-730711 wPt-730109 wPt-667618 wPt-7063 115.4 wPt-3091 148.9 115.9 wPt-0357 150.7 tPt-8557 117.7 wPt-0139 153.7 wPt-729839 118.1 wPt-666224 120.4 wPt-3308 122.4 wPt-7445 124.8 wPt-729806 148.9 wPt-667618 wPt-7063 150.7 tPt-8557 153.7 wPt-729839 6A-2 6B-1 6B-2
7B-1 7B-2 7B-3
0.0 wPt-1784 10.5 wPt-6994 15.6 wPt-4706 17.4 wPt-7954 19.4 wPt-3800 6A-2 19.8 6B-1wPt-0882 wPt-8239 6B-2 21.1 wPt-743522 22.9 wPt-664276 24.8 wPt-7207 26.2 wPt-3130 26.5 wPt-9990 wPt-8563 6B-2 7B-1 28.8 6BwPt-5066-1 0.0 wPt-744769 wPt-4025 5.4 wPt-7108 29.4 0.0 wPt-1089wPt-1784wPt-9255 0.0 wPt-6116 0.0 30.7 wPt-6674 2.1 wPt-0920 wPt-8246 wPt-4140 0.0 wPt-666773 10.5 wPt-6994 5.5 wPt-1541 5.7 tPt-8569 33.3 wPt-4564wPt-4706 2.5 wPt-1587 3.2 wPt-731592 15.6 6D 6.5 7A-1 wPt-0171 7A-2 7A-3 6.3 wPt-3445 40.317.4 wPt-1756wPt-7954 3.4 wPt-8919 wPt-9089 tPt-4178 8.7 wPt-4164 10.0 wPt-0312 wPt-2449 wPt-733669 3.6 45.119.4 wPt-3116wPt-3800 6.6 wPt-2880 9.5 wPt-732061 18.9 wPt-2305 wPt-2933 48.519.8 wPt-5234wPt-0882 wPt-8239 0.0 wPt-744897 6.0 wPt-7809 wPt-666964 12.2 wPt-9256 20.2 wPt-9516 7.5 wPt-669693 5.2 wPt-9072 53.421.1 wPt-2102wPt-743522 11.1 wPt-732951 15.0 wPt-0406 26.0 wPt-6372 wPt-3873 8.0 wPt-0866 5.6 wPt-1557 tPt-9518
QDH.ndsu.6A 22.9 wPt-664276
57.8 wPt-6127 10.5 wPt-5677 QPP.ndsu.6B.2 12.7 wPt-6696 QPC.ndsu.6B.1 17.8 wPt-1264 wPt-3045 34.6 wPt-8981 wPt-9665 wPt-2883 wPt-665112
QKSk.ndsu.6B 30.3 QPH.ndsu.6B1 24.8 wPt-7207 QALM.ndsu.6B.1 QPSS.ndsu-6B.1 wPt-744187 62.6 wPt-1437 wPt-2095 QKNd.ndsu.6B wPt-6878 39.1 wPt-2273 11.1
26.6 QPH.ndsu.7B.1 wPt-664517 26.2 wPt-3130 QDM.ndsu.7B.2 31.1 wPt-6594 wPt-3723 13.8 wPt-4620 QALM.ndsu.6B.2 65.0 wPt-9990 wPt-8563 27.0 wPt-744795 wPt-9660 7B.2(L) 54.6 7B.3(L) 7D.1(L)32.4 wPt-671801
26.5 QALB.ndsu.7B
QALT.ndsu.7B QAAL.ndsu.7B wPt-4142 79.1 wPt-4258 QDH.ndsu.7B 16.1 wPt-734169
67.3 QALM.ndsu.7B.1
wPt-5066 wPt-3207 QDM.ndsu.7B.1 28.8 31.7 QLd.ndsu.7B 22.5 wPt-7387 67.729.4 wPt-4930wPt-1089 wPt-9255 89.3 wPt-744652 0.0 wPt-61167A.2(L) 7A.3(S) 7B.1 23.8 wPt-9515 tPt-8504 72.330.7 wPt-745074wPt-6674 93.5 wPt-743502 0.0 wPt-666773 5.5 wPt-1541 26.2 wPt-9013 74.433.3 wPt-6667wPt-4564 7A-1 94.5 wPt-0266
wPt-0171 QALM.ndsu.7B.2 3.2 wPt-731592 6.5 wPt-4045 34.2 wPt-742417 QNdISk.ndsu.6B.2 96.0 wPt-9089 tPt-4178 75.140.3 wPt-665017wPt-1756 wPt-3526 8.7 0.0 wPt-4164wPt-9796 QPSS.ndsu-7B.2 96.9 wPt-8233 wPt-0635 34.6 wPt-743215 3.6 wPt-2880 82.245.1 wPt-666793wPt-3116 9.5 1.8 wPt-732061wPt-7734 wPt-8418 97.7 wPt-0194 35.4 wPt-744632 6.0 wPt-7809 wPt-666964 48.5 wPt-5256wPt-5234 12.225.0 wPt-9256 85.4 25.4 wPt-742051 101.3 wPt-8615 wPt-732951 53.4 wPt-2102 15.0 wPt-0406 QKSk.ndsu.7B 11.1 86.7 wPt-8814 wPt-2371 QPSS.ndsu-7B.1
QDH.ndsu.6A 6B.2(S+L) 57.8 wPt-61276D(S) 7A.1(S2) 26.6 106.2 wPt-2407 QPP.ndsu.6B.2 12.7 wPt-6696 QPC.ndsu.6B.1 17.8 wPt-1264 wPt-3045
QKSk.ndsu.6B wPt-6417 QPH.ndsu.6B1
88.1 wPt-741515 QALM.ndsu.6B.1 30.2 112.0 wPt-9987 QPSS.ndsu-6B.1 62.6 wPt-1437 wPt-2095 QKNd.ndsu.6B wPt-6878 QPH.ndsu.7B2
QGY.ndsu.6B 26.633.2 wPt-3059 QPH.ndsu.6B.2
wPt-1241 QNdD.ndsu.7B.3 92.3 wPt-6594 wPt-1695 121.6 wPt-665428 QALM.ndsu.6B.2 65.0 0.0 27.034.0 wPt-744795wPt-2199wPt-96607A.2(L) 7A.3(S) 7B.1 wPt-3605 wPt-4637 wPt-9299 101.367.3 wPt-4142 1.1 wPt-5114 wPt-672044 34.4 wPt-3207wPt-6966 0.0 123.7 31.7 wPt-2100 wPt-4796 0.0 wPt-1032 102.367.7 wPt-664250wPt-4930 1.5 wPt-664719 36.2 wPt-8300 4.9 wPt-5975 wPt-5892 wPt-6083 124.01.1 wPt-5338 108.672.3 wPt-2297wPt-745074 2.6 wPt-741955 wPt-672075 wPt-672051 7B-2 wPt-7887 QLd.ndsu.6B 4.6 wPt-666615 37.9 8.3 wPt-3002 wPt-9555 28.3 wPt-5558 wPt-6013 113.374.4 wPt-5408wPt-6667 wPt-665253 wPt-672171 131.7 wPt-1266
QPP.ndsu.6B.1 5.3 wPt-732847 38.5 wPt-664558 11.8 wPt-0514 40.9 wPt-1706
QKNd.ndsu.7A QNdISk.ndsu.6B.2 75.1 wPt-8412wPt-665017 wPt-3526 QNS.ndsu.7A 0.0 wPt-744769 0.0 wPt-1784 113.7 39.0 wPt-3572 wPt-9796 41.2 wPt-5524 wPt-671471 82.2 wPt-666793 0.05.4 wPt-7108 0.0 wPt-4025 10.5 wPt-6994 130.1 wPt-9971 39.3 wPt-2523 wPt-7734 65.1 wPt-4489 6B.2(S+L)wPt-5256 6D(S) 7A.1(S2) 1.8 wPt-8246 wPt-4140 65.8 wPt-7122 2.1 wPt-0920 85.4 QDM.ndsu.7A.2 43.0 wPt-4778 15.6 wPt-4706 139.0 wPt-4742 25.05.7 wPt-8418tPt-8569 86.7 wPt-8814 44.7 wPt-6967 QNdD.ndsu.7A.1 67.2 wPt-0971 2.5 wPt-1587
17.4 wPt-7954QPSS.ndsu.6B.2 139.4 wPt-744396 46.0 wPt-7113 25.46.3 wPt-742051wPt-3445 69.4 wPt-2083 3.4 wPt-8919
117 88.1 wPt-741515 19.4 wPt-3800 141.4 wPt-2218 26.6 wPt-2371wPt-2449 wPt-733669 84.3 wPt-1601 QGY.ndsu.6B 46.5 wPt-4960 wPt-8043 10.0 wPt-0312 QPH.ndsu.6B.2 wPt-1241
19.8 wPt-0882 wPt-823992.3 6.6 wPt-6417 95.0 wPt-0551 QDM.ndsu.7A.1 QDM.ndsu.7A.3 47.0 wPt-8149 30.2 141.8 wPt-743974wPt-3605 wPt-744302 wPt-2933 QNdD.ndsu.7A.2 18.9 wPt-2305 0.0 wPt-664387 21.1 wPt-743522 101.3 wPt-8700 wPt-3059 100.1 wPt-7763 47.9 33.2 wPt-669693 147.4102.3 wPt-0052wPt-664250 7.5 20.2 wPt-9516 wPt-9822 wPt-1628 22.9 wPt-664276 48.8 wPt-3794 34.08.0 wPt-2199wPt-0866 7B-3 wPt-6372 wPt-3873 155.6108.6 tPt-1723wPt-2297 wPt-5257 wPt-5101 26.0 1.6 wPt-2054 tPt-4614 QLd.ndsu.6B 24.8 wPt-7207 52.9 34.410.5 wPt-6966wPt-5677 0.0 wPt-744897 155.9113.3 wPt-6247wPt-5408 tPt-6794wPt-1032 34.6 wPt-8981 wPt-9665 wPt-667548 wPt-3130QPP.ndsu.6B.1 0.0 26.2 0.0 wPt-1784 36.211.1 wPt-8300wPt-7441870.0 wPt-9796 5.2 wPt-9072 39.1 wPt-2273
wPt-0008 QPH.ndsu.7B.1 167.4113.7 wPt-741530wPt-8412 59.6 1.1 wPt-5338 QDM.ndsu.7B.2 26.5 wPt-9990 wPt-8563 wPt-6994 13.8 wPt-672075wPt-46201.8 wPt-7734wPt-672051 wPt-1557 tPt-9518 10.5 wPt-9971 67.2 wPt-740561 37.9 5.6 54.6 wPt-3723 130.1 wPt-742929 wPt-6522 QALB.ndsu.7B 28.8 wPt-5066 167.7 28.3 wPt-5558 wPt-6013 wPt-8418 QALT.ndsu.7B wPt-66525325.0 wPt-672171 QAAL.ndsu.7B
15.6 wPt-4706 72.2 wPt-3135 QDH.ndsu.7B 16.1 wPt-734169 30.3 wPt-2883 wPt-665112 79.1 wPt-4258
QALM.ndsu.7B.1 QDM.ndsu.7B.1
139.0 wPt-4742 QLd.ndsu.7B 29.4 wPt-1089 wPt-9255184.117.4 wPt-7935wPt-7954 90.5 40.9rPt-4199wPt-1706 38.522.5 wPt-66455825.4wPt-7387 wPt-742051 31.1 wPt-664517 7A-2 QNS.ndsu.7A 89.3 wPt-744652 wPt-6674 QPSS.ndsu.6B.2 139.4 wPt-744396 41.2 wPt-5524 wPt-671471 30.7 184.519.4 rPt-1040wPt-38000.0 wPt-1695 39.023.8 wPt-357226.6wPt-9515 tPt-8504wPt-2371 32.4 wPt-671801 93.5 wPt-743502 wPt-4564 141.4 wPt-2218 wPt-4489 33.3 185.519.8 wPt-2564wPt-08821.1 wPt-8239wPt-5114 wPt-672044 0.0 wPt-4637 65.1 39.326.2 wPt-252330.2wPt-9013 wPt-6417 wPt-0266 141.8 wPt-743974 wPt-744302 94.5 40.3 wPt-1756 65.8 wPt-7122 QALM.ndsu.7B.2 wPt-743522 wPt-2100 wPt-4796 QDM.ndsu.7A.2 43.034.2 wPt-477833.2wPt-742417wPt-3059 193.621.1 wPt-31681.5 wPt-664719 4.9 7A-3 96.0 wPt-4045 45.1 wPt-3116 147.4 wPt-0052 wPt-6083 QNdD.ndsu.7A.1 67.2 wPt-0971 44.7 wPt-696734.0 wPt-2199 195.622.9 wPt-743099wPt-6642762.6 wPt-741955 34.6 wPt-743215 QPSS.ndsu-7B.2 96.9 wPt-8233 wPt-0635 48.5 wPt-5234 155.6 tPt-1723 69.4 wPt-2083 24.8 wPt-72074.6 wPt-666615 8.3 wPt-3002 wPt-9555 46.035.4 wPt-711334.4wPt-744632wPt-6966 0.0 wPt-744897 97.7 wPt-0194 wPt-2102 208.9155.9 wPt-3284wPt-6247 84.3 wPt-16010.0 wPt-1032
53.4 26.2 wPt-31305.3 wPt-732847 11.8 wPt-0514 46.5 wPt-496036.2 wPt-8043wPt-8300 5.2 wPt-9072 101.3 wPt-8615 QKNd.ndsu.7A wPt-6127 221.8 wPt-3581wPt-741530 wPt-05511.1 wPt-5338 QKSk.ndsu.7B
57.8 167.4 95.0 QPSS.ndsu-7B.1 QDM.ndsu.7A.3 QDM.ndsu.7A.1 wPt-672075 wPt-672051 QPP.ndsu.6B.2 47.0 wPt-8149 26.5 wPt-9990 wPt-8563 QNdD.ndsu.7A.2 5.6 wPt-1557 tPt-9518 106.2 wPt-2407 62.6 wPt-1437 wPt-2095222.1167.7 wPt-9881wPt-742929 wPt-6522 100.1 wPt-776328.3 wPt-5558 wPt-6013 37.9 28.8 wPt-5066 47.9 wPt-8700ConsistentwPt-665253 wPt-672171 QTL 30.3 wPt-2883 wPt-665112 112.0 wPt-9987 wPt-6594 wPt-1048wPt-7935 40.9 wPt-1706 QPH.ndsu.7B2 QALM.ndsu.6B.2 65.0 243.9184.1 wPt-1089 wPt-9255 6D 48.8 wPt-379438.5 wPt-664558 wPt-664517
31.1 QNdD.ndsu.7B.3 29.4 QNS.ndsu.7A 121.6 wPt-665428 67.3 wPt-4142 rPt-1040 41.2 wPt-5524 wPt-671471 30.7184.5 wPt-6674 wPt-525739.0 wPt-5101wPt-3572 32.4 wPt-671801 123.7 wPt-9299 67.7 wPt-4930 185.5 wPt-2564 0.0 wPt-1695 65.1 wPt-4489 52.9 wPt-2523 33.3 wPt-4564 0.0 wPt-4637 tPt-679439.3 wPt-5975 wPt-5892 72.3 wPt-745074 1.1 wPt-5114 wPt-672044 65.8 wPt-7122 Putative QTL 124.0 193.6 wPt-1756wPt-3168 59.6 QDM.ndsu.7A.2 wPt-000843.0 wPt-4778 40.3 1.5 wPt-664719 wPt-2100 wPt-4796 wPt-7887 wPt-6667 QNdD.ndsu.7A.1 67.2 wPt-0971 74.4 45.1195.6 wPt-3116wPt-743099 4.9 67.2 wPt-74056144.7 wPt-6967 wPt-1266 2.6 wPt-741955 wPt-6083 wPt-2083 131.7 QNdImSk.ndsu.6B.2 75.1 wPt-665017 wPt-3526208.9 wPt-5234wPt-3284 69.4 72.2 wPt-313546.0 wPt-7113 48.5 4.6 wPt-666615 8.3 wPt-3002 wPt-9555 82.2 wPt-666793 53.4221.8 wPt-2102wPt-3581 84.3 wPt-1601 90.5 rPt-419946.5 SSwPt-4960-RelatedwPt-8043 QTL 5.3 wPt-732847 11.8 wPt-0514 wPt-0551
85.4 wPt-5256 QKNd.ndsu.7A 95.0 QDM.ndsu.7A.3 QDM.ndsu.7A.1 47.0 wPt-8149
57.8222.1 wPt-6127wPt-9881 QNdD.ndsu.7A.2 QPP.ndsu.6B.2 86.7 wPt-8814 62.6243.9 wPt-1437wPt-1048wPt-2095 100.1 wPt-7763 47.9 wPt-8700 Fig. 88.13-2. GeneticwPt-741515 map and QTLwPt-6594 for 10 spike-related, and 10 agronomic traits of a RIL population derived from 48.8the crosswPt-3794 of the elite line WCB414
QALM.ndsu.6B.2 65.0 QGY.ndsu.6B
QPH.ndsu.6B.2 92.3 wPt-1241 wPt-5257 wPt-5101 and the exotic lineWCB61767.3 wPt-4142with SS trait, over four to six locations in ND, USA during 2009, 2010, and 2011 (Continued)52.9 101.3 wPt-3605 67.7 wPt-4930 tPt-6794 102.3 wPt-664250 72.3 wPt-745074 59.6 wPt-0008 108.6 wPt-2297 67.2 wPt-740561 QLd.ndsu.6B 74.4 wPt-6667 113.3 wPt-5408 QPP.ndsu.6B.1 72.2 wPt-3135
QNdImSk.ndsu.6B.2 75.1 wPt-665017 wPt-3526 113.7 wPt-8412 82.2 wPt-666793 90.5 rPt-4199 130.1 wPt-9971 85.4 wPt-5256 139.0 wPt-4742 86.7 wPt-8814
QPSS.ndsu.6B.2 139.4 wPt-744396 88.1 wPt-741515
141.4QGY.ndsu.6B wPt-2218 QPH.ndsu.6B.2 92.3 wPt-1241 141.8 wPt-743974 wPt-744302101.3 wPt-3605 147.4 wPt-0052 102.3 wPt-664250 155.6 tPt-1723 108.6 wPt-2297
QLd.ndsu.6B 155.9 wPt-6247 113.3 wPt-5408 QPP.ndsu.6B.1 167.4 wPt-741530 113.7 wPt-8412 167.7 wPt-742929 wPt-6522130.1 wPt-9971 184.1 wPt-7935 139.0 wPt-4742 184.5 rPt-1040
QPSS.ndsu.6B.2 139.4 wPt-744396 185.5 wPt-2564 141.4 wPt-2218 193.6 wPt-3168 141.8 wPt-743974 wPt-744302 195.6 wPt-743099 147.4 wPt-0052 208.9 wPt-3284 155.6 tPt-1723 221.8 wPt-3581 155.9 wPt-6247 222.1 wPt-9881 167.4 wPt-741530 243.9 wPt-1048 167.7 wPt-742929 wPt-6522 184.1 wPt-7935 184.5 rPt-1040 185.5 wPt-2564 193.6 wPt-3168 195.6 wPt-743099 208.9 wPt-3284 221.8 wPt-3581 222.1 wPt-9881 243.9 wPt-1048 7B.2(L) 7B.3(L) 7D.1(L)
7D.2(S)
0.0 wPt-744769 5.4 wPt-7108 7D-1 0.0 wPt-4025 wPt-8246 wPt-4140 2.1 wPt-0920 5.7 tPt-8569 wPt-744917 2.5 wPt-1587 wPt-3445 0.0 7D-2 6.3 wPt-664368 3.4 wPt-8919 wPt-2449 wPt-733669 10.6 wPt-744347 wPt-74452110.0 wPt-0312 6.6 wPt-2933 11.6 14.9 wPt-665730 18.9 wPt-2305 0.0 wPt-664387 7.5 wPt-669693 15.9 wPt-665471 20.2 wPt-9516 wPt-9822 wPt-1628 8.0 wPt-0866 17.2 wPt-733729 26.0 wPt-6372 wPt-3873 1.6 wPt-2054 tPt-4614 10.5 wPt-5677 18.5 wPt-745062 34.6 wPt-8981 wPt-9665 wPt-667548
11.1 wPt-744187 39.1 wPt-2273 QPH.ndsu.7B.1 QDM.ndsu.7B.2 19.9 wPt-664047 13.8 wPt-4620
20.4 wPt-744512 54.6 wPt-3723
QALB.ndsu.7B
QALT.ndsu.7B QAAL.ndsu.7B
QDH.ndsu.7B 16.1 wPt-734169 79.1 wPt-4258 QALM.ndsu.7B.1
QDM.ndsu.7B.1 wPt-664412 wPt-743860 QLd.ndsu.7B wPt-7387 22.5 20.8 wPt-743651 89.3 wPt-744652 wPt-9515 tPt-8504 23.8 wPt-743671 wPt-74338093.5 wPt-743502 wPt-9013 26.2 21.1 wPt-745070 94.5 wPt-0266 wPt-742417 QALM.ndsu.7B.2 34.2 wPt-743140 wPt-66379296.0 wPt-4045
wPt-743215 QPSS.ndsu-7B.2 34.6 wPt-742911 wPt-504996.9 wPt-8233 wPt-0635 35.4 wPt-744632 21.4 wPt-745121 wPt-74366697.7 wPt-0194 101.3 wPt-8615
QKSk.ndsu.7B wPt-0366 wPt-743306 wPt-744124 QPSS.ndsu-7B.1 106.2 wPt-2407 wPt-745068 112.0 wPt-9987
21.7QPH.ndsu.7B2 22.3 QNdD.ndsu.7B.3 wPt-743384 121.6 wPt-665428 22.9 wPt-742858 123.7 wPt-9299 wPt-5975 wPt-5892 wPt-743510 wPt-744736124.0 Consistent QTL 23.5 wPt-742840 wPt-7887 24.1 wPt-743193 wPt-0992131.7 wPt-1266 25.8 wPt-744818 Putative QTL wPt-664309 wPt-744675 wPt-669154 wPt-664317 27.0 wPt-664971 wPt-744265 SS-Related QTL wPt-744866 wPt-743790 27.6 wPt-664286 wPt-744656 wPt-744388 wPt-664020 27.9 wPt-663992 wPt-743269 28.6 wPt-663849 31.2 wPt-0303 wPt-743096 wPt-665260 34.2 wPt-743549 35.3 wPt-3359 52.7 wPt-743501 wPt-744300
Fig. 3-2. Genetic map and QTL for 10 spike-related, and 10 agronomic traits of a RIL population derived from the cross of the elite line WCB414 and the exotic lineWCB617 with SS trait, over four to six locations in ND, USA during 2009, 2010, and 2011 (Continued)
The genetic analysis of the RIL population resulted in the identification of two consistent QTL
(QKS.ndsu.1A1 and QKS.ndsu.4A) and three putative QTL for KS (QKS.ndsu.1B, QKS.ndsu.2A, and
QKS.ndsu.2D) (Table 3-6, Figure 3-2). These QTL had minor effects and explain PV ranging from 6.22%
to 12.53%. Together these QTL explained 7.1-20.2 of PV in different environments. Additive effects
showed that the branched parent (WCB617) contributed the alleles for increased KS at QKS.ndsu.1A1
and QKS.ndsu.4A loci, while the alleles for increase KS values at QKS.ndsu.1B, QKS.ndsu.2A, and
QKS.ndsu.2D loci were contributed by WCB414.
A total of eight QTL were associated to NNdISk, three of which were consistent
(QNNdISk.ndsu.1A.1, QNNdISk.ndsu.2D, and QNNdISk.ndsu.6A.2) and five were putative
(QNNdISk.ndsu.2A, QNNdISk.ndsu.2B.1, QNNdISk.ndsu.2B.2, QNNdISk.ndsu.3D, and
QNNdISk.ndsu.6B.2) (Table 4 Fig.2) and explained 34.2-43.6% of PV in different environments. Only
QNNdISk.ndsu.2D was a major QTL which explained 21.32% to 34.22% of the PV for NNdISk in different
environments. The other QTL had minor effects and explain 3.72% to12.63% of PV of this trait. Additive
118
effects showed that most of the alleles that increased the values of NNdISk were contributed by WCB617.
The exceptions were observed for QTL QNNdISk.ndsu.3D and QNNdISk.ndsu.6B.2 where the elite parent WCB414 contributed the positive alleles to increase NNdISk (Table 3-6).
In this population, our results showed that NS was controlled by 14 QTL. This trait was studied in five environments and none QTL was detected in at least 50% of them (Table 3-6). Therefore,
QNS.ndsu.1A, QNS.ndsu.1D.1, QNS.ndsu.1D.2, QNS.ndsu.2A, QNS.ndsu.2D, QNS.ndsu.3A,
QNS.ndsu.3B, QNS.ndsu.3D, QNS.ndsu.4B, QNS.ndsu.5A, QNS.ndsu.5B.1, QNS.ndsu.5B.2,
QNS.ndsu.5B.3, and QNS.ndsu.7A are putative QTL for NS. Among these, three of those QTL
(QNS.ndsu.2A, QNS.ndsu.5A.1 and QNS.ndsu.7A3) were major QTL explaining 15.9% to 20.6% of PV.
The other QTL were minor explaining 4.84% to 11.63% of the PV. The PVE explained by these QTL in the environments ranked from 11.6% to- 54.5%. The alleles for increased trait values were contributed by
WCB414 at all the loci, except QNS.ndsu.3D and QNS.ndsu.5B3.
Composite interval mapping for GY identified two consistent (QGY.ndsu.1D and QGY.ndsu.2D.1) and seven putative QTL (QGY.ndsu.1A, QGY.ndsu.2A.1, QGY.ndsu.2A.2, QGY.ndsu.2D.2,
QGY.ndsu.3A and QGY.ndsu.6B) (Table 3-6, Figure 3-2) which explained 8.8-58.6% of PV in different environments. QGY.ndsu.2D.1 explained up to 40.34% of PV and can be considered a major QTL. The
PVE by other QTL ranged from 4.6% to 9.76% and they were considered as minor QTL. The elite patent
(WCB414) contributed the alleles for increased GY at all the loci except QGY.ndsu.3A and QGY.ndsu.6B.
3.4.5. QTL for important agronomical traits
In this population, data analysis showed that DH is controlled by two consistent (QDH.ndsu.2D and QDH.ndsu.3A) and six putative QTL (QDH.ndsu.1A.1, QDH.ndsu.3B.1, QDH.ndsu.3B.2,
QDH.ndsu.3D, QDH.ndsu.6A, and QDH.ndsu.7B) (Table 3-6, Figure 3-2), which together explained 26.4-
43.2% of PV in different environments. One QTL on chromosome 2D (QDH.ndsu.2D) and another on chromosome 3A (QDH.ndsu.3A) have major effect, explaining up to 21.12% and 15.75% of PV, respectively. The other QTL are minor and explained between 6.73% and 11.09% of PV for DH. The branched parent (WCB617) provided the allele that increased the phenotypic value of DH at most of the loci, except at QDH.ndsu.3A1 and QDH.ndsu.6A.
119
Table 3-6. QTL identified for agronomic traits in a RIL population of hexaploid wheat derived from the cross of WCB414 and WCB617 QTL Environment† Flanking markers Pos.‡ (cM) CI§ (cM) LOD Thresh¶ a# R2(%) Kernels per spikelet
QKSk.ndsu.1B II wPt-9809-wPt-5061 79.5 71.5-95.3 4.7 3.2 0.1 9.8 QKSk.ndsu.2B I, IV, AE wPt-744808-wPt-4368 22.0-28.0 9.3-42.0 3.1-6.8 3.1-3.4 0.1-0.2 3.9-13.5 QKSk.ndsu.2D I, II, III, IV, AE wPt-666656-wPt-671914 50.1-74.8 48.4-76.6 14.2-21.0 3.1-3.4 0.2-0.3 21.5-38.8 QKSk.ndsu.6B I, III, IV, AE wPt-0171-wPt-6878 6.5-22.8 0-26.0 2.8-4.0 3.1-3.4 0.1-0.1 3.1-7.1 QKSk.ndsu.7B IV wPt-8615-wPt-2407 101.3 92.9-106.1 4.3 3.3 -0.1 5.2 RTPVE†† 34.7-48.6
Kernels per node
QKNd.ndsu.1A II, IV, AE wPt-8172-wPt-9429 104.9-113.9 88.9-132.7 2.9-4.4 3.1-3.3 -0.1_-0.1 8.3-11.3 QKNd.ndsu.1D III wPt-734132-wPt-3945 129.5 103.7-140.7 4.3 3.2 0.1 8.7 QKNd.ndsu.2B III wPt-7859-wPt-1133 0.0 0-2.9 6.3 3.2 0.1 13.1 QKNd.ndsu.3A IV wPt-9303-wPt-740730 13.5 8.5-27.5 3.3 3.05 0.1 6.9 QKNd.ndsu.4A III, AE wPt-2946-tPt9400 4.9-6.9 0-22.5 2.7-3.2 3.2-3.3 -0.1_-0.1 5.3-7.6 QKNd.ndsu.5B I wPt-6014-wPt-7006 183.7 167.8-194.6 4.4 3.2 0.1 12.9
120 QKNd.ndsu.6B III wPt-1264-wPt-6878 21.8 14.5-31.7 3.1 3.1 -0.1 8.9 QKNd.ndsu.7A I wPt-4637-wPt-2100 0.0 0-10.4 4.2 3.2 0.1 9.2
RTPVE†† 15.9-27.1
Kernels per spike
QKS.ndsu.1A I, IV, AE wPt-1924-wPt-667180 16.4-20.6 8.7-30.3 2.9-4.0 3.2-3.4 -0.9_-2.0 6.2-8.9 QKS.ndsu.1B II wPt-665204-wPt-665037 1.5 0-10.5 4.8 3.3 1.8 11.0 QKS.ndsu.2A IV wPt-740658-wPt-664128 102.8 85.2-121.5 4.5 3.2 2.0 12.3 QKS.ndsu.2D IV wPt-3692-wPt-6780 127.7 113.9-131.9 3.3 3.2 1.5 7.9 QKS.ndsu.4A II, IV, AE wPt-2247-wPt-9400 0.0-9.9 0-22.7 3.1-5.4 3.2-3.4 -1.2_-1.7 7.3-12.5 RTPVE†† 7.1-20.2 (Continues)
Table 3-6. QTL identified for agronomic traits in a RIL population of hexaploid wheat derived from the cross of WCB414 and WCB617 (Continued) QTL Environment† Flanking markers Pos.‡(cM) CI§(cM) LOD Thresh¶ a# R2(%) Number of nodes with immature spikelets at the spike base
QNNdISk.ndsu.1A.1 II, IV, AE wPt-8172-wPt-3272 107.9-130.0 80.8-143.4 2.6-5.4 3.1-3.5 -0.2_-0.3 4.0-12.6 QNNdISk.ndsu.2A III wPt-0003-wPt-4201 143.4 125-149.8 3.9 3.4 -0.4 10.4 QNNdISk.ndsu.2B.1 II wPt-744808-wPt-4368 22.0 10.5-36.4 4.0 3.1 -0.2 6.2 QNNdISk.ndsu.2B.2 AE wPt-1133-wPt-9274 23.0 9.6-30.2 3.5 3.5 -0.2 5.1 QNNdISk.ndsu.2D I, II, III, IV, AE wPt-8134-wPt-3144 32.4-41.8 28.3-43.9 10.0-17.7 3.1-3.5 -0.5_-0.7 21.3-34.2 QNNdISk.ndsu.3D IV wPt-741510-wPt-742339 3.6 0-15.6 4.3 3.2 0.3 5.9 QNNdISk.ndsu.6A.2 II, IV wPt-671561-wPt-0562 43.6 33.1-53.9 3.3-4.1 3.1-3.2 -0.2_-0.2 5.1-5.6 QNNdISk.ndsu.6B.2 III, AE wPt-4930-wPt-666793 67.7-75.1 52.9-84.9 2.7-3.5 3.4-3.5 0.2-0.3 3.7-5.7 RTPVE††¶ 34.2-43.6
Number spikes/area
QNS.ndsu.1A II, AE wPt-9429-wPt-6654 117.1-119.1 106.5-132.2 3.8-5.2 3.3 8.3-11.0 6.6-11.6 QNS.ndsu.1D.1 IV wPt-733835-wPt-672077 85.5 60.5-98.9 3.5 3.1 11.2 8.2 QNS.ndsu.1D.2 AE wPt-4497-wPt-5253 151.1 144.8-153 6.1 3.3 10.2 10.6
121 QNS.ndsu.2A V, VI, AE wPt-798339-wPt-2850 37.0-52.2 33.1-65.4 4.1-4.8 3.2-3.3 12.9-23.5 11.7-15.9 QNS.ndsu.2D III, VI, AE wPt-730427-wPt-665836 105.4-109.0 94.3-125.5 2.7-5.8 3.2-3.3 9.9-11.8 4.8-10.0
QNS.ndsu.3A IV wPt-7890-wPt-1353 86.4 80.9-91.6 5.3 3.1 12.7 10.9 QNS.ndsu.3B VI rPt-5396-wPt-731910 134.6 127.9-147.7 3.7 3.2 12.2 6.8 QNS.ndsu.3D IV, VI wPt-741522-wPt-9401 15.8-17.8 1.8-42.3 2.7-2.7 3.1-3.2 -9.4_-10.6 4.8-5.5 QNS.ndsu.4B IV tPt-0602-wPt-732423 3.0 0-11.5 3.4 3.1 12.0 9.0 QNS.ndsu.5A I, III, AE wPt-9094-wPt-8226 20.0-23.2 1.9-24.3 2.8-8.1 3.2-3.3 12.2-13.3 7.5-15.6 QNS.ndsu.5B.1 I rPt-6127-wPt-5737 17.8 6.1-26.4 4.4 3.2 15.3 9.5 QNS.ndsu.5B.2 III wPt-6465-wPt-3922 238.1 221.8-239.2 3.7 3.3 18.1 8.2 QNS.ndsu.5B.3 III wPt-2373-wPt-8449 251.8 251.4-258.3 3.6 3.3 -17.2 7.8 QNS.ndsu.7A IV wPt-7734-wPt-8418 6.8 0.6-17.2 6.7 3.1 17.7 20.59 RTPVE††¶ 11.6-54.5 (Continues)
Table 3-6. QTL identified for agronomic traits in a RIL population of hexaploid wheat derived from the cross of WCB414 and WCB617 (Continued) QTL Environment† Flanking markers Pos.‡(cM) CI§(cM) LOD Thresh¶. a# R2(%) Grain yield
QGY.ndsu.1A III wPt-1924-wPt-734000 15.4 11.0-19.9 4.6 3.3 211.9 8.0 QGY.ndsu.1D III, IV, AE wPt-733835-wPt-3945 90.5-129.5 66.5-140.5 2.9-3.6 3.3-3.4 142.2-176.6 5.0-8.5 QGY.ndsu.1D IV wPt-4497-wPt-5253 151.1 140.7-153 3.8 3.3 169.6 5.7 QGY.ndsu.2A1 AE wPt-668027-wPt-798459 30.51 24.6-44.7 4.9 3.4 215.7 9.8 QGY.ndsu.2A2 AE wPt-664128-wPt-7721 113.5 95.9-121.7 3.6 3.4 133.2 6.9 QGY.ndsu.2D1 I, III, IV, AE wPt-668017-wPt-4242 82.3-89.7 71.8-92.7 4.7-20.0 3.3-3.4 198.6-439.6 10.6-40.3 QGY.ndsu.2D2 III wPt-3692-wPt-6780 126.7 120.8-131.9 4.9 3.3 218.8 8.7 QGY.ndsu.3A AE wPt-741816-wPt-743909 3.6 0-11.3 3.5 3.4 -112.1 4.6 QGY.ndsu.6B IV wPt-744396-wPt-2218 139.4 114.1-147.1 4.0 3.3 -168.4 6.1 RTPVE†† 8.8-58.6
Plant height
QPH.ndsu.1B.1 II, VI wPr1560-wPt-0308 4.5-5.5 0-16.4 3.8-6.6 3.3-3.4 3.3-3.3 7.8-14.7
122 QPH.ndsu.1B.2 IV, V, AE wPt-671415-wPt-664989 0.0 0-14.6 3.2-3.3 3.3-3.4 2.0-2.6 5.9-6.3 QPH.ndsu.1D III, IV, AE wPt-672077-wPt-730172 119.0-120.0 107.2-135.5 2.8-4.7 3.1-3.4 -2.7_-4.1 9.0-15.9 QPH.ndsu.2D V, AE wPt-5014-wPt-731406 59.4-76.8 48.5-81.5 2.9-9.6 3.3-3.4 2.0-3.9 5.3-19.1 QPH.ndsu.4B III, IV, V, VI, AE wPt-732423-wPt-744434 23.0-29.6 11.6-42.3 2.81-7.3 3.1-3.4 -2.0_-3.5 5.3-13.8 QPH.ndsu.5B V wPt-6191-wPt-6014 141.8 122.3-158.0 3.4 3.3 2.4 8.5 QPH.ndsu.6B.1 VI wPt-6116-wPt-1541 1.0 0-26 3.4 3.3 -2.4 6.9 QPH.ndsu.6B.2 II wPt-8412-wPt-9971 129.7 121.1-137.8 6.3 3.4 4.5 13.9 QPH.ndsu.7B.1 II wPt-0920-wPt-1587 2.1 0-8.5 5.2 3.4 4.0 10.8 QPH.ndsu.7B.2 III, IV, AE wPt-9299-wPt-1266 123.7-124.0 112.0-129.8 4.2-5.4 3.1-3.4 -2.6_-3.4 8.4-10.8 RTPVE†† 26.9-47.3
Lodging
QLd.ndsu.1A VI wPt-729832-wPt-3698 67.1 62.6-74.5 5.6 3.4 -6.5 12.4 QLd.ndsu.2A.1 III wPt-2850-wPt-8068 89.7 81.5-106.3 5.6 3.4 -11.8 18.3 QLd.ndsu.2A.2 V wPt-668027-wPt-798459 29.5 24.2-34.6 5.0 3.1 6.6 22.8 (Continues)
Table 3-6. QTL identified for agronomic traits in a RIL population of hexaploid wheat derived from the cross of WCB414 and WCB617 (Continued) QTL Environment† Flanking markers Pos.‡(cM) CI§(cM) LOD Thresh¶. a# R2(%) Lodging
QLd.ndsu.3A III tPt-1079-wPt-1464 36.3 14.0-43.8 4.6 3.4 7.9 9.4 QLd.ndsu.3D I, AE wPt-742339-wPt-740580 7.9-8.9 0.4-21.9 3.5-7.3 3.3 6.8-12.6 11.9-20.3 QLd.ndsu.4B I, VI, AE wPt-732423-wPt-8892 20.0-23.0 12.1-36.8 3.7-7.6 3.3 -6.5_-10.6 8.2-18.3 QLd.ndsu.6B AE wPt-6247-wPt-741530 166.9 159.0-178.8 5.2 3.3 5.1 10.5 QLd.ndsu.7B AE wPt-742417-wPt-743215 34.2 28.4-35.4 3.8 3.3 -4.5 7.4 RTPVE†† 20.1-56.5
Days to heading
QDH.ndsu.1A.1 III, IV, AE wPt-1924-wPt-667180 15.4-20.6 10.7-26.5 5.6-6.3 3.2 -1.0_-1.4 9.2-10.9 QDH.ndsu.2D I, II, III, IV, V, AE wPt-3812-wPt-5014 45.5-76.1 27.4-78.0 3.7-10.9 3.2-3.4 -0.7_-1.3 6.0-21.1 QDH.ndsu.3A I, III, V, AE wPt-6854-wPt-0286 112.5-128.2 96.6-148.2 2.7-6.6 3.2-3.4 1.2-1.9 8.4-15.8 QDH.ndsu.3B.1 IV wPt-667895-wPt-6834 39.0 27.2-44.3 5.4 3.2 -0.9 10.3 QDH.ndsu.3B.2 V wPt-731663-wPt-4412 74.3 66.1-80.0 3.5 3.3 -1.2 6.9 QDH.ndsu.3D I, AE wPt-740580-wPt-6169 29.1-41.2 6.8-46.9 2.9-4.7 3.2-3.3 -0.8_-1.4 5.8-11.1
123 QDH.ndsu.6A II wPt-731592-wPt-9089 3.2 0-12.7 3.8 3.4 0.6 6.7 QDH.ndsu.7B V wPt-9515-wPt-9013 23.8 17.8-31.8 5.3 3.3 -1.4 8.9
RTPVE†† 26.4-43.2
Days to maturity
QDM.ndsu.1A III wPt-666424-wPt-667458 3.1 0-6.9 3.8 3.2 -0.7 7.7 QDM.ndsu.3A.1 I, III, IV, V, VI AE wPt-6854-wPt-0286 111.5-134.2 98.1-148.0 2.9-7.6 3.2-3.3 0.6-2.1 8.2-25.0 QDM.ndsu.3A.2 III wPt-1562-wPt-2740 161.4 150.2-165.9 4.6 3.2 0.9 12.9 QDM.ndsu.3B V wPt-731663-wPt-4412 75.3 67.0-80.6 3.8 3.2 -1.2 9.2 QDM.ndsu.3D I, AE wPt-6169-wPt-9258 43.7-45.7 36.4-46.9 5.0-5.4 3.3 -0.8_-1.4 9.2-11.7 QDM.ndsu.7A.1 IV, VI, AE wPt-2083-wPt-1601 75.4-80.4 52.9-93.9 4.4-5.5 3.2-3.3 0.8-1.2 10.9-15.4 QDM.ndsu.7A.2 VI, AE wPt-2371-wPt-3059 29.6-30.2 21.9-42.5 4.1-4.6 3.3 0.7-1.1 7.5-9.3 QDM.ndsu.7A.3 I wPt-3135-rPt-4199 72.2 59.3-87.6 3.8 3.3 1.0 7.5 (Continues)
Table 3-6. QTL identified for agronomic traits in a RIL population of hexaploid wheat derived from the cross of WCB414 and WCB617 (Continued) QTL Environment† Flanking markers Pos.‡(cM) CI§(cM) LOD Thresh¶. a# R2(%) Days to maturity
QDM.ndsu.7B.1 V wPt-9515-wPt-9013 23.8 23.7-26.1 5.5 3.2 -1.4 11.0 QDM.ndsu.7B.2 IV wPt-4025-wPt-0920 0.0 0-7.8 4.7 3.2 0.6 10.1 RTPVE†† 28.7-52.7 †I, Prosper 2009; II, Carrington 2009; III, Prosper 2010; IV, Carrington 2010; V, Prosper 2011; VI, Carrington 2011. ‡Position §Confidence Interval ¶Thresold calculated by permutation test. #Additive effects ††rank of phenotypic variation explained per environment
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Composite interval mapping analysis for DM resulted in the identification of one consistent QTL
(QDM.ndsu.3A1) and seven putative QTL (QDM.ndsu.1A, QDM.ndsu.3A.2, QDM.ndsu.3B,
QDM.ndsu.3D, QDM.ndsu.7A.1, QDM.ndsu.7A.2, QDM.ndsu.7A.3, QDM.ndsu.7B.1 and QDM.ndsu.7B.2)
(Table 3-6, Fig.3-2). The QTL QDM.ndsu.3A.1and QDM.ndsu.7A.1 were major and explained up to
24.97% and 15.41% of PV, respectively. Together, the QTL associated with DM explained 28.7-52.7% of
PV in different environments. The elite parent WCB414 provided alleles that increased the phenotypic values of DM at QDM.ndsu.3A1, QDM.ndsu.3A.2, QDM.ndsu.7A.1, QDM.ndsu.7A.2, QDM.ndsu.7A.3, and QDM.ndsu.7B.2; while the branched parent WCB617 contributed with alleles that increased DM at
QDM.ndsu.1A, QDM.ndsu.3B, QDM.ndsu.3D, and QDM.ndsu.7B.1.
In this RIL population, the segregation for PH was controlled by one consistent QTL
(QPH.ndsu.4B) and nine putative QTL (QNS.ndsu.1B.1, QPH.ndsu.1B.2, QPH.ndsu.1D, QPH.ndsu.2D,
QPH.ndsu.5B, QPH.ndsu.6B.1, QPH.ndsu.6B.2, QPH.ndsu.7B.1 and QPH.ndsu.7B.2) (Table 3-6, Figure
3-2). Two major QTL were identified for PH, one each on chromosomes 1D and 2D. The QTL on chromosome 1D (QPH.ndsu.1D) explained up to 15.90% of PV, while the QTL on chromosome 2D explained up to 19.12% of PV for PH. The minor QTL explained between 5.28% and 14.70% of PV for
PH. Together these QTL explained 26.9-47.3% of PV. The alleles for increased PH at QPH.ndsu.1B1,
QPH.ndsu.1B2, QPH.ndsu.2D, QPH.ndsu.5B, QPH.ndsu.6B2, and QPH.ndsu.7B1 loci were contributed by WCB414; while WCB617 provided alleles for increased PH at QPH.ndsu.1D, QPH.ndsu.4B,
QPH.ndsu.6B1 and QPH.ndsu.7B2 loci.
Composite interval mapping for Ld resulted in the detection of one consistent (QLd.ndsu.4B) and seven putative QTL (QLd.ndsu.1A, QLd.ndsu.2A.1, QLd.ndsu.2A.2, QLd.ndsu.3A, QLd.ndsu.3D,
QLd.ndsu.6B and QLd.ndsu.7B) (Table 3-6, Figure 3-2) and explained 20.1-56.5% of PV depending of environments. Among these QTL, QLd.ndsu.2A.1, QLd.ndsu.2A.2, QLd.ndsu.3D and QLD.ndsu.4B have major effect and explained up to 18.3%, 22.8%, 20.3% and 18.3% of PV, respectively. The elite parent
(WCB414) contributed with alleles for increased Ld at QLd.ndsu.2A.2, QLd.ndsu.3A, QLd.ndsu.3D, and
QLd.ndsu.6B loci, while WCB617 provided alleles that increased Ld at QLd.ndsu.1A, QLd.ndsu.2A1,
QLd.ndsu.4B, and QLd.ndsu.7B loci.
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3.4.6. Distribution of QTL at whole genome level and important QTL clusters
A total of 145 QTL on 17 chromosomes were identified in this study. Out of these, 40 were consistent QTL and 105 were putative QTL; meanwhile a total of 28 QTL were major (explained more than 15% of PV) and 117 QTL were minor QTL. A maximum of 16 QTL were identified on chromosome
1A in this study, followed by chromosomes 5B and 7B with 13 QTL each. Chromosomes 2A, 2D, and 6B had each 12 QTL; chromosomes 3A and 1D had 8 QTL each; chromosomes 4A, and 7A had 7 QTL each; chromosomes 1B, 3B, 4B and 6A had each 6 QTL; chromosomes 2B and 3D had 5 QTL each; and chromosome 5A had 3 QTL. No QTL were observed in the linkage groups belonging to chromosomes
5D, 6D and 7D. The B-genome had the highest number of QTL (61), followed by A-genome (59), and D- genome (25).The largest clusters of consistent QTL were located on chromosomes 1A and 2D. On chromosome 1A eight QTL were detected on the short arm (QLPP.ndsu.1A1, QLPP.ndsu.1A2,
QSL.ndsu.1A1, QNd.ndsu.1A.1, QDH.ndsu.1A.1, QDM.ndsu.1A, QGY.ndsu.1A, and QKS.ndsu.1A) and eight QTL on long arm. In chromosome 2DS eight QTL (QKSk.ndsu.2D, QNNdISk.ndsu.2D,
QGY.ndsu.2D1, QDH.ndsu.2D, QSL.ndsu.2D, QNd.ndsu.2D, QNdD.ndsu.2D, QALT.ndsu.2D) were grouped close to the major QTL for SS (QSS.ndsu.2D).
Other clusters of at least three consistent QTL were observed on 1DL (QGY.ndsu.1D,
QPH.ndsu.1D, and QNd.ndsu.1D), 2A (QNS.ndsu.2A, QAless.ndsu.2A.1 and QAless.ndsu.2A.2), 4BL
(QPH.ndsu.4, QLd.ndsu.4B, and QSL.ndsu.4B), and 5BS (QALM.ndsu.5B, QALT.ndsu.5B,
QAAL.ndsu.5B and QPP.ndsu.5B).
3.5. Discussion
3.5.1. Phenotypic variation
The cross between a branched and pubescent exotic line (WCB617) with a glabrous and conventional-spike-type elite line (WCB414) resulted in the segregation of a large number of spike-related traits. Most of these traits had transgressive segregation, and in the cases of PC and Aless traits their expression was absent in the parents. This situation reveals the presence of new allelic combinations which are usually absent in the elite × elite cross where most of the alleles are fixed.
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Genotypes with the clavate trait also expressed fusiform spikes, while some RIL with glume pubescences had plants with glabrous spikes. A similar situation known as heterobranching was described for the SS trait in this population (Chapter 2). The presence of seed admixture as a cause for these phenomena was discarded considering that the seeds from each genotype were derived from individual head-rows. To the best of our knowledge, this was not reported previously (for the presence of clavate architecture and glume pubescences). Therefore, we named theses novel phenotype as hetero- clavate and hetero-pubescent phenotypes and the causes of their expression need to be clarified.
This was the first time that a RIL population segregating for SS was used to study spike-related and agronomical important traits. With some exceptions, PSS and PP had the same type of correlations with other traits (Table 3-2, and Table 3-4). Both traits are positively correlated with SL, Nd, NdD, NNdISk, and DH; but negatively associated to awn lengths, NS, KSk, TKW and GY. These results demonstrate a poor agronomic performance of branched lines and pubescent lines. Previous studies also indicated a poor agronomic performance of genotypes with SS (Percival, 1921; Koric, 1973; Millet, 1986, 1987; Hucl and Fowler, 1992; Zhang et al., 2012).
The genetics of Nd have been studied previously by counting the number of spikelets per spike
(Sk) (Araki et al., 1999; Kato et al., 2000; Sourdille et al., 2000a; Li et al., 2002; Sourdille et al., 2003;
Jantasuriyarat et al., 2004; Kumar et al., 2007; Li et al., 2007; Chu et al., 2008; Cui et al., 2012). In these studies, the spikes had one spikelet per rachis node making Nd and Sk equivalent. In our study, however,
RILs with SS have different value for Nd and Sk. In fact, we studied the genetics of Sk as a component of
SS in a previous investigation (Chapter 2). Likewise, the trait NdD is comparable to the trait spike density
(SD) or compactness reported in previously (Sourdille et al., 2000b; Sourdille et al., 2003; Jantasuriyarat et al., 2004; Marza et al., 2006; Ma et al., 2007; Chu et al., 2008; Cui et al., 2012). Spike density is determined as the ratio between Sk and SL. However, based on our previous work (Chapter 2), where it was considered SS and SD as the same trait, we determined NdD as the ratio between Nd and SL.
3.5.2. QTL for spike-related traits
In the present study, the identification of relatively few major and several minor genes for all spike-related traits shows that these traits have a polygenic control. The detection of some consistent and a number of environment specific QTL for all those traits suggests that the inheritance of these traits are
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also influenced by the environmental factors. Quantitative genetic control and/or influence of environment on these traits is in agreement with previous studies (Araki et al., 1999; Kato et al., 2000; Sourdille et al.,
2000a; Börner et al., 2002; Li et al., 2002; Sourdille et al., 2003; Jantasuriyarat et al., 2004; Verma et al.,
2005; Marza et al., 2006; Kumar et al., 2007; Ma et al., 2007; Chu et al., 2008; Heidari et al., 2011; Cui et al., 2012).
Overlapping between positions and/or confidence intervals of QTL for SL and Nd were observed in this population (Table 3-5; Fig. 3-2) demonstrating a close linkage between genes and/or pleiotropic effects of a gene on these spike-dimension-related traits. These situations were observed at
QSL.ndsu.1A.1and QNd.ndsu.1A.1; QSL.ndsu.1A.2and QNd.ndsu.1A.2; QSL.ndsu.2D and
QNd.ndsu.2D; and QSL.ndsu.4B and QNd.ndsu.4B. This confirms the positive correlations observed between SL and Nd (Table 3-2). QSL.ndsu.1A1and QNd.ndsu.1A1 were consistent QTL for SL and Nd identified on 1AS (Table 3-5; Fig 3-2). Interestingly, chromosome 1A has been recognized in most of the previous (Sourdille et al., 2000; Li et al., 2002; Marza et al., 2006; Kumar et al., 2007; Ma et al., 2007;
Heidari et al., 2011) as a carrier of SL genes; whereas the presence of genes associated with Nd on chromosome 1A only was reported in two previous studies (Heidari et al., 2011; Cui et al., 2012).
Although the QTL, QSL.ndsu.2D and QNd.ndsu.2D were consistent for these spike-dimension traits,
QNd.ndsu.2D was a major QTL while QSL.ndsu.2D was minor QTL. The position of these QTL on 2DS chromosome coincides with previous QTL found for SL and Nd (Sourdille et al., 2000b; Li et al., 2002;
Verma et al., 2005; Kumar et al., 2007). Certainly, these QTL are located on a highly rich gene region of the wheat genome (Erayman, 2004) in which the major gene for SS was also located in this population
(Chapter 2). On the other hand, although QSL.ndsu.4B and QNd.ndsu.4B were also detected in the same position on 4BS, it is important to note that QSL.ndsu.4B was a consistently detected in two environments and AE; while QNd.ndsu.4B was detected in Prosper 2010 and AE only (Table 3-5; Fig 3-2). We believe this is the first time that QTL for SL and Nd are detected on chromosome 4B.
Unique SL and Nd QTL were observed on 2BS (QSL.ndsu.2B), 4AL (QSL.ndsu.4A,
QNNd.ndsu.4A), 1DL (QNd.ndsu.1D), and 2A (QNNd.ndsu.2A). Among these QTL, it is worth to focus on
QSL.ndsu.4A and QNd.ndsu.1D. The putative QTL for SL located on 4AL, QSL.ndsu.4A, was a major
QTL that explained up to 17.8 % of PV. Most of the previous QTL-mapping studies for SL have also
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detected a QTL for this trait on 4AL (Börner et al., 2002; Li et al., 2002; Jantasuriyarat et al., 2004; Kumar et al., 2007; Chu et al., 2008). Meanwhile, QNd.ndsu.1D was a minor QTL, but it was consistently detected in Prosper 2009, 2010 and AE. This QTL was located in a cluster of genes which will be discussed later. At the QTL associated to SL and Nd, the exotic parent WCB617 contributed the alleles that increased phenotypic values of these traits. This is consistent with the differences for SL and Nd between the parents, which resulted in favor of WCB617 in all the environments (Appendix Table B1).
The identification of one QTL associated to NdD on chromosome 2D (QNdD.ndsu.2D) is in agreement with previous reports by Sourdille et al. (2000b, 2003), Ma et al. (2007), Heidari et al. (2010), and Cui et al. (2012). However, the PV explained by QNdD.ndsu.2D is higher compared to the previous studies. QNdD.ndsu.2D explained up to 35.1% of PV (Table 3-5), while the maximum PV reported by
Sourdille et al. (2000b ), Ma et al., (2007), Heidari et al., (2011), Cui et al. (2012) were11.2%, 23.2%,
26.1%, 13.54%, respectively. The QTL on chromosome 2D was located in the same region where
QSS.ndsu.2D, a major QTL for SS was detected (Chapter 2). Interestingly, although NdD is derived from the ratio of Nd and SL, none QTL detected for NdD shared the same positions as QTL for Nd and SL.
Instead, NdD and PC shared a minor a putative QTL on 5BL (QNdD.ndsu.5B and QPC.ndsu.5B.1), which is in agreement with the fact that clavate spikes are produced by changes in spike density. The identification of QNdD.ndsu.5B on 5BL was confirmed by previous results reported by Ma et al. (2007) and Chu et al. (2008) for SD; while the discovery of QTL on chromosome 7B was also confirmed for SD by Marza et al. (2006). To our best knowledge, this is the first time that QTL for NdD and indirectly for SD
(for non-SS genotypes) are reported on chromosome 7A (QNdD.ndsu.7A1, QNdD.ndsu.7A2).
The PC was expressed in a total of 27 RIL but was not expressed by either parent. Therefore we assumed that this trait is the result of recombination process between WCB414 and WCB617 and epistatic interactions. The identification of seven QTL associated with PC (QPC.ndsu.1B, QPC.ndsu.4A,
QPC.ndsu.3B1, QLPC.ndsu.3B2, QPC.ndsu.5B1, QPC.ndsu.5B2 and QPC.ndsu.6B1) suggests that this trait is quantitatively inherited. To the best of our knowledge, this is also the first time that clavate spike architecture was studied and genetically dissected in common wheat. The most similar studies on clavate-like spike have been conducted on club wheat sub-species. In club wheat, the spikes are uniformly dense, short, stiff, compacted, with a spike length of 3.5 to 6 cm with 17 to 25 closely packed
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spikelets (Percival, 1921). The spike architecture of club wheat is determined by the C locus which has been located close to the centromere of chromosome 2D. Apparently in common wheat, the C locus is monomorphic because no studies located QTL associated with spike morphology in the same location as
C locus (Johnson et al., 2008). The absence of QTL for PC in chromosome 2D indicate that the clavate architecture is not controlled by the C locus. Indeed, the identification of seven QTL associated with PC is in agreement with results reported in many papers that indicate that spike architecture in common wheat is controlled by multiples QTL scattered through the genome. Although none of the QTL associated with
PC explained more that 15% of PV, it is worth to highlight the consistency observed for QPC.ndsu.4A across the environments. Most likely, QPC.ndsu.4A is the most important QTL for PC, but the estimation of the effects of this QTL could be influenced by experimental error or environmental conditions that limited the expression of this trait in other RILs.
Despite the fact that the genetics of pubescences on glumes in wheat has been studied for almost one century, few studies have dissected this trait using molecular markers. In the present study, although one major QTL and seven minor QTL associated to PP were identified, PVE by these QTL suggests that this trait is mainly controlled by one gene located on the short arm of chromosome 1A
(QPP.ndsu.1A1) (Table 3-5). Previous studies have also shown that hairy glumes are controlled by one gene (Hg) located on 1AS in Triticum (Briggle and Sears, 1966; McIntosh and Bennett, 1978; Blanco et al., 1998; Khlestkina et al., 2000; Spielmeyer and Richards, 2004; Khlestkina et al., 2006). Additionally, interactions of the minor QTL located on 1AS, 4AL, 5BS, and 6BS with QPP.ndsu.1A1 could explain the hetero-pubescent behavior observed in some RIL.
The phenotypic dissection of awn length into three spikes areas (ALB, ALM, ALT) detected more
QTL than a simple average of all the awn measurements (AAL). The traits ALB, ALT, and ALM resulted in the detection of 7, 7, and 8 QTL, respectively; while for AAL, only 4 QTL were detected (Table 3-5; Fig 3-
2). Common QTL were observed among the different awn measurements. However, only one QTL located on chromosome 7B was common across the different awn measurements. This finding suggests that chromosome 7B carries a gene controlling awn length across the entire spike. The traits ALT and
ALM shared QTL at chromosomes 5A (QALT.ndsu.5A and QALM.ndsu.5A) and 5B (QALT.ndsu.5B and
QALM.ndsu.5B). The QTL on chromosome 5B was also detected for AAL trait suggesting its importance
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in the determinations of the awn length especially at the top and middle of the spike. Similarly, ALB and
AAL had a common QTL on chromosome 6A (QALB.ndsu.6A and QAAL.ndsu.6A). Among the QTL detected for only one awn-related trait, QALB.ndsu.2D detected for ALT, seems important as it was detected in all the environments and explained up to 20% of PV.
The presence of four apically awnleted RIL in this experiment was not expected considering the awned phenotype of both parents. Composite interval mapping determined that two QTL on chromosome
2A (QAless.ndsu.2A1 and QAless.ndsu.2A2) were associated to this trait. The position of
QAless.ndsu.2A1coincides with the position of the exclusive QTL for ALB named QALB.ndsu.2A. The elite parent (WCB414) provided the alleles for the awn inhibition in the apically awnleted phenotype as well as the alleles for a reduction in the length of the awns at the bottom of the spike (ALB). Given that this parent is awned, we believe that an undetermined genetic process should be responsible of the increased expression of this gene in the four apically awnleted RIL.
In wheat, the genes Hd, B1, and B2 located on chromosomes 4AS, 5AL, and 6BL respectively, have been previously associated with awness (Rao 1981). Moreover, the presence of three QTL on chromosomes 2DS, 4AS and 6BL associated with ALB, ALM, ALT and AAL have also been eported
(Sourdille et al., 2002; Sourdille et al., 2003). QTL located on chromosome 4AS and 6BL have been found to co-segregate with the genes Hd and B2, respectively (Sourdille et al., 2002). The present study also identified QTL associated with awns on 2DS, 5AL and 6BL (QALT.ndsu.2D, QALT.ndsu.5A,
QALM.ndsu.5A, and QALM.ndsu.6B.1). However, contrary to previous studies, the present research identified QTL on 1AL, 1B, 2AL, 3A, 4AL, 4BL, 5BS, 6AS, 6BS and 7BL demonstrating the polygenic control of awn length in wheat. Nevertheless, it is worth to point out that this is the first time that a RIL population derived from a branched and awned parent and an awned elite line was used to study the genetics of awn length and awn inhibition. Therefore, differences between our results and previous studies may be explained by differences in the germplasm studied.
3.5.3. QTL for yield and yield components
The identification of multiple QTL for GY and its components KSk, KNd, KS, NNdISk, and NS
(Table 3-6, Fig 3-2) is in accordance with previous studies that identified a polygenic control of these traits
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and/or high effects of the environment on their expression (Kato et al., 2000; Börner et al., 2002; Gross et al., 2003; Huang et al., 2003; Huang et al., 2004; McCartney et al., 2005; Verma et al., 2005; Marza et al.,
2006; Kuchel et al., 2007; Kumar et al., 2007; Li et al., 2007; Ma et al., 2007; Cuthbert et al., 2008; Wang et al., 2009; Heidari et al., 2011; Bennet et al., 2012a; Cui et al., 2012; Mergoum et al., 2013). The number of kernels per spikelet is an indirect measurement of floret fertility. The genetics of kernels per spikelet has been traditionally studied through progeny analysis (Keteta et al., 1976; Sidwell et al., 1976;
Sayed., 1978; Ibrahim et al., 1983), but there are limited studies reporting the genetic dissection of this trait using molecular markers (Bennet et al., 2012a). Although an earlier study for KSk conducted under water-limited environments reported six QTL, none of them were located on chromosomes 2BL, 2DS,
6BL and 7BL where QTL were identified in the present study. This could be due to either different genetic material used in these studies or to differences in environmental conditions. Most of the alleles that contributed to increase KSk were derived from the elite parent WCB414. This was expected considering that WCB414 had always superior values of KSk compared to WCB617 (Appendix Table B2) and for which PSS and KSk were inversely correlated (Table 3-2).
Considering that branched spikes have several spikelets per rachis node, we analyzed the number of kernels per node. Considering that conventional spikes (non-SS) have the same phenotypic value for KSk and KNd, it was expected to discover common QTL between these traits. However, only
QKSk.ndsu.6B and QKNd.ndsu.6B (detected in only one environment) shared the same position. This result suggests an independent genetic control of these traits. Among the QTL associated with KNd, only
QKNd.ndsu.1A was consistently detected in 50% of the environment studied and had the largest percentage of PVE (up to 11.31%). This QTL was located in a QTL cluster on 1AL together with QTL for spike-related traits.
Kernels per spike (KS) is an important yield component and was studied widely in the past. For this trait, the identification of one consistent QTL on 1AS (QKS.ndsu.1A) and 4AL (QKS.ndsu.4A) as well as two putative QTL on 1B (QKS.ndsu.1B) and 2DS (QKS.ndsu.2D) coincides with the findings of previous studies (Börner et al., 2002; Marza et al., 2006; Cuthbert et al., 2008; Wang et al., 2008; Bennett et al., 2012a). QKS.ndsu.4A is co-located with QKNd.ndsu.4A and QNd.ndsu.4A, two putative QTL for
KNd and Nd (Fig. 3-2), suggesting pleiotropic effect of this genetic region on these traits. Interestingly, we
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found that the branched parent (WCB617) is the source of the alleles that increased KS at QKS.ndsu.1A and QKS.ndsu.4A. Considering the consistency of these QTL, these alleles could be of great interest for breeding programs aimed to increase KS.
The spikelets in the middle of the wheat spikes are developed before the other spikelets at the bottom or top (Sharman, 1967). Hence, it is common to observe immature spikelets at the spike base.
The genetics of immature spikelets have received the attention of some studies considering their impact on the number of grain per spikes and ultimately on grain yield (Ma et al., 2007; Li et al., 2007). The trait
NNdISk assessed in this study is comparable to the basal sterile spikelet number studied in conventional spikes (Ma et al., 2007; Cui et al., 2012). In the branched spikes studied in this population, the presence of a large number of immature spikelets at the bottom of the spike is obvious, but it is often not possible to distinguish immature spikelets from others at each rachis node. Therefore, it was decided to count the nodes with immature spikelets instead of the number of spikelets. The location of consistent QTL for
NNdISk on 1AL (QNNdISk.ndsu.1A) 2DS (QNNdISk.ndsu.2D) is in agreement with a previous study by
Cui et al. (2012).
Fourteen QTL related to NS were identified, the largest number of QTL found for a trait in this study. However, none of these QTL were consistently detected in 50% of the 6 environments of the study.
Only four QTL were consistent in more than one environment, which suggests a high impact of the environment on the expression of the genes associated with NS. Among these putative QTL for NS, three of them explained more than 15% of PV in some environments. This was the cases of QNS.ndsu.2A,
QNS.ndsu.5A, and QNS.ndsu.7A which explained 15.9% 15.6% and 20.6% of PV, respectively. The location of these chromosomes on chromosomes 2A, 5A and 7A confirms previous findings (Huang et al.
2003; Huang et al. 2004;Li et al., 2007; Cuthbert et al., 2008; Heidari et al., 2011).In general, the alleles which increased NS were derived from the elite parent (WCB414). This result suggest that the alleles from the branched parent (WCB 617) are associated with poor tiller capacity, which confirm the negative correlation observed between PSS and NS (Table 3-4) observed previously (Percival, 1921; Hucl and
Fowler, 1992).
The location of a consistent QTL associated with GY on 1DL (QGY.ndsu.1D) has been not reported previously. This minor QTL overlapped with QTL for PH (QPH.ndsu.1D) and Nd (QNd.ndsu.1D).
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On the other hand, the location of a QTL for GY on 2DS (QGY.ndsu.2D1) confirms previous genetic analyses (Huang et al. 2003; Narasimhamoortht et al. 2006; Kumar et al., 2007). Indeed, this QTL is very close to the major gene for SS (QSS.ndsu.2D) and explained up to 40.3% PV. In fact, only one study
(Kumar et al., 2007) had reported a QTL on chromosome 7A explaining more than 40% of PV of GY in common wheat.
3.5.4. QTL for other important agronomic traits
In addition to yield and its components, other important agronomic traits considered by breeders are PH, Ld, DH and DM. These traits play an important role in cultivar adaptation and ultimately in grain yield. During the last 15 years, a large number of QTL studies have shown that PH, Ld, DH and DM are controlled by several loci located throughout the wheat genome, whose expression is influenced by environmental conditions (Cadalen et al., 1998; Araki et al., 1999; Keller et al., 1999; Kato et al., 1999;
Ahmed et al., 2000; Börner et al., 2002; Sourdille et al., 2000a ; Sourdille et al., 2003; Huang et al., 2003;
Huang et al., 2004; McCartney et al., 2005; Verma et al., 2005; Huang et al., 2006; Marza et al., 2006;
Narasimhamoorthy et al., 2006; Chu et al., 2008; Cuthbert et al., 2008; Wang et al., 2009; Bennet et al.,
2012a).The positive correlations observed between PH and Ld (Table 3-3) can be expected, since tall plants are usually more susceptible to lodging. This result is in agreement with the overlapping position of
QPH.ndsu.4B and QLd.ndsu.4B on 4BS. These consistent QTL explained up to 13.8% of PV of PH and up to 18.3% of PV of Ld. The position of QPH.ndsu.4B and QLd.ndsu.4B coincides with the position of the major gibberellic acid insensitive semi dwarf gene Rht-B1b (Rht1) (Cadalen et al., 1998, McCartney et al., 2005a; Cuthbert et al., 2008). Interestingly, QPH.ndsu.4B and QLd.ndsu.4B are also overlapping with a consistent QTL for SL (QSL.ndsu.4B) and a putative QTL for Nd (QSL.ndsu.4B). This result suggests that selection for semi dwarf alleles may also be coupled with reduced spike dimensions. The reduced spike size observed in the checks (commercial varieties) as well as in the elite parent is probably caused by this gibberellic acid insensitivity gene. However, the inability to detect any QTL for plant height on chromosome arm 4DS where another major gene for plant height Rht-D1b (Rht2) is located (Sourdille et al., 1998, McCartney et al., 2005a), suggest that either there was no polymorphism for that locus in the population or Rht-D1b (Rht2) has no role in the genetic control of PH in this populations. On the other
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hand, the detection of other QTL not shared between PH and Ld suggests that other biological process may be involved in the regulation of these traits. For instance a new QTL was detected on chromosome
1A for Ld, which was not detected for PH.
Wheat genotypes with earlier DH are expected to mature early in general; therefore the positive associations observed between DH and DM were expected (Table 3-3). We observed that positions and/or CI for QTL associated to DH and DM overlapped on 3A, 3BL, 3D, and 7BL. QDH.ndsu.3A and
QDM.ndsu.3A.1 were consistent and major QTL that explained up to 15.8% and 25.0% of PV of DH and
DM, respectively. Previous studies (Huang et al., 2004) have also reported QTL associated with DH on
3A. Most likely, the 3A region corresponds to the gene associated with earliness per se (Eps) identified on this chromosome (Shah et al., 1999). Another important QTL for DH identified in our population was
QDH.ndsu.2D which was located on 2DS and was consistently identified in five environments and AE.
This is major QTL that explained up to 21.1% of PV of DH (Table 3-6). It is well known that chromosome
2D carries the photoperiod response gene (Ppd1) on 2DS (Scarth and Law, 1984; Millet, 1986, 1987; Xu et al., 2005), and several studies have reported QTL on 2DS associated with DH using SSR markers
(Börner et al., 2002; Huang et al., 2003; Narasimhamoorthy et al., 2006) and DArT markers (Sorrells et al., 2011; Bennet et al., 2012b). The flanking markers of QDH.ndsu.2D (wPt-3812 and wPt-5014) were also located on chromosome 2D at ≈ 17cM apart from Ppd1 by Sorrells et al. (2011). These results suggest that QDH.ndsu.2D may represent Ppd1 gene.
3.5.5. Genetic associations of spike pubescences and SS with other traits
On 1AS, a cluster of five consistent QTL for PP, SL, Nd, DH and KS (QPP.ndsu.1A1,
QSL.ndsu.1A1, QNd.ndsu.1A.1, QDH.ndsu.1A.1, and QKS.ndsu.1A) were detected suggesting a pleiotropic effect of this chromosome region on these traits. Considering that QPP.ndsu.1A1 was the major gene that explained up to 91.82% of PV of PP, it is possible that the co-location of these QTL corresponds to an effect on glume pubescences on SL, Nd, DH and KS. Certainly, PP was positively correlated with these traits (Table 3-2; and Table 3-4). The effect of glume pubescences on other wheat traits has scarcely been studied. Among these few studies, it was reported under growth chamber conditions that glume pubescences are associated with more kernels per spikelets (Maes et al., 2001).
Interestingly, we found that at QKS.ndsu.1A the alleles that increased the number of KS were derived
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from the pubescent parent. On the other hand, the positive association of glume pubescences with SL,
Nd, and DH should be considered in further studies.
In the long arm of chromosome 1A (1AL) we observed another cluster of consistent QTL for KNd
(QKNd.ndsu.1A), NNdISk (QNNdISk.ndsu.1A), and ALT (QALT.ndsu.1A). The co-location on 1AL of QTL for NNdISk and ALT is in agreement with the negative association between these traits (Table 3-2), suggesting a significant role of the awns at the top of the spikes on the number of immature spikelets at the spike bottom.
Using the same population, we have shown previously, (Chapter 2) that SS-related traits are mainly controlled by a major and consistent QTL located on 2DS (QSS.ndsu-2D), a major and putative
QTL located on 7B (QSS.ndsu-7B.2) and six minor QTL located on chromosome 5B, 6A, 6B and 7B
(QSS.ndsu-5B, QSS.ndsu-6A.2, QSS.ndsu-6B.1, QSS.ndsu-6B.2, and QSS.ndsu-7B.1). The position and/or CI of QSS.ndsu-2D are overlapping with the consistent QTL for NdD (QNdD.ndsu.2D), NNdISk
(QNNdISk.ndsu.2D), DH (QDH.ndsu.2D), and Ksk (QKSk.ndsu.2D) (Fig. 3-2). This finding suggests either a pleiotropic effect of the region associated with SS on these traits or a closely linked QTL; and it is in agreement with the correlations observed between PSS and these traits (Table 3-2 and Table 3-4).
Interestingly, previous studies reported that chromosome 2D of the multi-spikelet line “Noa” had in addition to the gene associated to a large number of spikelets, a gene for late heading date which is independent of the day-length sensitive gene ppd (Millet, 1986, 1987). Other consistent QTL were also shown to be closely linked to the major QTL associated with SS (QSS.ndsu-2D), demonstrating the impact of SS on other spike-related and agronomic traits of wheat. This was the case of QTL for SL
(QSL.ndsu.2D), Nd (QNd.ndsu.2D), ALT (QALT.ndsu.2D), and GY (QGY.ndsu.2D) which were clustered on 2DS. In addition, PSS was also correlated with these traits demonstrating that branched spikes tend to be long, with a high number of nodes, reduced awn length at the top of the spike, and poor grain yield
(Table 3-2 and Table 3-4). Among these associations, the association between PSS and GY is of interest, considering that QGY.ndsu.2D explained up to 40.3% of PV of GY. It could be possible that the branched genotypes improve their agronomic performance to break the linkage between negative alleles for GY at
QGY.ndsu.2D and alleles for SS at QPSS.ndsu.2D.
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3.6. Conclusion
A genetic study using a RIL population derived from a cross between an elite parent (WCB414) and an exotic non-adapted line with SS and pubescences (WCB617) resulted in the identification of 60 spike-related QTL and 85 agronomic QTL. This large number of QTL identified demonstrates the richness of using elite x exotic cross for the genetic analysis of traits in common wheat. The major QTL associated to SS located on 2DS identified in this study has either pleiotropic effect or it is closely linked to consistent
QTL for SL, Nd, NdD, ALT, NNdISk, DH, KSk, and GY which demonstrate a remarkable impact of the SS on spike morphology and ultimately on GY. Similarly, the influence of glume pubescences (PP) on SL,
Nd, DH and KS was supported by locating a cluster of consistent QTL for these traits on 1AS.
Considering the consistency across environments of several of the QTL reported in this study, the development of molecular markers from these loci could be valuable to increase genetic diversity of wheat breeding programs.
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CHAPTER 4. GENETIC DISSECTION OF QUALITY TRAITS IN WHEAT USING AN ELITE×
EXOTIC RIL POPULATION
4.1. Abstract
Recognizing new QTL and alleles in exotic germplasm is paramount for the future of wheat traits and particularly quality improvement. In the present study, a RIL population developed from a cross of an elite wheat elite line (WCB414) and an exotic genotype with supernumerary spikelets (SS) and pubescence (WCB617), was used to identify QTL and new alleles for eight quality traits. Composite interval mapping for thousand kernels weight (TKW), kernel volume weight (KVW), grain protein content
(GPC), percent of flour extraction (FE) and four mixograph-related traits identified a total of 69 QTL including 19 consistent QTL. These QTL were located on 18 different chromosomes. Thirteen of these
QTL explained more than 15% of phenotypic variation (PV) and were considered as major QTL. The exotic parent contributed positive alleles that increased PV of the traits at 56% of loci across the genome.
In this study we identified 12 QTL for TKW (R2 = 7.2%-17.1%); 10 QTL for KVW (R2 = 6.7%-22.5%); 11
QTL for GPC ( R2 = 4.7%-16.9%); six QTL for FE (R2 = 4.8%-19%) and 31 QTL for mixogram-related traits (R2 =3.2%-41.2%).The co-localization of QTL for SS and quality-related QTL suggests a pleiotropic effects among these genetic regions. A cluster of QTL for quality traits was observed on 6BS. Closely linked QTL suggest pleiotropic effects between some quality traits.
4.2. Introduction
Common wheat (Triticum aestivum L.) was in 2012 the cereal with most area harvested (about
216 million Ha), and the third cereal with highest production worldwide (about 720 million tons), surpassed only by maize and rice. In 2009, wheat was the main food supply crop in the world (66 kg/capita/year) and the most important source of proteins (16.2 g/capital/day) worldwide (FAO-FAOSTAT,
2014). These statistics make wheat breeding a paramount activity for food security in the world and for the dynamics of the global markets. Breeding programs of wheat have the objective to supply varieties for a diverse market and end-users including growers, millers, bakers, and consumers. This is a daunting challenge since selection for optimal characteristics for one of these customers could be detriment for
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others (Carena, 2009). Wheat breeders select the best genotypes through the assessment of a large number of agronomic and quality traits. Plant height, grain yield, disease resistance, and days to heading are examples of agronomic characters tested; while grain protein content, flour extraction, dough resistance and baking performance are quality traits considered in the selection of superior cultivars. The assessment of quality traits, at difference of agronomic traits, requires large samples, time, and specialized personal to conduct complex procedures. In addition, most of the quality traits have a quantitative inheritance and their expression is affected by environmental conditions (Campbell et al.,
2001; Nelson et al., 2006; Tsilo et al., 2010; Tsilo et al., 201; Simons et al., 2012) limiting the genetic gain though breeding cycles.
Molecular-assisted selection (MAS) has been presented as a shortcut for the development of premium cultivars (Nelson et al., 2006; Raman et al., 2009; Simons et al., 2012). This approach offers the opportunity for wheat breeders to select genotypes bypassing the assessment of quality traits. Therefore, in the last 15 years a broad number of genetic studies in wheat have been conducted to map QTL/genes associated with quality traits in wheat and identify suitable markers to use in MAS (Campbell et al., 2001;
Gross et al., 2003; Huang et al., 2006; Kuchel et al., 2006; McCartney et al., 2006; Nelson et al., 2006;
Kunert et al., 2007; Mann et al., 2009; Raman et al., 2009; Sun et al., 2010; Zhao et al., 2010; Tsilo et al.,
2011; Carter et al., 2012; Li et al., 2012a; Li et al., 2012b; Simons et al., 2012; Maphosa et al., 2013).
Recently, wheat breeding programs have started to take advantage of mass genotyping strategies such as Diversity array Technology (DArT) and Illumina infinium analysis, which permit the development of
(sutured) genetic maps identifying suitable QTL and alleles for quality traits (Raman et al., 2009; Tsilo et al. 2011; Simons et al. 2012; Kumar et al., 2013; Maphosa et al., 2013).
Among the quality traits, GPC has received special attention because it is an indicator of the performance of wheat derived products (Zhao et al., 2010). In addition, wheat markets are determined by the percentage of protein in the grain (Regional Quality Report, 2011). Several studies have genetically dissected this trait and reported the existence of genes associated with this trait on all wheat chromosomes (Galande et al., 2001; Gross et al., 2003; Prasad et al., 2003; Sourdille et al., 2003; Huang et al., 2006; Kunert et al., 2007; Mann et al., 2009; Nelson et al., 2006; Raman et al., 2009; Sun et al.,
2010; Tsilo et al., 2010; Zhao et al., 2010; Conti et al., 2011; Li et al., 2012a; Li et al., 2012b; Carter et al.,
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2012; Maphosa et al., 2013). In several of these studies, molecular markers associated with genes regulating gluten proteins have been reported. Gluten is the coherent mass formed by the binding of glutenin and gliadin (storage proteins) after water is added to flour (Stone and Savin, 1999). Glutenins are responsible for dough strength and are conformed by subunits of high molecular weight (HMW) and subunits of low weight (LMW). The major genes controlling HMW (Glu-1, Glu-A1, Glu-B1and Glu-D1) are located on the long arms of the homeologous group 1; while the major genes controlling LMW (Glu-A3,
Glu-B3, and Glu-D3) are also in the same homeologous group but in the short arm of these chromosomes. Gliadins, responsibly for dough viscosity, are controlled mainly by genes located on the short arm of homeologous groups 1(ω-gliadins and γ-gliadins) and 6 (α-gliadins, β-gliadins) (Payne
1987).
Wheat varieties with good grading standards such as thousand kernel weight (TKW) and kernel volume weight (KVW) usually has more flour extraction (FE) with high quality (Gwirtz et al., 2006). These important traits for millers have been also dissected genetically. Loci controlling TKW have been identified on all wheat chromosomes (Araki et al., 2001; Börner et al., 2002; Kato et al., 2000; Gross et al., 2003;
Huang et al., 2003; Huang et al., 2004; McCartney et al., 2005; Huang et al., 2006; Kumar et al., 2006; Li et al., 2007; Cuthbert et al., 2008; Wang et al., 2009; Sun et al., 2009; Tsilo et al., 2010; Heidari et al.,
2011; Simons et al., 2012; Bennett et al., 2012). For KVW also, QTL have been reported on all chromosomes except 1A and 6D (Campbell et al., 1999; McCartney et al., 2005; Narasimhamoorthy et al., 2006; Kunert et al., 2007; Huang et al., 2006; Sun et al., 2009; Sun et al., 2010; Bennett et al., 2012;
Simons et al., 2012; Carter et al., 2012). In the case of FE, QTL have been detected on chromosomes
1A, 1B, 2A, 3B, 4A, 4B, 4D, 5A, 5B, 5D, 6A, 6D and 7A (Campbell et al,. 2001; Kuchel et al., 2006;
Nelson et al., 2006; Raman et al., 2009; Carter et al., 2012; Simons et al., 2012; Maphosa et al., 2013).
Rheological properties govern the performance of wheat flour dough during mechanical treatment
(Alamri 2009a, 2009b). Mixograph, Farinograph, and Alveograph are used to assess these rheological characteristics giving an insight about baking performance (Gwirtz et al., 2006). Several studies have also reported QTL for rheological traits derived from Mixograph, Farinograph and/or Alveograph assessments
(Campbell et al., 2006; Huang et al., 2006; Mann et al., 2009; Tsilo et al., 2011; Li et al., 2012b; Simons et al., 2012; Mergoum et al. 2013, Maphosa et al. 2013). In the case of Mixograph, QTL have been found
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in all chromosomes except chromosomes 3D and 6B (Campbell et al., 2006; Huang et al., 2006; Mann et al., 2009; Tsilo et al., 2011; Li et al., 2012b; Simons et al., 2012; Mergoum et al., 2013, Maphosa et al.,
2013).
Although the recent information derived from QTL analysis suggests that the QTL/genes controlling wheat quality are contributed by whole-wheat genome, previous studies have suggested that
D-genome plays a pivotal role in bread wheat attributes (Kerber and Tipples, 1969; Nelson et al., 2006).
Due to the relatively recent addition of the D-genome into hexaploid wheat, the D-genome is the less diverse than other wheat genomes (Akunov et al., 2010). Consequently, wheat varieties show low polymorphism for D-genome. This could jeopardize future improvement of wheat quality traits, particularly for those loci located on D-genome. The use of the genetic diversity present in landraces and exotic wheat has been suggested as a mechanism to enrich wheat genetic pools (Raman et al., 2010). In the past, a combination of molecular markers and breeding approaches has allowed the identification and introgression of novel QTL present in exotic wheat lines and landraces (Nelson et al., 2006, Huang et al.,
2003; Huang et al., 2004; Narasimhamoorthy et al., 2006; Kunert et al., 2007; Naz et al., 2008). Wheat genotypes with supernumerary spikelets (SS) is an unexplored exotic germplasm for quality traits. Usually wheat bears one spikelet per rachis node, however, in some landraces; it is possible to find genotypes in which a rachis node has more than one spikelet (Sharman 1967; Martinek and Bednár 1998). This trait was reported to be controlled by a major gene located on chromosome 2D and some minor genes located on other chromosomes (Koric, 1973; Peng et al., 1998; Klindworth et al., 1990; Dobrovolskaya et al., 2009)(Chapter 2). Considering the high number of spikelets in which grains could be developed, SS have been suggested as a means to increase grain yield (Pennell and Halloran, 1983, 1984, Hucl and
Fowler. 1992). The quality performance of genotypes with SS is mostly unknown. In the present paper, a
RIL population derived from the cross of a white wheat (WW) genotype and an exotic line with SS, was used to genetically dissect eight quality traits. White wheat (WW) is a commercial alternative to hard red spring wheat (HRSW) in the northern plains of USA (Ransom et al., 2006) and is characterized by good- quality performance. The impact of genetic regions controlling SS on quality traits was studied.
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4.3. Material and Methods
4.3.1. Plant material
A RIL population derived from a cross between an elite genotype WCB414 and an exotic genotype WCB617 was used in this study. WCB414 is a white wheat (WW) genotype developed by the
Hard White and Specialty Wheat breeding program at North Dakota State University (NDSU), Fargo, ND
USA. This line was chosen for its adaptation and good-quality performance and has a conventional spike morphology. The genotype WCB617 is an exotic line with SS phenotype and glume pubescence, maintained by the NDSU wheat Germplasm Enhancement project as a source to enrich genetic diversity in wheat breading programs at NDSU. Single seed descent method was used to advance the RIL population to F7 generation. Afterward, plants were grown in greenhouse facilities at NDSU to increase seeds and advance the population to F8 generation. In the growing seasons of 2009, spikes from each plot were collected and grown in New Zealand winter nursery as head rows in order to ensure genetic purity of each RIL. Thus, in the years 2009 and 2010, the generations F7:9, and F10:11 were included in this study. The HRSW cultivars “Alsen” (PI 615543) (Frohberg et al. 2006), “Steele-ND” (PI 634981)
(Mergoum et al. 2005), “Glenn” (PI 639273) (Mergoum et al. 2006), “Faller” (PI 648350) (Mergoum et al.
2008), “Barlow” (PI 658018) (Mergoum et al. 2011), “Briggs” (PI 632970) (Devkota et al. 2007) and WW cultivar “Alpine” (Agripro® wheat variety, USA) were included as checks in this study.
4.3.2. Field experiment
During the years of 2009 and 2010, the parents, 163 RILs and seven checks were planted in a 13
× 13 partially balanced square lattice design with two replicates, at two different location, Prosper
(46.96300N, 97.01980 W, altitude 274 m, Bearden series soils) and Carrington (47.45000N, 99.12390 W,
484 m of altitude, Heimdal-Emrick series soils) in North Dakota (ND), USA. Planting and environmental conditions were previously described in Chapters 2 and 3. The environments were designated as:
I=Prosper 2009, II= Carrington 2009, III= Prosper 2010, IV= Carrington 2010.
4.3.3. Phenotypic data collection
The grain samples collected from the field experiments were cleaned by using a clipper grain cleaner before recording phenotypic data. The quality traits analyzed in this study were TKW, KVW, GPC,
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FE and mixograph related trait which include mixograph envelope peak time (MEPT), mixograph MID line peak time (MMLPT), mixograph MID peak integral (MMLPI) and general mixograph pattern (Mx). TKW (g) was obtained from the number of seeds in 10 g sample, counted using a seed counter (Seedburo
Equipment Co., Chicago, IL). KVW (kg m-3) was measured according to the American Association of
Cereal Chemist International (AACCI) method 55-10.01 (AACCI, 2008). Whole-grain GPC (%) in on a 12 percent basis was measured using Near-Infrared Reflectance following the AACCI standard method
39.25.01 (AACC International, 2008). FE was determined using 100 or 150 g grain sample tempered to
16.0% moisture. Brabender Quadrumat Junior Mill was used to mill the grain sample and bran was discarded from the flour. Flour extraction was reported on clean dry wheat basis (Bass, 1988). Mixograph measurements were obtained from 35 g of flour in a National Manufacturing Mixograph (National
Manufacturing, TMCO Division, Lincoln NE) following the AACCI 54-40.02 (AACC International 2008).
The Mixmart software was used to collect information of MEPT (min), MMLPT (min) and MMLPI (% torque x min). General Mixograph pattern (Mx) was based on a 0-9 scale of (0 = weakest; and 9,= strongest). The traits MEPT, MMLPT, MMLPI and Mx are referred as Mixograph-related traits in this study.
Due to small seed sample of WCB617 in Prosper 2009 and Carrington 2009, different procedures were used for estimating KVW and GPC. A miniature test weight device was used for KVW. The GPC was calculated from flour following the combustion method 46-30.01 of AACCI (AACC International 2008) using a Leco FP 528 (Leco Corporation 3000 Lakeview Avenue, St. Joseph MI). The information of flour protein content of these two samples was used as equivalent of GPC considering the close correlation between both traits. Data of FE and mixograph-related traits for WCB617 in Prosper 2009 could not be obtained due to the small seed sample.
4.3.4. Data analysis
Data from Prosper 2009 for FE and mixograph related traits were subjected to analyses of variance (ANOVA) for a random complete block design using the MIXED procedure of the Statistical
Analysis System (SAS) (SAS 2004). The data for FE and mixograph related traits from Carrington 2009,
Prosper 2010, and Carrington 2010, as well as data for TKW, TW and GPC from all the environments were subjected to ANOVA for a lattice design using the MIXED procedure of SAS (SAS 2004).
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Differences in the statistical analysis methodologies used in this study were due to missing data. ANOVA was estimated for each environment separately. To estimate genotype × environment interaction, combined ANOVA over location was performed. The Fmax test (Tabachnik and Fidell, 2001), considering a ratio of less than 10-fold, was conducted to verify homogeneity of variance before conducting combined
ANOVA analysis. In ANOVA analyses, the RILs, parents, and checks were considered as fixed effects; while environments and blocks were considered as random effects. F-tests were considered significant at p<0.05. Significant differences between genotypes were assessed using an F-protected least significant difference (LSD) value at p<0.05. Correlations between two traits were assessed using significant
Pearson coefficients at P<0.05. Procedures described by Gomez and Gomez (1984) were followed to test homogeneity among correlation coefficients of different environments at P<0.005. Pooled homogeneous correlation coefficients were considered significant at P≤0.05. Broad-sense heritability for each trait on plot-basis (Holland et al. 2003) was calculated excluding the means of parents and checks. The mixed procedure of SAS (SAS 2004) was used to conduct this analysis considering all sources of variation as
random component. The outputs of the covariance parameter estimates were used in the equation =
/[ + ( )+ ( )], where is the genotype variance, is the G × E, e is the number of environments and r is the number of replicates.
The evaluations of penetrance of supernumerary spikelets (PSS), penetrance of pubescences
(PP), penetrance of clavate architecture (PC) and apical awnleted expression (Aless) reported in Chapter
2 and Chapter 3 were correlated with the traits investigated in this study and co-located in the genetic map to identify the influence of these traits on quality traits. Other spike-related traits described in chapter
3 were co-located in the genetic map.
4.3.5. QTL analysis
The molecular map developed in chapter 2 was used in the present study. The genetic map consisted of 939 DArT markers located on 671 unique loci in 38 linkage groups. The total genetic distance of the genetic map was 3,114.2 cM with a average distance between any two markers of 4.6 cM.
The map represented 20 of the 21 wheat chromosomes. The program QTL Cartographer V2.5_011
(Wang et al. 2012) was used to conduct confidence interval mapping (CIM) following the steps described in chapter 3. QTL discrimination of putative QTL was conducted following the parameters described in
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chapter 3. In summary, a putative QTL was declared at 2.5 LOD. Five hundred permutations were performed to determine critical LOD threshold. QTL in only one environment with a LOD lower than the critical threshold were discarded. Putative QTL detected in at least two environments (including AE) were reported regardless the critical LOD threshold. A QTL was considered as consistent if was detected in
50% of the environments studied. A QTL was called as major when explained 15% or more of the phenotypic variance (PV). Linkage groups and QTL were identified using MapChart 2.2 program
(Voorrips, 2002).
4.4. Results
4.4.1. Phenotypic data
The elite parent (WCB 414) had better performance than the exotic parent (WCB 617) in all the environments for the traits TKW, KVW, and FE (Appendix Table C1). However, the exotic parent (WCB
617) had higher GPC than the elite parent (WCB 414) (Appendix Table C1). For different mixograph measurements (MX, MEPT, MMLPT, MMLPI), although WCB 414 had better performance than WCB 617 in most of the environments, these differences were not significant at P<0.05 (Appendix Table
C1).Transgressive segregation in direction of both parents was observed for KVW and GPC (Fig. 4-1;
Appendix Table C1) in all the environment. A similar segregation was observed for MX, MEPT, MMLPT, and MMLPI in all environments, except at Prosper 2009. In this case, phenotypic values of the exotic parent were missing. Nevertheless, in this environment, the mixograph-related traits showed transgressive segregation in direction of WCB 414 parent (Appendix Table C1). For TKW, transgressive segregation in both parent directions was observed in all the environments except in Carrington 2009, where the segregation was in direction of the elite parent only (Appendix Table C1). For the trait FE, transgressive segregation in both directions was observed at Carrington 2010 and Prosper 2010, while at
Carrington 2009, it was observed in the direction of the exotic parent (Fig. 4-1; Appendix Table C1). At
Prosper 2009, where the phenotypic values of FE for the exotic parent could not be estimated, no transgressive segregation in the direction of the parent WCB 414 was observed (Appendix Table C1).
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0.26 0.28 WCB617 WCB414
0.20 0.21
0.13 0.14
WCB414
Relativefrequency Relativefrequency
0.07 0.07 WCB617
0.00 0.00 22.26 24.33 26.40 28.47 30.54 32.61 34.68 36.75 38.82 645.76 666.80 687.83 708.86 729.89 750.93 771.96 792.99 814.03 TKW (g) KVW (kg/m^3)
0.26 0.28 WCB414
WCB414 WCB617
0.19 0.21
0.13 0.14
WCB617
Relativefrequency Relativefrequency
0.06 0.07
0.00 0.00 13.71 14.25 14.78 15.31 15.84 16.37 16.90 17.43 17.97 12.08 18.82 25.56 32.29 39.03 45.77 52.51 59.24 65.98 GPC (%) FE (%)
0.30 0.36 WCB617
0.23 WCB414 0.27 WCB414
0.15 0.18
WCB617
Relativefrequency Relativefrequency
0.08 0.09
0.00 0.00 2.79 3.20 3.61 4.02 4.43 4.84 5.25 5.66 6.08 1.04 2.78 4.51 6.25 7.98 9.71 11.45 13.18 14.92 Mx (1-8) MEPT (min)
0.41 0.39 WCB617
WCB414
0.31 0.29
WCB414
0.21 0.19 Relativefrequency Relativefrequency WCB617 0.10 0.10
0.00 0.00 1.28 3.14 5.01 6.87 8.74 10.60 12.47 14.33 16.20 66.14 133.28 200.42 267.55 334.69 401.83 468.96 536.10 603.24 MMLPT (min) MMLPI (%TQ*min)
Fig. 4-1. Frequency distribution of 163 RIL for mean of eight quality traits over four six environments
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Table 4-1. Mean squares, coefficient of variation and heritabilities for quality traits of the parents, RILs and checks evaluated in four environments in North Dakota, USA, during 2009 and 2010. Mean squares Trait† CV(%)‡ H§ SE¶ Genotype Environment G x E Error
TKW 52.8** 3294.73 9.67** 2 4.6 0.50 0.04 KVW 6583.2** 593271.0 1169.6** 130.4 1.6 0.47 0.04 FE 1848.8** 4591.3 67.8** 12.4 8.2 0.84 0.02 GPC 4.0** 224.9 1.1** 0.2 2.9 0.39 0.04 MX 2.3** 25.31 0.7** 0.6 17.1 0.24 0.03 MEPT 34.1** 638.0 6.2** 2.4 26.0 0.48 0.04 MMLPT 36.3** 531.2 6.3** 2.6 25.3 0.49 0.04 MLPI 38855.0** 354470.0 47663.1** 1824.3 17.2 0.60 0.03 ** Significance at P < 0.01, respectively; ns, not significant at P < 0.05 †TKW, thousand Kernel weight; KVW, kernel volume weight; FE, flour extraction; GPC, grain protein content; Mx, mixogram score; MEPT, mixogram envelope peak time; MMLPT, mixogram MID line peak time; MLPI, mixogram MID peak Integral. ‡Coefficient of variation. §Broad sense heritability on plot-means basis calculated in the RILs. ¶Standard error of heritability.
Error variances among the environments were homogenous (Appendix Table C2) allowing a combined ANOVA to be performed. The eight quality traits had significant G×E interactions(Table 4-1).
Broad sense heritability on plot means basis ranged from 0.24 for Mx to 0.84 for FE (Table 4-1). Several inconsistent correlations (positive and negatives correlations for the same pair of traits in different environments) were observed among several quality traits (Table 4-2). Flour extraction was positively and significantly associated with TKW and KVW, but negatively associated with mixograph-related traits; while these traits had strong positive correlations among them (Table 4-2). The presence of spikes with SS
(PSS) was positively associated with GPC and mixograph-related traits, but negatively associated with
TKW. Meanwhile spikes with PP trait were positively correlated with GPC, MEPT, MMLPT and MMLPI; but negatively associated to TKW, KVW and FE. The traits PC and Aless only showed weakly associations with GPC (Appendix Table C3).
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Table 4-2. Pearson's correlation coefficients among traits and their significance for the RIL, their parents and checks grown at Prosper and Carrington, ND, in 2009 and 2010. TKW KVW GPC FE MX MEPT MMLPT MMLPI 0.26**§ -0.40**‡ TKW 1 0.28**¶ Ns -0.31**‡ -0.31**‡ -0.29**‡ 0.67**‡ 0.22**† 0.37**‡ 0.27**‡ 0.25**‡ 0.24**‡ KVW 1 -0.18* 0.41**¶ 0.22**† -0.26**† -0.25**† -0.15*†
-0.59**
GPC 1 ns 0.33**¶ -0.31**§ -0.30**§ -0.27**†
FE 1 -0.16*† -0.25**† -0.26**† -0.20**†
MX 1 Ns ns 0.26**§
0.98**‡ 0.88**‡ MEPT# 1 0.99**‡ 0.92**‡
0.93**‡ MMLPTd 1 0.92**‡
*, ** Significance at P < 0.05, 0.01, respectively; ns not significant at P < 0.05 TKW, thousand Kernel weight; KVW, kernel volume weight; FE, flour extraction; GPC, grain protein content; Mx, mixograph score; MEPT, mixograph envelope peak time; MMLPT, mixograph MID line peak time; MLPI, mixograph MID peak Integral †r from one environment, ‡r pooled from two environments, §r pooled from three environments, ¶r pooled from four environments #Alternative pooled correlations were observed between the traits MEPT and MMLPI. The lowest pooled r value is presented. ††Alternative pooled correlations were observed between the traits MMLPT and MMLPI. The lowest pooled r value is presented.
4.4.2. Identification of genomic regions (QTL) associated with eight quality traits
A total of 69 QTL were detected for eight quality traits investigated in the present study (Table 4-
3). These QTL were located on eighteen different chromosomes (All accept chromosome 4D, 5D and
6D). A total of nine QTL were detected on chromosome 6B; seven QTL on each chromosomes 1B and
2D; five QTL on each chromosomes 2B and 3A; four QTL each on chromosomes 1A, 5B, 6A, and 7B; three QTL on each chromosomes 1D, 2A, 3B, 3D and 4A; two QTL on each chromosomes 4B, and 7D; one QTL on each chromosomes 5A, and 7A. In terms of genome wide distribution of QTL, a total of 34,
21 and 15 quality-related QTL were detected on B, A and D-genome, respectively. Among all the 70 QTL, a total of 18 QTL were consistent (identified in at least 50% of the environments) (Table 4-3). A total of 13
QTL explained more than 15% of PV and were considered as major QTL, while the remaining 57 QTL explained less than 15% of PV and were considered as minor QTL. The alleles for increased phenotypic
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values at 31 loci were contributed by WCB414, while exotic parent WCB617 contributed alleles for 39 loci that increased the effect of quality traits.
4.4.3. QTL for TKW, KVW, and FE
A total of 11 QTL were associated with TKW, including one consistent QTL (QTKW.ndsu.4A)
(Table 4-3, Fig.4-2). Only the consistent QTL QTKW.ndsu.4A could be considered as major QTL as it explained up to 17.1% of PV of TKW. The other QTL explained between 6.2% and 13.9% of PV for TKW.
In individual environments, the number of QTL identified for TKW ranged from three to five and PV explained by all the QTL identified in individual environments ranged from 24 to 47.3% (Table 4). Both parents contributed with alleles that increased the phenotypic values of TKW. For the consistent QTL
QTKW.ndsu.4A, elite parent WCB414 contributed the alleles for increased TKW.
For KVW, a total of 10 QTL including three consistent QTL (QKVW.ndsu.1A.1, QKVW.ndsu.2A.1 and QKVW.ndsu.6A.1) were detected (Table 4-3; Fig. 4-2). The number of QTL identified for TKW in individual environments ranged from three to four. The PV explained by all the QTL in individual environments ranged from 40.8 to 51.2%. Four QTL (QKVW.ndsu.1B.1, QKVW.ndsu.2A.1,
QKVW.ndsu.2A.2, and QKVW.ndsu.6A.1) including two consistent QTL, had major effect (PV>15%) on
KVW, at least in some of the environments. Additive effects indicated that the elite parent WCB414 contributed positive alleles for increasing KVW at most of the loci, including the three consistent QTL.
A total of six QTL including four consistent QTL (QFE.ndsu.1A, QFE.ndsu.1B, QFE.ndsu.3D, and
QFE.ndsu.6A) were associated with FE in this RIL population (Table 4-3; Fig. 2). The number of QTL identified for FE in individual environments ranged from two (Prosper 2009) to six (Prosper 2010) and the
PV explained by all the QTL in individual environments ranged between 15.3% and 51.5%. The PV explained by individual QTL ranged from 4.8% to 19.0%. Only one QTL (QFE.ndsu.2B), which was identified in a single environment, explained more than 15% PV of FE. The elite parent WCB 414 provided the alleles that increased phenotypic values of FE at all loci except QFE.ndsu.6A.
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4.4.4. QTL for GPC
GPC was controlled by 11 different QTL in this population (Table 4-3; Fig. 2). These include three consistent QTL (QGPC.ndsu.5B, QGPC.ndsu.6B.1 and QGPC.ndsu.7B) located on chromosome 5B, 6B and 7B (Table 4-3; Fig. 4-2). The number of QTL identified in individual environments ranged from three
(Prosper 2009) to five (Carrington 2010). The PV explained by individual QTL ranged from 4.7% to 16.9% and the PVE explained by all the QTL in individual environments ranged from 38.5%- 40.3%. Two QTL had major effect and explained up to 16.5% (QGPC.ndsu.1A.1) and 16.9% (QGPC.ndsu.6B.1) of PV for
GPC. The other nine QTL were minor and explained between 4.7% and 13.7 % of PV. The elite parent contributed alleles for increased GPC at five loci (QGPC.ndsu.1B, QGPC.ndsu.4B, QGPC.ndsu.6B.1,
QGPC.ndsu.6B.2, and QGPC.ndsu.7B), while the exotic parent contributed alleles for increased GPC at six loci (QGPC.ndsu.1A.1, QGPC.ndsu.1A.2, QGPC.ndsu.2B, QGPC.ndsu.2D, QGPC.ndsu.3D and
QGPC.ndsu.5B).
4.4.5. QTL for mixograph-related traits
The genetic dissection of MEPT resulted in the detection of two consistent QTL
(QMEPT.ndsu.1D, and QMEPT.ndsu.5B) and five putative QTL (QMEPT.ndsu.1B, QMEPT.ndsu.2B,
QMEPT.ndsu.2D.1, QMEPT.ndsu.2D.2, and QMEPT.ndsu.4A) (Table 4-3; Fig. 4-2). In individual environments, the number of identified QTL ranged from one (Prosper 2010) to six (Carrington 2010).
The PV explained by all QTL identified in individual environments ranged from 25.9%-64.5%. The QTL located on 1DL (QMEPT.ndsu.1D) seems to be the most important locus in controlling MEPT as it was detected in all environments and explained up to 41.2% of PV. The remaining six QTL were minor and explained between 3.2% and 10.1% of PV (Table 4-3). Both parents contributed QTL alleles to increase
MEPT, however, for both consistent QTL (QMEPT.ndsu.1D, and QMEPT.ndsu.5B), the alleles for increased values of MEPT were contribute by elite parent (Table 4-3).
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Table 4-3. Summary of information for QTL identified in a RIL population derived from the cross of an elite line (WCB414) and exotic line (WCB617) with SS at four locations in ND, USA during 2009 and 2010. QTL Environment† Flanking markers Pos‡.(cM) CI§ (cM) LOD Thresh.¶ a# R2(%) Thousand kernel weigth
QTKW.ndsu.2A II wPt-2372-wPt-2850 70.2 58.5-84.4 4.8 3.3 -1.3 13.5 QTKW.ndsu.2B IV wPt-8760-wPt-6158 43.4 32.1-60.8 4.1 3.2 1.1 8.8 QTKW.ndsu.2D II wPt-5014-wPt-668017 76.8 66.6-86.8 4.1 3.3 1.0 7.8 QTKW.ndsu.3A1 IV, AE wPt-741078-wPt-743858 2.0-2.4 0-12.9 5.0-8.0 3.2-3.4 -0.9_-1.6 9.2-13.9 QTKW.ndsu.3A2 III wPt-6854-tPt-0242 97.5 89.6-115.1 3.8 3.2 -1.2 7.9 QTKW.ndsu.3B1 I wPt-9170-wPt-743661 128.8 116.8-147.3 3.4 3.3 -0.8 7.8 QTKW.ndsu.3B2 IV tPt-6487-wPt-2299 44.0 38.3-57.6 4.4 3.2 -1.1 7.3 QTKW.ndsu.3B3 I wPt-9586-wPt-741584 4.8 0-17.7 3.6 3.3 -0.8 7.2 QTKW.ndsu.4A I, II, III, IV, AE wPt-2247-wPt-744614 1.0-22.7 0-38.1 3.6-9.4 3.2-3.4 1.0-1.7 7.3-17.1 QTKW.ndsu.5A AE wPt-9094-wPt-800131 12.0 0-24.3 4.2 3.4 -1.0 12.9 QTKW.ndsu.6B IV wPt-664250-wPt-2297 102.3 96.5-106.4 5.5 3.2 1.4 9.2 ††RTPVE 24-47.3
Kernel volume weight 160 QKVW.ndsu.1A I, IV, AE wPt-4065-wPt-5167 179.2-180.2 161.0-191.6 4.5-6.0 3.2-3.5 8.1-10.0 9.5-12.7
QKVW.ndsu.1B I, AE wPt-0308-wPt-8240 22.5 10.1-29.8 5.1-6.6 3.4-3.5 9.5-10.3 9.3-12.2 QKVW.ndsu.2A1 II, IV, AE wPt-798339-wPt-2372 38.0-40.0 30.5-45.7 4.1-8.3 3.2-3.5 9.7-17.0 10.3-22.5 QKVW.ndsu.2A2 III wPt-2850-wPt-8068 88.7 79.7-106.5 5.8 3.4 25.0 17.7 QKVW.ndsu.4B I wPt-5559-wPt-1046 5.5 0.4-6.8 4.2 3.4 7.5 7.6 QKVW.ndsu.5B III wPt-2373-wPt-8449 252.8 247.8-260.4 5.5 3.4 -21.1 11.9 QKVW.ndsu.6A.1 I, II, III, IV, AE wPt-733115-wPt-667618 113.6-135.8 98.8-149.3 2.7-8.9 3.2-3.5 7.4-17.0 7.2-21.0 QKVW.ndsu.6A.2 II wPt-667170-wPt-4791 79.7 61.8-85.2 4.4 3.2 -9.2 7.8 QKVW.ndsu.6B III wPt-5408-wPt-8412 113.3 102.2-122.1 3.5 3.4 14.7 6.7 QKVW.ndsu.7B II wPt-665428-wPt-9299 121.6 96.7-134.3 4.5 3.3 -6.8 8.5 ††RTPVE 40.8-51.2
(Continues)
Table 4-3. Summary of information for QTL identified in a RIL population derived from the cross of an elite line (WCB414) and exotic line (WCB617) with SS at four locations in ND, USA during 2009 and 2010. (Continued) QTL Environment† Flanking markers Pos‡. (cM) CI§ (cM) LOD Thresh.¶ a# R2(%) Flour extraction QFE.ndsu.1A I, II, III, IV, AE wPt -3698-wPt-1906 93.5 -94.0 81.1 -110.8 2.6 -5.6 3.3-3.7 3.7 -6.0 4.9 -11 QFE.ndsu.1B III, IV, AE wPt-2389-wPt-3451 0.0 0-15.1 2.9-4.5 3.3-3.6 3.8-5.2 5.0-8.2 QFE.ndsu.2B III wPt-1813-wPt-8404 57.6 42.9-68.8 4.1 3.3 7.3 19.0 QFE.ndsu.3D II, III, IV wPt-665093-wPt-6169 39.2-42.2 33.1-46.9 2.7-3.3 3.3-3.7 4.6-4.6 5.8-7.7 QFE.ndsu.4A III, AE wPt-0817-wPt-7926 20.8-30.3 12.5-39.1 2.6-3.4 3.3-3.6 3.5-4.3 4.8-6.5 QFE.ndsu.6A I, II, III, IV, AE wPt-666773-wPt-731592 1.0-2.0 0-12.7 3.2-4.5 3.3-3.7 -4.4_-4.9 6.0-9.1 ††RTPVE 15.3-51.5
Grain protein content QGPC.ndsu.1A.1 III, AE wPt -1924-wPt-7872 15.4 -33.4 12.4 -44.4 4.9 -8.7 3.2-3.4 - 0.2_-0.6 8.1 -16.5 QGPC.ndsu.1A.2 I wPt-8172-wPt-9429 101.9 85.4-113.4 4.1 3.2 -0.3 8.2 QGPC.ndsu.1B II, AE wPt-1684-wPt-5899 61.7 51.7-75.7 2.8-3.4 3.3-3.4 0.2-0.2 4.7-5.3 QGPC.ndsu.2B IV wPt-744808-wPt-4368 30.0 14.4-48.1 3.4 3.4 -0.3 10.5
161 QGPC.ndsu.2D III wPt-8134-wPt-2761 30.4 13.9-38.5 3.7 3.2 -0.4 7.8
QGPC.ndsu.3D III wPt-740945-rPt-1806 0.0 0-9.8 5.5 3.2 -0.4 9.9 QGPC.ndsu.4B II wPt-732423-wPt-8892 12.0 3.6-17.8 5.5 3.3 0.3 8.8 QGPC.ndsu.5B I, II, IV, AE wPt-1895-wPt-6191 111.7-113.3 101.1-127.5 3.6-6.6 3.2-3.4 -0.2_-0.3 5.3-13.7 QGPC.ndsu.6B.1 I, II, IV, AE wPt-5234-wPt-1437 52.5-57.8 41.0-71.5 2.8-9.2 3.2-3.4 0.3-0.4 5.5-16.9 QGPC.ndsu.6B.2 IV wPt-1241-wPt-3605 95.3 88.1-102.2 3.6 3.4 0.3 7.7 QGPC.ndsu.7B III, IV, AE wPt-0266-wPt-9299 95.5-121.6 90.2-131.7 2.7-6.0 3.2-3.4 0.3 4.7-10.4 ††RTPVE 38.5-40.3
Mi xogram envelope peak time QMEPT.ndsu.1B IV, AE wPt-8320-wPt-1560 3.8 0 -19.3 2.5 -4.6 3.3-3.3 - 0.6_-0.6 3.2 -6.0 QMEPT.ndsu.1D I, II, III, IV, AE wPt-7333835-wPt-672077 83.5-85.5 68.8-93.1 8.0-23.6 3.2-3.3 0.8-1.9 18.7-41.2 QMEPT.ndsu.2B IV wPt-744808-wPt-4368 22.0 11.1-35.6 4.1 3.3 0.8 5.6 QMEPT.ndsu.2D.1 II wPt-730568-wPt-671914 74.8 64.8-86.7 3.9 3.3 -0.5 7.2 QMEPT.ndsu.2D.2 IV wPt-741084-wPt-666656 49.5 46.6-55.8 5.6 3.3 -1.0 7.9 QMEPT.ndsu.4A IV rPt-7285-wPt-6728 21.6 16.4-33.6 3.5 3.3 -0.8 4.8 QMEPT.ndsu.5B I, IV, AE wPt-1895-wPt-6191 110.7-118.3 94.6-135.3 3.0-5.5 3.3-3.3 0.5-1.1 5.5-10.1 ††RTPVE 25.9-64.5 (Continues)
Table 4-3. Summary of information for QTL identified in a RIL population derived from the cross of an elite line (WCB414) and exotic line (WCB617) with SS at four locations in ND, USA during 2009 and 2010. (Continued) Environment ‡ § ¶ # 2 QTL † Flanking markers Pos . (cM) CI (cM) LOD Thresh. a R (%)
Mixogram MID line peak time
QMMLPT.ndsu.1B IV, AE wPt-8320-wPt-1560 3.8 0-20 2.9-4.8 3.1-3.2 -0.6_-0.7 4.1-6.5 QMMLPT.ndsu.1D I, II, III, IV, AE wPt-743310-wPt-672077 78.6-84.5 67.4-93.3 7.1-22.6 3.0-3.2 0.8-2.3 15.7-40.3 QMMLPT.ndsu.2D II wPt-730568-wPt-671914 74.8 64.8-86.8 4.0 3.0 -0.6 7.6 QMMLPT.ndsu.3A AE tPt-1079-wPt-1464 47.3 36.0-59.5 2.6 3.2 0.4 3.5 QMMLPT.ndsu.5B I, III, IV, AE wPt-1895-wPt-6191 110.7-127.3 101.1-157.2 2.9-4.1 3.1-3.2 0.6-1.0 6.0-7.9 QMMLPT.ndsu.6B IV wPt-0052-tPt-1723 150.4 142.0-164.1 3.3 3.1 0.8 5.6 ††RTPVE 23.3-56.2
Mixogram MID peak integral %TQ*MIN
QMMLPI.ndsu.1B I, III wPt-8320-wPt-1560 3.8 0-17.8 2.8-3.4 3.2-3.3 -17.5_-23.1 4.7-6.1 QMMLPI.ndsu.1D I, II, III, IV, AE wPt-743310-wPt-672077 79.6-83.5 68.8-94.1 7.0-22.2 3.1-3.3 28.9-60.5 16.4-37.1 QMMLPI.ndsu.2D.1 II wPt-2675-wPt-8134 27.0 18.9-32.2 8.6 3.1 -36.7 17.7 QMMLPI.ndsu.2D.2 IV, AE wPt-741084-wPt-666656 49.5 38.5-62.7 3.4-3.6 3.1-3.2 -18.2_-23.4 4.2-4.8
162 QMMLPI.ndsu.3D III wPt-669255-wPt-3094 33.7 20.0-42.3 3.4 3.3 -26.1 6.3 QMMLPI.ndsu.6B.1 I, AE wPt-745074-wPt-6667 72.3 66.7-85.3 3.5-3.7 3.2-3.2 17.2-18.6 4.7-6.7 QMMLPI.ndsu.6B.2 IV wPt-0052-tPt-1723 148.4 141.3-154.5 7.4 3.1 40.3 10.8 QMMLPI.ndsu.7D II, AE wPt-744917-wPt-664368 0.0 0-10.4 2.8-2.8 3.1-3.2 -15.5_-16.2 3.8-4.9 ††RTPVE 32.4-52.1
Mixogram
QMx.ndsu.2B.2 AE wPt-8404-wPt-8398 76.8 63.8-86.4 5.3 3.3 -0.2 10.1 QMx.ndsu.3A.1 II tPt-0242-wPt-0286 122.2 108.3-134.1 6.8 3.3 -0.4 15.6 QMx.ndsu.3A.2 II wPt-1562-wPt-2740 163.4 153.7-165.9 5.6 3.3 0.3 11.0 QMx.ndsu.6A III wPt-666074-wPt-733115 109.6 99.0-141.0 3.9 3.3 0.2 12.6 QMx.ndsu.6B.1 II wPt-0171-wPt-4164 8.5 0-14.6 3.8 3.3 -0.3 6.5 QMx.ndsu.6B.2 I, II, IV, AE wPt-1756-wPt-5234 44.3-45.1 38.5-48.1 5.2-10.5 3.2-3.3 0.3-0.4 11.3-19.9 QMx.ndsu.7A IV wPt-3135-rPt-4199 72.2 63.9-85.3 2.8 3.2 -0.2 5.8 (Continues)
Table 4-3. Summary of information for QTL identified in a RIL population derived from the cross of an elite line (WCB414) and exotic line (WCB617) with SS at four locations in ND, USA during 2009 and 2010. (Continued) QTL Environment† Flanking markers Pos‡. (cM) CI§ (cM) LOD Thresh.¶ a# R2(%) Mixogram MID line peak time
QMx.ndsu.7B.1 IV wPt-8246-wPt-3445 5.7 0-20.3 2.7 3.2 -0.2 5.3 QMx.ndsu.7B.2 AE wPt-744769-wPt-7108 1.0 0-7.4 3.8 3.3 -0.2 6.9 wPt-744917-wPt- QMx.ndsu.7D I, IV, AE 0.0 0-9.4 3.1-4.3 3.2-3.3 -0.2_-0.2 6.6-7.1 664368 ††RTPVE 29.0-48.8
†I, Prosper 2009; II, Carrington 2009; III, Prosper 2010; IV, Carrington 2010; V, Prosper 2011; VI, Carrington 2011. ‡Position §Confidence Interval ¶Thresold calculated by permutation test. #Additive effects ††rank of phenotypic variation explained per environment
163
1B-1 1B-2 1B-3 1A-1 1A-2
1A-1 1B-1
0.0 wPt-730136 0.0 wPt-741297 1.6 wPt-3347 3.8 wPt-8320 3.1 wPt-666424 4.5 wPt-1560 7.3 wPt-667458 22.5 wPt-0308 0.0 wPt-671415 wPt-4666 wPt-6122 39.3 wPt-8240 8.3 1B-1 1B-2 1B-3 1.0 wPt-664989 wPt-731412 wPt-8744 wPt-0441 1.5 wPt-665204 9.0 wPt-664586 42.1 wPt-744960 wPt-1726 0.0 wPt-2389 0.0 wPt-3950 2.0 wPt-665037 15.4 wPt-1924 wPt-669484 46.8 wPt-740897 1.1 wPt-3451 2.4 wPt-734314 3.9 wPt-664735 wPt-734000 wPt-732520 47.4 wPt-6012 5.5 wPt-1573 7.9 wPt-664703 14.7 wPt-665480 20.6 1B-1wPt-666537 wPt-730618 1B-2 48.0 1B-3wPt-3103 19.6 wPt-2315
tPt-6091 wPt-1786 QKS.ndsu.1B 8.9 QFE.ndsu.1B 24.1 wPt-666986
wPt-6709 wPt-3198 38.5 wPt-1247 QALB.ndsu.1B QPH.ndsu.1B.2 wPt-664593QPH.ndsu.1B.1 50.3 wPt-8682 10.2 QMMLPI.ndsu.1B 24.4 wPt-4971 wPt-4647 wPt-667180 wPt-4177 QMMLPT.ndsu.1B 38.8 wPt-1818 QDM.ndsu.1A 27.4 QPP.ndsu.1A.2 57.4 wPt-2597 wPt-1770 QPC.ndsu.1B 33.4 wPt-734027 15.6 43.3 rPt-7906 29.2 wPt-6979
QGY.ndsu.1A 58.9 wPt-3819 QDH.ndsu.1A QKS.ndsu.1A wPt-6975 30.6 wPt-730758 wPt-730783
QSL.ndsu.1A.1 wPt-7872 62.9 QPP.ndsu.1A.1 34.0 QKVW.ndsu.2A 61.7 wPt-1684 wPt-664772 78.5 wPt-9809
QNd.ndsu.1A.1 54.8 QGPC.ndsu.1A.1 65.3 wPt-5899 wPt-1862 wPt-669118 84.3 wPt-5061 57.6 66.0 wPt-1876 wPt-667558 wPt-743329 67.1 wPt-731722 85.4 wPt-5034 59.3 wPt-8749 QTKW.ndsu.1B wPt-4729 97.3 rPt-9074
QLd.ndsu.1A 67.7 1A-1 1A-2 QKSk.ndsu.1B 61.1 wPt-5776 76.1 wPt-1116 66.1 wPt-729832 77.1 wPt-4343
QFE.ndsu.1A 76.5 wPt-3698 wPt-730408 77.4 wPt-5740
QSL.ndsu.1A.2 QNd.ndsu.1A.2 QGPC.ndsu.1A.2 94.0 wPt-2872 82.8 wPt-4811
QPP.ndsu.1A.3
QKNd.ndsu.1A.1 94.4 wPt-1906 QNdISk.ndsu.1A QNS.ndsu.1A 95.9 wPt-733588
QALT.ndsu.1A 0.0 wPt-665613wPt-741297wPt-666269 96.6 3.8 wPt-734301wPt-8320 1B-3 97.0 4.5 wPt-665784wPt-1560 100.9 22.5 wPt-8172wPt-0308
164 117.1 wPt-9429 0.0 wPt-671415 QKVW.ndsu.1A 39.3 wPt-8240 123.00.0 wPt-6654wPt-741297 1.0 wPt-664989 wPt-8744 wPt-0441 150.33.8 42.1 wPt-3272wPt-8320 1.5 wPt-665204 0.0 wPt-2389 4.5 wPt-0432wPt-1560wPt-744960 wPt-1726 1B-2 176.9 1.1 wPt-3451 2.0 wPt-665037 177.222.5 46.8 wPt-4065wPt-0308wPt-740897 wPt-1573 0.0 3.9wPt-671415wPt-664735 191.939.3 47.4 wPt-5167wPt-8240wPt-6012 5.5 1.0 14.7wPt-664989wPt-665480
194.0 48.0 wPt-667814wPt-8744wPt-3103wPt-0441 19.6 wPt-2315 QKS.ndsu.1B QFE.ndsu.1B 1.5 24.1wPt-665204wPt-666986 0.0 wPt-730136 200.142.1 wPt-7784 38.5 wPt-1247 QALB.ndsu.1B 0.0 wPt-2389 QPH.ndsu.1B.2 QPH.ndsu.1B.1 50.3 wPt-744960wPt-8682wPt-1726
QMMLPI.ndsu.1B wPt-4658 2.0 24.4wPt-665037wPt-4971 wPt-4647 1.6 wPt-3347 QMMLPT.ndsu.1B 210.9 57.4 wPt-2597 1.1 38.8wPt-3451 wPt-1818 46.8QPC.ndsu.1B wPt-740897 wPt-666424 215.4 wPt-5316 rPt-7906 3.9 29.2wPt-664735wPt-6979 3.1 47.4 58.9 wPt-6012wPt-3819 5.5 43.3wPt-1573 7.3 wPt-667458 219.6 wPt-8016 62.9wPt-2315 wPt-6975 14.7 30.6wPt-665480wPt-730758 wPt-730783
QKVW.ndsu.2A 48.0 61.7 wPt-3103wPt-1684 19.6 QKS.ndsu.1B
wPt-4666 wPt-6122 QFE.ndsu.1B 78.5 wPt-9809 24.1 wPt-666986 38.5 wPt-1247 QALB.ndsu.1B
65.3 wPt-5899 QPH.ndsu.1B.2 8.3 QPH.ndsu.1B.1 50.3 wPt-8682
wPt-731412 QMMLPI.ndsu.1B 84.3 wPt-5061 24.4 wPt-4971 wPt-4647 1A-2 QMMLPT.ndsu.1B 57.4 66.0 wPt-2597wPt-1876 38.8 wPt-1818 9.0 wPt-664586 QPC.ndsu.1B 85.4rPt-7906 wPt-5034 29.2 wPt-6979 0.0 wPt-3950 58.9 67.1 wPt-3819wPt-731722 43.3 15.4 wPt-1924 wPt-669484 QTKW.ndsu.1B wPt-4729 62.9 97.3wPt-6975 rPt-9074 30.6 wPt-730758 wPt-730783
QKVW.ndsu.2A 67.7 61.7 wPt-1684 QKSk.ndsu.1B wPt-734000 wPt-732520 2.4 wPt-734314 wPt-664703 65.3 76.1 wPt-5899wPt-1116 78.5 wPt-9809 20.6 wPt-666537 wPt-730618 7.9 66.0 77.1 wPt-1876wPt-4343 84.3 wPt-5061 wPt-6709 wPt-3198 8.9 tPt-6091 wPt-1786 67.1 77.4 wPt-731722wPt-5740 85.4 wPt-5034 wPt-667180 wPt-4177 10.2 wPt-664593 Consistent QTL
QDM.ndsu.1A 27.4 QTKW.ndsu.1B QPP.ndsu.1A.2 67.7 82.8 wPt-4729wPt-4811 97.3 rPt-9074 wPt-1770 QKSk.ndsu.1B
33.4 wPt-734027 15.6
QGY.ndsu.1A QDH.ndsu.1A QKS.ndsu.1A 76.1 wPt-1116
QSL.ndsu.1A.1 wPt-7872 QPP.ndsu.1A.1 34.0 77.1 wPt-4343 wPt-664772 Putative QTL
QNd.ndsu.1A.1 54.8 QGPC.ndsu.1A.1 77.4 wPt-5740 wPt-1862 wPt-669118 57.6 82.8 wPt-4811 wPt-667558 wPt-743329
59.3 wPt-8749 SS-Related QTL QLd.ndsu.1A 61.1 wPt-5776 66.1 wPt-729832
QFE.ndsu.1A 76.5 wPt-3698 wPt-730408
QSL.ndsu.1A.2 QNd.ndsu.1A.2
QGPC.ndsu.1A.2 94.0 wPt-2872 QPP.ndsu.1A.3
QKNd.ndsu.1A.1 94.4 wPt-1906 QNS.ndsu.1A Fig. 4-2. Genetic map and QTL for 35 spike-related, agronomic, and quality traits of a RIL population derived from the cross of the elite line QNdImSk.ndsu.1A 95.9 wPt-733588 QALT.ndsu.1A WCB414wPt-665613 andwPt-666269 the exotic lineWCB617 with SS trait, over four to six locations in ND, USA during 2009, 2010, and 2011 96.6 wPt-734301 97.0 wPt-665784 100.9 wPt-8172
117.1 wPt-9429 QKVW.ndsu.1A 123.0 wPt-6654 150.3 wPt-3272 176.9 wPt-0432 177.2 wPt-4065 191.9 wPt-5167 194.0 wPt-667814 200.1 wPt-7784 210.9 wPt-4658 215.4 wPt-5316 219.6 wPt-8016 2A 2B-1
1D
1D 0.0 wPt-0966 2A 1.0 wPt-667500 0.0 wPt-7859 0.0 wPt-734106 15.0 wPt-0413 wPt-4671 3.0 wPt-1133 11.5 wPt-5251 16.3 wPt-666832 wPt-668040 23.3 wPt-9274 19.5 wPt-668027 wPt-9380 2B-2 30.7 wPt-0100
21.0 QKNd.ndsu.2B
33.1 wPt-798459 QSL.ndsu.2B 42.4 wPt-8760 35.0 wPt-798339 49.6 wPt-743857 45.6 wPt-6158 wPt-3565 47.2 wPt-2372 wPt-10003
50.6 wPt-742859 46.6 wPt-1813 QLd.ndsu.2A.2
QNNdISk.ndsu.2B2 QAless.ndsu.2A.2
51.6 wPt-743310 QGY.ndsu.2A.1
QALB.ndsu.2A
QKVW.ndsu.2A.1 QNS.ndsu.2A QAless.ndsu.2A.1 wPt-2850 74.8 wPt-8404
73.7 QTKW.ndsu.2B QNNd.ndsu.2A wPt-732602 QFE.ndsu.2B 82.5 94.7 wPt-8068 83.8 wPt-8398
wPt-733835 wPt-667287 95.8 wPt-6711 wPt-0568 91.2 wPt-9070 QMx.ndsu.2B 83.5 QTKW.ndsu.2A wPt-3743 wPt-740658 wPt-8490 98.8 tPt-9405
100.0 wPt-672077 QLd.ndsu.2A.1 QKVW.ndsu.2A.2 QKS.ndsu.2A 108.5 wPt-664128 125.1 wPt-730172 QGY.ndsu.2A.2 122.1 wPt-7721 125.8 wPt-669681 122.4 wPt-0003 2A 2B-1QALT.ndsu.2A 129.5 wPt-734132 149.8 wPt-4201
QNNdISk.ndsu.2A 130.9 wPt-3945 131.2 wPt-731020 wPt-732575 wPt-4427 wPt-668079 131.5 wPt-665749 131.9 wPt-730475 2B-2 wPt-665360 wPt-4687 132.8 wPt-671947 0.0 wPt-9190 7.8 tPt-4125 QNS.ndsu.1D 133.1 wPt-729788
165 wPt-665550
QMEPT.ndsu.1D 12.0 QMMLPI.ndsu.1D
QMMLPT.ndsu.1D 133.4 wPt-8545 wPt-730718 13.0 wPt-7350
QALB.ndsu.1D 134.4 wPt-666963 14.4 wPt-9336
QGY.ndsu.1D.1 134.7 wPt-7057 15.3 tPt-5438 QPH.ndsu.1D wPt-744181 QKNd.ndsu.1D wPt-730605 wPt-7092
QNd.ndsu.1D 16.7 wPt-7697 wPt-7957 18.9 wPt-2397 135.0 wPt-732386 wPt-0948
wPt-4988 wPt-665327 19.4 QNS.ndsu.1D QGY.ndsu.1D.2 wPt-4711 wPt-741382 QKSk.ndsu.2B 22.0 wPt-744808
wPt-7711 wPt-6503 QMEPT.ndsu.2B.1 QGPC.ndsu.2B 135.4 wPt-1263 QNNdISk.ndsu.2B1 49.4 wPt-4368 2B-1 50.2 wPt-2425 137.7 wPt-8866 51.7 wPt-5736 wPt-5721 wPt-5915 138.6 52.5 wPt-0049 wPt-2968 0.0 wPt-7859 55.1 wPt-4917 0.0 wPt-734106 140.8 wPt-730879 3.0 wPt-1133 11.5 wPt-5251 146.1 wPt-1865 23.3 wPt-9274 19.5 wPt-668027
151.1 wPt-4497 30.7 wPt-0100 QKNd.ndsu.2B
33.1 wPt-798459 QSL.ndsu.2B 42.4 wPt-8760 35.0 153.0wPt-798339wPt-5253 45.6 wPt-6158 wPt-3565 47.2 wPt-2372 wPt-10003
46.6 wPt-1813 QLd.ndsu.2A.2
QNNdISk.ndsu.2B2 Consistent QTL
QAless.ndsu.2A.2
QGY.ndsu.2A.1
QALB.ndsu.2A
QKVW.ndsu.2A.1 QNS.ndsu.2A QAless.ndsu.2A.1 wPt-2850 74.8 wPt-8404
73.7 QTKW.ndsu.2B QNNd.ndsu.2A 94.7 wPt-8068 QFE.ndsu.2B 83.8 wPt-8398 Putative QTL
95.8 wPt-6711 wPt-0568 91.2 wPt-9070 QMx.ndsu.2B QTKW.ndsu.2A wPt-740658 wPt-8490 98.8 tPt-9405 SS-Related QTL
QLd.ndsu.2A.1 QKVW.ndsu.2A.2
QKS.ndsu.2A 108.5 wPt-664128
QGY.ndsu.2A.2 122.1 wPt-7721 Fig. 4-2. Genetic map and QTL122.4 for 35 spikewPt-0003-related, agronomic, and quality traits of a RIL population derived from the cross of the elite line QALT.ndsu.2A 149.8 wPt-4201
WCB414 and the exotic lineWCB617 with SS trait, over four to six locations in ND, USA during 2009, 2010, and 2011 (Continued) QNNdISk.ndsu.2A
3A-1 3A-2 3A-3
2D2D 0.0 wPt-2675 27.4 wPt-8134 3A-1 36.2 wPt-2761 0.0 wPt-741078 38.6 wPt-741029 wPt-3677 wPt-5683 2.4 wPt-671711 wPt-741848 39.7 wPt-663767 wPt-732866 3.0 wPt-743858 wPt-734114 wPt-740855 3.6 wPt-741816 40.3 wPt-732705 6.1 wPt-743909 40.9 wPt-671410 0.0 wPt-1681 6.7 wPt-742486 0.0 tPt-7492 41.5 wPt-5574 1.3 wPt-2755 41.8 wPt-667785 8.1 wPt-742039 1.7 wPt-730156 wPt-7608 wPt-3144 wPt-664967 8.7 wPt-742665 2.7 wPt-9802 wPt-730529 9.2 wPt-741986 wPt-742118 wPt-1339 wPt-730057 wPt-66093A-1 QGY.ndsu.3A 3A-2 3A-3 28.7 wPt-7243 wPt-6096 QTKW.ndsu.3A1 13.5 wPt-9303 42.1 wPt-671455 wPt-0936 QKNd.ndsu.3A 13.9 wPt-740730
QLd.ndsu.3A 54.5 wPt-9160 wPt-0398 wPt-4093 wPt-742673 35.9 wPt-667640 wPt-21863A-1wPt-6342 3A-2 QALB.ndsu.3A 3A-3 59.0 wPt-5263 wPt-2294 36.3 tPt-1079 42.4 wPt-7149 wPt-664901 48.2 wPt-1464 43.4 wPt-667843 QMMLPT.ndsu.3A 48.9 wPt-9928 43.7 wPt-4144 wPt-666857 50.4 wPt-9634 44.1 wPt-740836 57.1 wPt-664488 45.5 wPt-3812 QNS.ndsu.3A 47.3 wPt-667294 59.9 wPt-7217 48.8 wPt-666223 79.8 wPt-1655 wPt-2938
49.5 wPt-741084 QTKW.ndsu.3A2 86.4 wPt-7890 QGPC.ndsu.2D
QNdD.ndsu.2D 50.1 wPt-666656 wPt-0085
QDH.ndsu.3A
QMMLPI.ndsu.2D.1
QMx.ndsu.3A1 QSS.ndsu-2D
QDM.ndsu.3A1 wPt-1353 QNdISk.ndsu.2D 50.6 wPt-2652 wPt-6259 87.1
QDH.ndsu.2D 51.1 wPt-667266 wPt-6854 wPt-9369
QMEPT.ndsu.2D.2 96.5 QMMLPI.ndsu.2D.2 wPt-0650
QKSk.ndsu.2D 52.1
QPH.ndsu.2D wPt-7992 QNd.ndsu.2D
QALT.ndsu.2D 53.0 wPt-741302 QSL.ndsu.2D
QTKW.ndsu.2D 56.3 wPt-667671 119.2 tPt-0242
QMMLPT.ndsu.2D
QMEPT.ndsu.2D.1
QGY.ndsu.2D.1 QMx.ndsu.3A2 QDM.ndsu.3A.2 59.4 wPt-731489 148.4 wPt-0286 63.1 wPt-731406 149.4 wPt-1562 wPt-3816
QNS.ndsu.2D
65.0 wPt-664714 165.2 wPt-2740 QKS.ndsu.2D
166 65.7 wPt-9900
QGY.ndsu.2D.2 66.1 wPt-733889 wPt-732277 165.9 wPt-744743 66.4 wPt-671859 0.0 wPt-741078 66.7 wPt-667406 wPt-6647452.4 wPt-671711 wPt-741848 67.0 wPt-671737 wPt-6718923.0 wPt-743858 0.0wPt-668120 wPt-741078wPt-6704 68.6 wPt-1554 3.6 wPt-741816 wPt-671711 wPt-741848 3A-3 68.9 2.4wPt-6850 6.1 wPt-743909 wPt-731134 wPt-743858 0.0 wPt-1681 70.5 3.0 6.7 wPt-742486 0.0 tPt-7492 72.1 3.6wPt-667476 wPt-741816 3A-2 1.3 wPt-2755 wPt-733932 8.1 wPt-742039 1.7 wPt-730156 73.4 6.1 wPt-743909 2.7 wPt-7608 74.0 wPt-733033 8.7 wPt-742665 0.0 wPt-1681 74.5 6.7wPt-732509 wPt-742486wPt-671669 0.0 tPt-7492 9.2 wPt-741986 wPt-7421181.3 wPt-2755 wPt-1339 QGY.ndsu.3A 74.8 8.1wPt-730568 wPt-742039 1.7 wPt-73015628.7
75.1 QTKW.ndsu.3A1 wPt-671914 wPt-66712413.5 wPt-9303 wPt-7608 8.7 wPt-742665 2.7 QKNd.ndsu.3A 76.8 wPt-5014 13.9 wPt-740730 QLd.ndsu.3A 79.3 9.2wPt-668017 wPt-741986 wPt-742118 wPt-133954.5 wPt-9160 wPt-0398 QGY.ndsu.3A 35.9 wPt-667640 28.7 82.8QALB.ndsu.3A wPt-671623 59.0 wPt-5263 QTKW.ndsu.3A1 13.5 wPt-9303 87.7 wPt-667536 36.3 tPt-1079
QKNd.ndsu.3A 90.5 13.9wPt-4242 wPt-740730 wPt-1464 QLd.ndsu.3A 48.2 54.5 wPt-9160 wPt-0398 QMMLPT.ndsu.3A 94.5 35.9wPt-730613 wPt-667640 QALB.ndsu.3A 48.9 wPt-9928 59.0 wPt-5263 98.3 36.3wPt-732270 tPt-1079 105.4 wPt-730427 50.4 wPt-9634 48.2wPt-667485 wPt-1464wPt-6064 QMMLPT.ndsu.3A 57.1 wPt-664488 QNS.ndsu.3A 107.8 48.9wPt-741208 wPt-992859.9 wPt-7217 109.0 50.4wPt-732923 wPt-9634 wPt-665836 wPt-73205279.8 wPt-1655 wPt-2938 QTKW.ndsu.3A2 57.1 wPt-664488 Consistent QTL QNS.ndsu.3A 109.8 wPt-0153 wPt-66776586.4 wPt-7890 wPt-4329 wPt-7825wPt-7217 59.9 QDH.ndsu.3A QMx.ndsu.3A1
QDM.ndsu.3A1 87.1 wPt-1353 110.2 79.8wPt-5106 wPt-1655 wPt-2938
QTKW.ndsu.3A2 wPt-6854 wPt-9369 111.2 wPt-733725 96.5 Putative QTL
111.6 86.4wPt-6514 wPt-7890 wPt-7992 QDH.ndsu.3A QMx.ndsu.3A1 114.0 wPt-3757 QDM.ndsu.3A1 87.1 wPt-1353 tPt-0242
115.8 wPt-9848 119.2 QMx.ndsu.3A2 QDM.ndsu.3A.2 wPt-6854 wPt-9369 125.7 96.5wPt-3692 wPt-666518148.4 wPt-0286 SS-Related QTL 131.9 wPt-6780 wPt-7992149.4 wPt-1562 wPt-3816
119.2 tPt-0242165.2 wPt-2740
QMx.ndsu.3A2 QDM.ndsu.3A.2 148.4 wPt-0286165.9 wPt-744743 Fig. 4-2. Genetic map and QTL for 35 spike-related, agronomic, and149.4 qualitywPt-1562 traits ofwPt-3816 a RIL population derived from the cross of the elite line WCB414 and the exotic lineWCB617 with SS trait, over four to six165.2 locationswPt-2740 in ND, USA during 2009, 2010, and 2011(Continued) 165.9 wPt-744743
3B-1 3B-2 3B-3
3B-1 3B-2 3B-3
3D-1 3D-2
3B-1 3D-1 0.0 wPt-7225 0.0 wPt-1615 wPt-2858 4.8 wPt-9586 0.4 wPt-8446 3B-3 0.0 wPt-740945 8.1 wPt-741584 1.4 wPt-1620 1.3 rPt-1806 wPt-0965 8.6 wPt-669355 wPt-741201 0.0 wPt-7225 3.8 wPt-744251 0.0 wPt-1615 wPt-2858 wPt-740590 wPt-741484 12.6 wPt-7739 2.6 0.4 wPt-8446 5.7 wPt-3260 4.8 wPt-9586 wPt-729938 wPt-741190 18.0 wPt-667895 1.4 wPt-1620 7.8 wPt-10972 8.1 wPt-741584 2.9 wPt-742255 40.1 wPt-6834 3.8 wPt-744251 18.2 wPt-666139 8.6 wPt-669355 wPt-741201 3.2 wPt-730115 wPt-741102 47.6 wPt-0912 5.7 wPt-3260 30.7 wPt-741331 wPt-741413 12.6 wPt-7739 3.8 wPt-733405 wPt-740976 0.0 wPt-8096 wPt-5906 54.8 wPt-8959 7.8 wPt-10972 32.3 wPt-0267 wPt-741750 18.0 wPt-667895 6.1 wPt-742264 8.8 wPt-5769 56.1 wPt-6856 wPt-10349 18.2 wPt-666139 33.9 wPt-5916 40.1 wPt-6834 7.7 wPt-741252 wPt-743515 56.4 wPt-3342 30.7 wPt-741331 wPt-74141337.3 wPt-3536 wPt-0302 47.6 wPt-0912 8.1 wPt-9810 59.4 rPt-0996 wPt-2119 32.3 wPt-0267 wPt-74175043.7 wPt-48420.0 wPt-8096 wPt-590634.6 wPt-7301 54.8 wPt-8959 9.0 wPt-5163
QTKW.ndsu.3B.3 60.1 wPt-731500 33.9 wPt-5916 44.0 tPt-64878.8 wPt-5769 56.1 wPt-6856 wPt-10349 9.6 wPt-741500 62.0 wPt-8845 37.3 wPt-3536 wPt-0302 wPt-2299 wPt-0013 56.4 wPt-3342 10.6 wPt-734051 64.2 wPt-3760 43.7 wPt-4842 wPt-732330 wPt-733544 59.4 rPt-0996QDH.ndsu.3B.1 wPt-2119 wPt-742530 wPt-733412 69.6 34.6 wPt-7301 65.8 wPt-2685 3B-1 44.0 tPt-6487 3B-2 wPt-2766 wPt-0895 QTKW.ndsu.3B.3 3B-3 60.1 wPt-731500 wPt-742329 wPt-742443 QTKW.ndsu.3B2 66.3 wPt-731663 wPt-2299 wPt-0013 wPt-2298 62.0 wPt-8845 wPt-742171 wPt-741733 95.2 wPt-6216 64.2 wPt-3760 77.2 wPt-4412 0.0 wPt-731103
wPt-732330 wPt-733544 QDH.ndsu.3B.1 wPt-740619 wPt-741216
QDM.ndsu.3B QDH.ndsu.3B.2 3D-1 3D-2 QPC.ndsu.3B.2 79.8 wPt-1311 69.6 wPt-2766 wPt-089596.3 wPt-9579 65.8 wPt-2685 10.9 wPt-741037 wPt-6965 0.8 wPt-667148
QTKW.ndsu.3B2 wPt-7266 wPt-8396 wPt-2298 96.7 wPt-2936 66.3 wPt-731663 wPt-2464 wPt-9335 2.0 wPt-0732 wPt-4370 wPt-0365 95.2 wPt-6216 98.9 wPt-1191 77.2 wPt-4412 wPt-742148 wPt-740873 3.6 wPt-741510 wPt-733983
QDM.ndsu.3B 80.2 wPt-0405 wPt-1822 QDH.ndsu.3B.2 wPt-9579 108.9 wPt-6945 QPC.ndsu.3B.2 79.8 wPt-1311 wPt-741495 wPt-742463 3.9 wPt-742339 wPt-2814 96.3 QPC.ndsu.3B.1 wPt-7956 wPt-0438 QGPC.ndsu.3D wPt-2936 114.8 wPt-1159 wPt-10130 wPt-7266 wPt-8396 wPt-741157 QLd.ndsu.3D 22.1 wPt-740580 96.7 wPt-740945 11.4 wPt-732044 0.0 wPt-4370 wPt-0365 wPt-8141 wPt-5825 QNNdISk.ndsu.3D wPt-665093
167 124.1 33.2 98.9 wPt-1191 QNS.ndsu.3D 11.9 wPt-742030
QNS.ndsu.3B 1.3 rPt-1806 wPt-0965 QTKW.ndsu.3B1 80.6 wPt-4576 QDH.ndsu.3D 108.9 wPt-6945 tPt-1759 wPt-6973 wPt-740590 wPt-741484 80.2 wPt-0405 wPt-1822 15.8 wPt-741522 43.7 wPt-6169
QPC.ndsu.3B.1 124.8 2.6 QMMLPI.ndsu.3D
tPt-1366 wPt-729938 wPt-741190 wPt-7956 wPt-0438 QFE.ndsu.3D 46.9 wPt-9258 QDM.ndsu.3D 114.8 wPt-1159 wPt-10130 19.9 wPt-9401 124.1 wPt-732044 125.3 wPt-7015 2.9 wPt-742255 wPt-8141 wPt-5825 22.8 wPt-740703 wPt-730115 wPt-741102
QNS.ndsu.3B 3.2 QTKW.ndsu.3B1 tPt-1759 wPt-6973125.8 wPt-9170 wPt-664393 80.6 wPt-4576 24.4 wPt-741829 wPt-733640 124.8 3.8 wPt-733405 wPt-740976 tPt-1366 134.3 wPt-743661 wPt-110326.1 wPt-742264 wPt-740662 wPt-741800 125.3 wPt-7015 134.6 rPt-5396 7.7 wPt-741252 wPt-743515 wPt-740803 wPt-741333 125.8 wPt-9170 wPt-664393148.1 wPt-731910 wPt-43648.1 wPt-9810 24.7 wPt-741656 wPt-741987 9.0 wPt-5163 wPt-1615 wPt-2858 0.0 wPt-7225134.3 wPt-743661 wPt-11032 0.0 wPt-742431 wPt-741558 9.6 wPt-741500 wPt-9586 0.4 wPt-8446134.6 rPt-5396 4.8 wPt-741767 wPt-740640 10.6 wPt-734051 wPt-741584 1.4 wPt-1620148.1 wPt-731910 wPt-4364 wPt-742530 wPt-733412 8.1 25.0 wPt-741521 wPt-6693553D-wPt-7412012 3.8 wPt-744251 wPt-742329 wPt-742443 8.6 25.3 wPt-740665 wPt-741984 wPt-742171 wPt-741733 12.6 wPt-7739 5.7 wPt-3260 wPt-731103 25.7 wPt-742156 wPt-740619 wPt-741216 18.0 wPt-6678950.0 7.8 wPt-10972 10.9 0.8 wPt-667148 26.4 wPt-1968 3B-2 wPt-741037 wPt-6965 40.1 wPt-6834 18.2 wPt-666139 wPt-2464 wPt-9335 2.0 wPt-0732 26.9 wPt-1516 30.7 wPt-741331 wPt-741413 wPt-742148 wPt-740873 47.6 wPt-09123.6 wPt-741510 wPt-733983 3.9 wPt-742339 wPt-2814 27.2 wPt-1336 32.3 wPt-0267 wPt-741750 0.0 wPt-8096 wPt-5906 wPt-741495 wPt-742463 54.8 wPt-8959
QGPC.ndsu.3D 30.7 wPt-669255 8.8 wPt-5769 11.4 wPt-741157 56.1QLd.ndsu.3D wPt-685622.1 wPt-10349wPt-740580 33.9 wPt-5916 QNNdISk.ndsu.3D 33.2 wPt-665093 wPt-3094 QNS.ndsu.3D 11.9 wPt-742030 42.3
56.4 QDH.ndsu.3D wPt-3342
37.3 wPt-3536 wPt-0302 15.8 wPt-741522 43.7 wPt-6169 QMMLPI.ndsu.3D 59.4 rPt-0996QFE.ndsu.3D 46.9 wPt-2119wPt-9258 43.7 wPt-4842 34.6 wPt-7301 19.9 wPt-9401 QDM.ndsu.3D wPt-740703 44.0 tPt-6487 22.8 QTKW.ndsu.3B.3 60.1 wPt-731500 Consistent QTL wPt-2299 wPt-0013 24.4 wPt-741829 wPt-733640 62.0 wPt-8845 wPt-740662 wPt-741800 64.2 wPt-3760
wPt-732330 wPt-733544 QDH.ndsu.3B.1 69.6 wPt-740803 wPt-741333 wPt-2766 wPt-0895 24.7 wPt-741656 wPt-741987 65.8 wPt-2685 Putative QTL QTKW.ndsu.3B2 wPt-2298 wPt-742431 wPt-741558 66.3 wPt-731663 95.2 wPt-6216 wPt-741767 wPt-740640 77.2 wPt-4412
25.0 wPt-741521QDM.ndsu.3B QDH.ndsu.3B.2 QPC.ndsu.3B.2 79.8 wPt-1311 SS-Related QTL 96.3 wPt-9579 25.3 wPt-740665 wPt-741984 96.7 wPt-2936 25.7 wPt-742156 wPt-7266 wPt-8396 98.9 wPt-1191 26.4 wPt-1968 wPt-4370 wPt-0365 Fig. 4-2. Genetic map and QTL for 35 spikewPt-1516-related, agronomic, and quality traits of a RIL population derived from the cross of the elite line 108.9 wPt-6945 26.9 80.2 wPt-0405 wPt-1822 QPC.ndsu.3B.1 27.2 wPt-1336 wPt-7956 wPt-0438 114.8 wPt-1159WCB414wPt-10130 and the exotic lineWCB61730.7 with wPt-669255SS trait, over four to six locations in ND, USA during 2009, 2010, and 2011(Continued)
124.1 wPt-732044 42.3 wPt-3094 wPt-8141 wPt-5825 QNS.ndsu.3B QTKW.ndsu.3B1 tPt-1759 wPt-6973 80.6 wPt-4576 124.8 tPt-1366 125.3 wPt-7015 125.8 wPt-9170 wPt-664393 134.3 wPt-743661 wPt-11032 134.6 rPt-5396 148.1 wPt-731910 wPt-4364 4A 4B-1
4A 4B-1
0.0 wPt-2247 3.1 wPt-8841 4.9 wPt-2946 4B-2 5A-1 5A-2 5D 11.6 tPt-9400 4A 18.4 wPt-743165 19.8 wPt-0817 0.0 wPt-2247 21.6 rPt-7285 3.1 wPt-8841 22.7 wPt-6728 4.9 wPt-2946 23.3 wPt-744614 4B-1 11.6 tPt-9400 23.6 wPt-3810 wPt-743165 18.4 wPt-1091 wPt-744595 wPt-4280 19.8 wPt-0817 24.9 0.0 21.6 rPt-7285 26.9 wPt-5951 wPt-1272 22.7 wPt-6728 30.3 wPt-5857 2.3 wPt-734310 wPt-732448 23.3 wPt-744614 42.1 wPt-7926 5.5 wPt-5559 23.6 wPt-3810 wPt-1046 wPt-1091 wPt-744595 wPt-428062.4 wPt-9833 6.8 24.9 0.0
26.9 wPt-5951 wPt-1272 62.7 wPt-4645 QKVW.ndsu.4B
QKS.ndsu.4A
QNNd.ndsu.4A QKNd.ndsu.4A
30.3 wPt-5857 2.3 wPt-734310 wPt-73244890.0 wPt-0798 QGPC.ndsu.4B QTKW.ndsu.4A 42.1 wPt-7926 QFE.ndsu.4A 5.5 wPt-5559 100.2 wPt-0150 QMEPT.ndsu.4A wPt-1046 62.4 wPt-9833 6.8 wPt-742056 wPt-2951
62.7 wPt-4645 QKVW.ndsu.4B
QKS.ndsu.4A
QNNd.ndsu.4A QKNd.ndsu.4A QGPC.ndsu.4B
90.0 wPt-0798 103.9 wPt-744256 wPt-664749 QTKW.ndsu.4A QFE.ndsu.4A 100.2 wPt-0150 wPt-672107 4B-2
QMEPT.ndsu.4A QPP.ndsu.4A 4B-2 wPt-742056 wPt-29515A-1 5A-2 104.3 wPt-68675D wPt-0447 0.0 wPt-0596 wPt-5870 wPt-744256 wPt-664749 0.0 tPt-0602
103.9 105.6 wPt-742020 wPt-2631 0.0 wPt-9094 0.0 wPt-1038 3.3 wPt-667413 QSL.ndsu.4A wPt-672107 QALB.ndsu.4A 12.0 wPt-732423
QPP.ndsu.4A 13.1 wPt-1903 wPt-3334 3.7 wPt-665027 104.3 wPt-6867 wPt-0447 108.7 wPt-9675 wPt-1961 23.6 wPt-8892 23.2 wPt-800131
105.6 wPt-742020 wPt-2631 110.5 wPt-8967 wPt-3845 31.0 wPt-744434 wPt-8226
QALM.ndsu.5A QNS.ndsu.4B
QSL.ndsu.4A 24.3
QPC.ndsu.4A
QALT.ndsu.5A QALB.ndsu.4A
108.7 wPt-9675 wPt-1961 42.9 wPt-1101 wPt-6016 QNS.ndsu.5A
112.3 wPt-1007 QTKW.ndsu.5A QLd.ndsu.4B
110.5 wPt-8967 wPt-3845 QNd.ndsu.4B 53.8 wPt-6209 QPC.ndsu.4A 4B-2 5A-1 wPt-669526 5A-2QPH.ndsu.4B 5D 113.0 QSL.ndsu.4B wPt-1007 wPt-9067 112.3 wPt-2836 58.6 113.0 wPt-669526 113.6 59.7 wPt-7062 113.6 wPt-2836 114.9 wPt-6390 70.7 wPt-2214 114.9 wPt-6390 119.9 rPt-7987 86.4 wPt-744934 rPt-7987 119.9 wPt-734161 wPt-0764 103.5 wPt-7412 tPt-8905
168 wPt-734161 wPt-0764 120.2 120.2 wPt-8657 wPt-4487 wPt-8657 wPt-4487 119.6 wPt-5303 wPt-731166 121.7 wPt-731166 121.7 QALM.ndsu.4B 123.0 wPt-5642 123.0 wPt-5642 123.8 wPt-1161 wPt-4680 123.8 wPt-1161 wPt-4680 124.2 wPt-6757 wPt-6757 127.1 wPt-3250 124.2 5A-2 127.1 wPt-3250
0.0 wPt-0596 wPt-5870 0.0 tPt-0602 0.0 wPt-9094 0.0 wPt-1038 3.3 wPt-667413 12.0 wPt-732423 13.1 wPt-1903 wPt-3334 3.7 wPt-665027 23.6 wPt-8892 23.2 wPt-800131
31.0 wPt-744434 wPt-8226 QALM.ndsu.5A QNS.ndsu.4B 24.3 QALT.ndsu.5A
42.9 wPt-1101 wPt-6016 QNS.ndsu.5A
QTKW.ndsu.5A QLd.ndsu.4B QNd.ndsu.4B 53.8 wPt-6209
QPH.ndsu.4B QSL.ndsu.4B 58.6 wPt-9067 59.7 wPt-7062 70.7 wPt-2214 86.4 wPt-744934 103.5 wPt-7412 tPt-8905 Consistent QTL 119.6 wPt-5303 5A-1
QALM.ndsu.4B 0.0 wPt-0596 wPt-5870 0.0 tPt-0602 0.0 wPt-9094 0.0 wPt-1038Putative QTL3.3 wPt-667413 12.0 wPt-732423 13.1 wPt-1903 wPt-3334 3.7 wPt-665027 23.6 wPt-8892 23.2 wPt-800131
31.0 wPt-744434 wPt-8226 QALM.ndsu.5A QNS.ndsu.4B 24.3 QALT.ndsu.5A SS-Related QTL
42.9 wPt-1101 wPt-6016 QNS.ndsu.5A
QTKW.ndsu.5A QLd.ndsu.4B QNd.ndsu.4B 53.8 wPt-6209 QPH.ndsu.4B QSL.ndsu.4B 58.6 wPt-9067 59.7 wPt-7062 70.7 wPt-2214 wPt-744934 Fig. 4-2. Genetic map and QTL for 86.435 spike-related, agronomic, and quality traits of a RIL population derived from the cross of the elite line 103.5 wPt-7412 tPt-8905 WCB414 and the exotic lineWCB617 with SS trait, over four to six locations in ND, USA during 2009, 2010, and 2011 (Continued)
119.6 wPt-5303 QALM.ndsu.4B
5B
6A-1 6A-2
5B 0.0 wPt-9724 6A-1 0.7 wPt-8604 11.0 wPt-7237 0.0 wPt-730591 17.1 wPt-3085 1.5 wPt-2636 wPt-0864 17.8 rPt-6127 3.7 wPt-4836 26.5 wPt-5737 7.4 wPt-741630 20.8 wPt-9687 34.8 tPt-8942 wPt-4736 22.3 wPt-1285
QNS.ndsu.5B.1 38.1 wPt-2041 24.5 wPt-669498 QPP.ndsu.5B 60.6 wPt-7418 28.4 wPt-0228 65.6 wPt-742141 wPt-5514 32.2 wPt-1377 90.7 wPt-3049 37.8 wPt-9832 106.2 wPt-6531 43.6 wPt-671561 QALM.ndsu.5B 43.9 wPt-0562 wPt-741170 QALT.ndsu.5B 106.7 wPt-8393 46.7 wPt-5964 QAAL.ndsu.5B.1 wPt-3457 109.0 53.8 wPt-733976 109.7 wPt-1895 wPt-9679 wPt-3524 112.3 wPt-1548 55.8 wPt-731934 0.0 wPt-666773
135.8 wPt-6191 wPt-2707 56.1 wPt-7027 3.2 wPt-731592 QGPC.ndsu.5B QMEPT.ndsu.5B wPt-6014 56.6 tPt-2833 wPt-731842 wPt-9089 tPt-4178 158.7 3.6 wPt-2880
187.5 wPt-7006 57.7 wPt-8719 QNdD.ndsu.5B
QMMLPT.ndsu.5B 6.0 wPt-7809 wPt-666964 QPH.ndsu.5B 58.1 rPt-9065
207.2 wPt-1060 11.1 wPt-732951
QFE.ndsu.6A QALM.ndsu.6A wPt-666574 tPt-0877 QDH.ndsu.6A 4B-2 5A-1 5A-2 5D 220.7 wPt-4402 61.8 wPt-6904 wPt-671855 12.7 wPt-6696
QPC.ndsu.5B.1 QLPC.ndsu.5B.1 221.7 wPt-0295 77.9 wPt-732760
229.1 wPt-7665 wPt-9116 78.2 wPt-3965 wPt-7623 QKNd.ndsu.5B
236.1 wPt-0484 QSS.ndsu-6A.2 78.5 wPt-731010
237.1 wPt-6465 QNdISk.ndsu.6A.2 79.7 wPt-667170 QAAL.ndsu.6A 238.9 wPt-3922 QALB.ndsu.6A 82.1 wPt-4791 wPt-667780 wPt-2822
QKVW.ndsu.6A2 6A-1 244.4 6A-2wPt-6971 QALT.ndsu.6A
QPC.ndsu.5B.2 82.4 wPt-664733 wPt-733764 QLPC.ndsu.5B.2 wPt-2373 251.8 wPt-9584
QNS.ndsu.5B.2 263.3 wPt-8449 83.7 wPt-666208
169 263.9 wPt-1348
QMx.ndsu.6A 84.7 wPt-9131 wPt-8539 QKVW.ndsu.5B QNS.ndsu.5B.3 289.1 wPt-3995 wPt-671799 QKVW.ndsu.6A1 85.0 292.6 wPt-3569 86.6 tPt-6278 307.8 wPt-744851 89.9 wPt-666988
QSS.ndsu-5B 94.8 tPt-7399
QAAL.ndsu.5B.2 tPt-4875 wPt-0929 0.0 wPt-730591 308.2 wPt-744750 95.6 wPt-666074 113.6 wPt-733115 1.5 wPt-2636 wPt-0864 309.1 wPt-1457 114.5 wPt-731413 3.7 wPt-4836 114.9 wPt-730711 wPt-730109 7.4 wPt-741630 115.4 wPt-3091 115.9 wPt-0357 20.8 wPt-9687 117.7 wPt-0139 22.3 wPt-1285 118.1 wPt-666224 24.5 wPt-669498 120.4 wPt-3308 122.4 wPt-7445 28.4 wPt-0228 124.8 wPt-729806 32.2 wPt-1377 148.9 wPt-667618 wPt-7063 5D37.8 wPt-9832 150.7 tPt-8557 43.6 wPt-671561 153.7 wPt-729839 0.0 43.9 wPt-0596wPt-0562wPt-5870wPt-741170 0.0 tPt-0602 0.0 wPt-9094 0.0 wPt-1038 3.3 46.7 wPt-667413wPt-5964 6A-2 12.0 wPt-732423 wPt-1903 wPt-3334 3.7 53.8 wPt-665027wPt-733976 23.6 wPt-8892 13.1 23.2 wPt-800131 wPt-9679 wPt-3524
31.0 wPt-744434 wPt-8226 55.8 QALM.ndsu.5A QNS.ndsu.4B 24.3 wPt-666773 QALT.ndsu.5A wPt-731934 0.0
42.9 wPt-1101 wPt-6016 QNS.ndsu.5A
QTKW.ndsu.5A 56.1 wPt-7027 3.2 wPt-731592 Consistent QTL QLd.ndsu.4B QNd.ndsu.4B 53.8 wPt-6209 wPt-9089 tPt-4178 QPH.ndsu.4B QSL.ndsu.4B 56.6 tPt-2833 wPt-731842 58.6 wPt-9067 3.6 57.7 wPt-8719 wPt-2880 59.7 wPt-7062 58.1 rPt-9065 6.0 wPt-7809 wPt-666964 Putative QTL 70.7 wPt-2214 11.1 wPt-732951
QFE.ndsu.6A QALM.ndsu.6A wPt-666574 tPt-0877 QDH.ndsu.6A 86.4 wPt-744934 61.8 wPt-6904 wPt-671855 12.7 wPt-6696 103.5 wPt-7412 tPt-8905 77.9 wPt-732760 SS-Related QTL wPt-3965 wPt-7623 119.6 wPt-5303 78.2
QSS.ndsu-6A.2 78.5 wPt-731010
QALM.ndsu.4B Fig. 4-2. Genetic map and QTL for 35 spike-related, agronomic, and quality traits of a RIL population derived from the cross of the elite line
QNdImSk.ndsu.6A.2 79.7 wPt-667170 QAAL.ndsu.6A WCB414 and theQALB.ndsu.6A exotic lineWCB61782.1 wPt-4791 with SS trait, over four to six locations in ND, USA during 2009, 2010, and 2011 (Continued)
wPt-667780 wPt-2822 QKVW.ndsu.6A2 QALT.ndsu.6A 82.4 wPt-664733 wPt-733764 wPt-9584 83.7 wPt-666208
QMx.ndsu.6A 84.7 wPt-9131 wPt-8539 wPt-671799
QKVW.ndsu.6A1 85.0 86.6 tPt-6278 89.9 wPt-666988 94.8 tPt-7399 95.6 wPt-666074 113.6 wPt-733115 114.5 wPt-731413 114.9 wPt-730711 wPt-730109 115.4 wPt-3091 115.9 wPt-0357 117.7 wPt-0139 118.1 wPt-666224 120.4 wPt-3308 122.4 wPt-7445 124.8 wPt-729806 148.9 wPt-667618 wPt-7063 150.7 tPt-8557 153.7 wPt-729839 6B-1 6B-2
6B-1 6B-2 0.0 wPt-1784 10.5 wPt-6994 15.6 wPt-4706 17.4 wPt-7954 6B-119.4 wPt-3800 19.8 wPt-0882 wPt-8239 0.0 21.1wPt-1784 wPt-743522 10.5 22.9wPt-6994 wPt-664276 15.6 24.8wPt-4706 wPt-7207 17.4 26.2wPt-7954 wPt-3130 wPt-3800 6B-2 19.4 26.5 wPt-9990 wPt-8563 19.8 28.8wPt-0882 wPt-8239wPt-5066 wPt-743522 6D 7A-1 7A-2 7A-3 21.1 29.4 wPt-1089 wPt-9255 22.9 wPt-664276 0.0 wPt-6116 wPt-6674 24.8 30.7wPt-7207 5.5 wPt-1541 26.2 33.3wPt-3130 wPt-4564 6.5 wPt-0171 26.5 40.3wPt-9990 wPt-8563wPt-1756 8.7 wPt-4164 28.8 45.1wPt-5066 wPt-3116 9.5 wPt-732061 29.4 48.5wPt-1089 wPt-9255wPt-5234 0.0 wPt-611612.2 wPt-9256 30.7 53.4wPt-6674 wPt-2102 5.5 wPt-154115.0 wPt-0406 33.3 wPt-4564
57.8 wPt-6127 QMx.ndsu.6B1 6.5 wPt-0171 QPP.ndsu.6B.2
QPC.ndsu.6B.1 17.8 wPt-1264 wPt-3045
QKSk.ndsu.6B QPH.ndsu.6B1
40.3 wPt-1756 QALM.ndsu.6B.1 8.7 wPt-4164 QPSS.ndsu-6B.1 62.6 wPt-1437 wPt-2095 QKNd.ndsu.6B 26.6 wPt-6878 45.1 wPt-3116 wPt-6594 9.5 wPt-732061
QALM.ndsu.6B.2 65.0 48.5 wPt-5234 27.0 wPt-744795 wPt-9660 67.3 wPt-4142 12.2 wPt-9256 QLPP.ndsu.6B3 wPt-2102 31.7 wPt-3207 53.4QMx.ndsu.6B2 wPt-0406 67.7 wPt-4930 15.0
57.8 wPt-6127 QMx.ndsu.6B1 QPP.ndsu.6B.2
QPC.ndsu.6B.1 17.8 wPt-1264 wPt-3045
QKSk.ndsu.6B QPH.ndsu.6B1 QALM.ndsu.6B.1
QGPC.ndsu.6B1 wPt-745074 72.3 QPSS.ndsu-6B.1 62.6 wPt-1437 wPt-2095 QKNd.ndsu.6B 26.6 wPt-6878 74.4wPt-6594 wPt-6667 QALM.ndsu.6B.2 65.0 27.0 wPt-744795 wPt-9660 wPt-4142 0.0 wPt-9796
QNdImSk.ndsu.6B.2 67.3 wPt-665017 wPt-3526
QLPP.ndsu.6B3 75.1 31.7 wPt-3207 QMx.ndsu.6B2 QMMLPI.ndsu.6B1 67.7 82.2wPt-4930 wPt-666793 1.8 wPt-7734
QGPC.ndsu.6B1 wPt-745074 QGPC.ndsu.6B2 72.3 85.4 wPt-5256 25.0 wPt-8418 QTKW.ndsu.6B
74.4 86.7wPt-6667 wPt-8814 25.4 wPt-742051 QNdISk.ndsu.6B.2 QKVW.ndsu.6B 75.1 wPt-665017 wPt-3526 26.6 wPt-2371 QMMLPI.ndsu.6B1 88.1 wPt-741515 82.2 wPt-666793 6D QGY.ndsu.6B 30.2 wPt-6417 QPH.ndsu.6B.2 92.3 wPt-1241 QGPC.ndsu.6B2 85.4 wPt-5256 QTKW.ndsu.6B 33.2 wPt-3059 86.7 101.3wPt-8814 wPt-3605 QKVW.ndsu.6B 0.0 wPt-1695 88.1 102.3wPt-741515wPt-664250 34.0 wPt-2199 QMMLPI.ndsu.6B2 1.1 wPt-5114 wPt-672044 0.0 wPt-4637
QGY.ndsu.6B 34.4 wPt-6966 QPH.ndsu.6B.2
170 QMMLPT.ndsu.6B2 92.3 108.6wPt-1241 wPt-2297 QLd.ndsu.6B wPt-2100 wPt-4796 0.0 wPt-1032 101.3 113.3wPt-3605 wPt-5408 1.5 wPt-664719 36.2 wPt-8300 4.9 QPP.ndsu.6B.1 wPt-6083 1.1 wPt-5338 102.3 113.7wPt-664250wPt-8412 2.6 wPt-741955 wPt-672075 wPt-672051
QMMLPI.ndsu.6B2 4.6 wPt-666615 37.9 8.3 wPt-3002 wPt-9555 28.3 wPt-5558 wPt-6013 QMMLPT.ndsu.6B2 108.6 wPt-2297 wPt-665253 wPt-672171
QLd.ndsu.6B 130.1 wPt-9971 113.3 wPt-5408 5.3 wPt-732847 11.8 wPt-0514 40.9 wPt-1706 QPP.ndsu.6B.1 38.5 wPt-664558 wPt-4742 QKNd.ndsu.7A
139.0 QNS.ndsu.7A 113.7 wPt-8412 39.0 wPt-3572 41.2 wPt-5524 wPt-671471 QPSS.ndsu.6B.2 139.4 wPt-744396 130.1 wPt-9971 141.4 wPt-2218 39.3 wPt-2523 65.1 wPt-4489 139.0 wPt-4742 65.8 wPt-7122 141.8 wPt-743974 wPt-744302 QDM.ndsu.7A.2 43.0 wPt-4778
QPSS.ndsu.6B.2 139.4 wPt-744396
QNdD.ndsu.7A.1 67.2 wPt-0971 147.4wPt-2218 wPt-0052 44.7 wPt-6967 141.4 69.4 wPt-2083 141.8 155.6wPt-743974tPt-1723wPt-744302 46.0 wPt-7113 147.4 155.9wPt-0052 wPt-6247 46.5 wPt-4960 wPt-8043 84.3 wPt-1601
QMx.ndsu.7A 95.0 wPt-0551 QDM.ndsu.7A.1 tPt-1723 QDM.ndsu.7A.3 47.0 wPt-8149 155.6 167.4 wPt-741530 QNdD.ndsu.7A.2 155.9 167.7wPt-6247 wPt-742929 wPt-6522 47.9 wPt-8700 100.1 wPt-7763 167.4 184.1wPt-741530wPt-7935 48.8 wPt-3794 wPt-742929 wPt-6522 167.7 184.5 rPt-1040 wPt-5257 wPt-5101 184.1 wPt-7935 52.9 tPt-6794 185.5rPt-1040 wPt-2564 184.5 Consistent QTL 59.6 wPt-0008 185.5 193.6wPt-2564 wPt-3168 193.6 195.6wPt-3168 wPt-743099 67.2 wPt-740561 195.6 208.9wPt-743099wPt-3284 Putative QTL 72.2 wPt-3135 208.9 221.8wPt-3284 wPt-3581 90.5 rPt-4199 221.8 222.1wPt-3581 wPt-9881 222.1 243.9wPt-9881 wPt-1048 243.9 wPt-1048 SS-Related QTL
Fig. 4-2. Genetic map and QTL for 35 spike-related, agronomic, and quality traits of a RIL population derived from the cross of the elite line WCB414 and the exotic lineWCB617 with SS trait, over four to six locations in ND, USA during 2009, 2010, and 2011 (Continued)
6D 7A-1 7A-2 7A-3
6D 7A-1 7A-2 7A-3
7B-1 7B-2
0.0 wPt-9796 1.8 wPt-7734 25.0 wPt-8418 25.4 wPt-742051 26.6 wPt-2371 30.2 wPt-6417 33.2 wPt-3059 7A-1 7A-3 0.0 wPt-1695 34.0 wPt-2199 0.0 wPt-9796 1.1 wPt-5114 wPt-672044 34.4 wPt-6966 1.8 0.0 wPt-7734wPt-4637 wPt-2100 wPt-4796 0.0 wPt-1032 1.5 wPt-664719 36.2 wPt-8300 25.0 4.9 wPt-8418 2.6 wPt-741955 wPt-672075 wPt-672051 25.4 wPt-742051wPt-6083 1.1 wPt-5338 4.6 wPt-666615 37.9 wPt-665253 wPt-672171 26.6 8.3 wPt-2371wPt-3002 wPt-9555 28.3 wPt-5558 wPt-6013 11.8 wPt-0514 7B-1 0.0 wPt-744769 5.3 wPt-732847 38.5 wPt-664558 30.2 wPt-6417 40.9 wPt-1706 wPt-7108 QKNd.ndsu.7A 5.4 QNS.ndsu.7A wPt-3059 0.0 wPt-4025 39.0 wPt-3572 33.2 41.2 wPt-5524 wPt-671471 2.1 wPt-0920 wPt-8246 wPt-4140 0.0 wPt-1695 34.0 wPt-2199 wPt-4489 5.7 tPt-8569 39.3 wPt-2523 0.0 wPt-463765.1 2.5 wPt-1587 1.1 wPt-5114 wPt-672044 34.4 wPt-6966 65.8 wPt-7122 3.4 wPt-8919 6.3 wPt-3445 QDM.ndsu.7A.2 43.0 wPt-4778 wPt-2100 wPt-4796 0.0 wPt-1032 1.5 wPt-664719 36.2 wPt-8300 4.9 10.0 wPt-0312 wPt-2449 wPt-733669 QNdD.ndsu.7A.1 67.2 wPt-0971 6.6 6D 7A-12.6 44.7wPt-741955wPt-6967 7A-2 7A-3wPt-6083 1.1 wPt-5338 18.9 wPt-2305 wPt-2933 wPt-672075 wPt-672051 69.4 wPt-2083 4.6 46.0wPt-666615 wPt-7113 37.9 wPt-665253 wPt-672171 8.3 wPt-3002 wPt-9555 28.3 wPt-5558 wPt-60137B-3 20.2 wPt-9516 7D-1 7.5 wPt-6696937D-2 84.3 wPt-1601 wPt-6372 wPt-3873 8.0 wPt-0866 5.3 46.5wPt-732847wPt-4960 wPt-8043 38.5 wPt-664558 11.8 wPt-0514 40.9 wPt-1706 26.0 QKNd.ndsu.7A wPt-5677 QNS.ndsu.7A 10.5
QMx.ndsu.7A 95.0 wPt-0551 34.6 wPt-8981 wPt-9665 QDM.ndsu.7A.1 QDM.ndsu.7A.3 47.0 wPt-8149 39.0 wPt-3572 QNdD.ndsu.7A.2 41.2 wPt-5524 wPt-671471 11.1 wPt-744187
39.1 wPt-2273 QMx.ndsu.7B2 QPH.ndsu.7B.1 100.1 wPt-7763 QDM.ndsu.7B.2
47.9 wPt-8700 39.3 wPt-2523 65.1 wPt-4489 54.6 wPt-3723 QMx.ndsu.7B1 13.8 wPt-4620
QALB.ndsu.7B
QALT.ndsu.7B QAAL.ndsu.7B
wPt-3794 65.8 wPt-7122 QDH.ndsu.7B 16.1 wPt-734169
48.8 QDM.ndsu.7A.2 43.0 wPt-4778 79.1 wPt-4258
QALM.ndsu.7B.1 QDM.ndsu.7B.1 7B-1 7B-2 QLd.ndsu.7B 22.5 wPt-7387 wPt-5257 wPt-5101 44.7 wPt-6967 QNdD.ndsu.7A.1 67.2 wPt-0971 89.3 wPt-744652 52.9 69.4 wPt-2083 93.5 wPt-743502 23.8 wPt-9515 tPt-8504 tPt-6794 46.0 wPt-7113 wPt-9013 84.3 wPt-1601 94.5 wPt-0266 26.2
wPt-4960 wPt-8043 QALM.ndsu.7B.2 59.6 wPt-0008 46.5 96.0 wPt-4045 34.2 wPt-742417
QMx.ndsu.7A 95.0 wPt-0551 QDM.ndsu.7A.1 QDM.ndsu.7A.3 wPt-8149
47.0 QPSS.ndsu-7B.2 67.2 wPt-740561 QNdD.ndsu.7A.2 96.9 wPt-8233 wPt-06350.0 wPt-744917 34.6 wPt-743215 72.2 wPt-3135 47.9 wPt-8700 100.1 wPt-7763 97.7 wPt-0194 10.6 wPt-664368 35.4 wPt-744632 48.8 wPt-3794 101.3 wPt-8615 90.5 rPt-4199 QKSk.ndsu.7B 11.6 wPt-744347 wPt-744521 wPt-5257 wPt-5101 QPSS.ndsu-7B.1 106.2 wPt-2407 52.9 QGPC.ndsu.7B wPt-9987 14.9 wPt-665730 QKVW.ndsu.7B 112.0 tPt-6794 QPH.ndsu.7B2 wPt-665471 QNdD.ndsu.7B.3 121.6 wPt-665428 15.9 59.6 wPt-0008 123.7 wPt-9299 17.2 wPt-733729 67.2 wPt-740561 0.0 wPt-9796 wPt-5975 wPt-589218.5 wPt-745062 1.8 wPt-7734 72.2 wPt-3135 124.0 wPt-7887 171 wPt-664047 90.5 rPt-4199 131.7 wPt-1266 19.9 25.0 wPt-8418 20.4 wPt-744512 25.4 wPt-742051
wPt-664412 wPt-743860 26.6 wPt-2371 20.8 wPt-743651 30.2 wPt-6417 7A-2 7B-2 wPt-743671 wPt-743380 33.2 wPt-3059 21.1 wPt-745070 0.0 wPt-1695 wPt-2199 34.0 0.0 wPt-744769 7B-3 wPt-743140 wPt-663792 1.1 wPt-5114 wPt-672044 wPt-6966 0.0 wPt-4637 34.4 5.4 wPt-7108 wPt-742911 wPt-5049 1.5 wPt-664719 wPt-8300 0.0 wPt-2100wPt-4025wPt-4796 0.0 wPt-1032 36.2 4.9 wPt-8246 wPt-4140 0.0 wPt-744897 21.4 wPt-745121 wPt-743666 0.0 wPt-664387 2.6 wPt-741955 wPt-672075 wPt-672051 2.1 wPt-6083wPt-0920 1.1 wPt-53385.7 tPt-8569 5.2 wPt-9072 wPt-0366 wPt-743306 wPt-9822 wPt-1628 4.6 wPt-666615 37.9 wPt-665253 wPt-672171 8.3 2.5 wPt-3002wPt-1587wPt-9555 28.3 wPt-5558 wPt-6013 6.3 wPt-3445 5.6 wPt-1557 tPt-9518 wPt-744124 1.6 wPt-2054 tPt-4614
5.3 wPt-732847 38.5 wPt-664558 11.8 3.4 wPt-0514wPt-8919 40.9 wPt-1706 QKNd.ndsu.7A QNS.ndsu.7A wPt-2449 wPt-733669 30.3 wPt-2883 wPt-665112 21.7 wPt-745068 wPt-667548 39.0 wPt-3572 10.0 wPt-0312 41.2 wPt-55246.6 wPt-671471 wPt-2933 wPt-664517 QMx.ndsu.7D wPt-743384 18.9 wPt-2305 65.1 wPt-4489 31.1 22.3 39.3 wPt-2523 wPt-669693 wPt-671801 QMMLPI.ndsu.7D wPt-742858 20.2 wPt-9516 65.8 wPt-71227.5 32.4 22.9 QDM.ndsu.7A.2 43.0 wPt-4778 26.0 wPt-6372 wPt-3873 8.0 wPt-0866 wPt-743510 wPt-744736 wPt-6967 QNdD.ndsu.7A.1 67.2 wPt-0971 23.5 44.7 10.5 wPt-5677 wPt-742840 wPt-7113 34.6 wPt-8981 wPt-9665 69.4 wPt-2083 46.0 11.1 wPt-744187 24.1 wPt-743193 wPt-0992
39.1 wPt-2273 QMx.ndsu.7B2 QPH.ndsu.7B.1 46.5QDM.ndsu.7B.2 wPt-4960 wPt-8043 84.3 wPt-1601 QMx.ndsu.7B1 13.8 wPt-4620 25.8 wPt-744818
QMx.ndsu.7A 54.6 wPt-3723 95.0 wPt-0551
QDM.ndsu.7A.3 QDM.ndsu.7A.1
wPt-8149 QALB.ndsu.7B
47.0 QALT.ndsu.7B QNdD.ndsu.7A.2
QAAL.ndsu.7B wPt-664309 wPt-744675 79.1 wPt-4258 QDH.ndsu.7B 16.1 wPt-734169 Consistent QTL QALM.ndsu.7B.1 wPt-7763 100.1 QDM.ndsu.7B.1
wPt-8700 QLd.ndsu.7B 47.9 22.5 wPt-7387 wPt-669154 wPt-664317 wPt-3794 89.3 wPt-744652 27.0 48.8 23.8 wPt-9515 tPt-8504 wPt-664971 wPt-744265 wPt-5257 wPt-5101 93.5 wPt-743502 26.2 wPt-9013 Putative QTL wPt-744866 wPt-743790 52.9 tPt-6794 94.5 wPt-0266 QALM.ndsu.7B.2 34.2 wPt-742417 27.6 wPt-664286 wPt-744656 59.6 wPt-0008 96.0 wPt-4045
QPSS.ndsu-7B.2 34.6 wPt-743215 wPt-744388 wPt-664020 wPt-740561 96.9 wPt-8233 wPt-0635 27.9 67.2 35.4 wPt-744632 SS-Related QTL wPt-663992 wPt-743269 72.2 wPt-3135 97.7 wPt-0194 101.3 wPt-8615 28.6 wPt-663849 90.5 QKSk.ndsu.7B rPt-4199 QPSS.ndsu-7B.1 106.2 wPt-2407 31.2 wPt-0303 QGPC.ndsu.7B wPt-9987 wPt-743096 wPt-665260 QKVW.ndsu.7B 112.0 34.2 QPH.ndsu.7B2 wPt-743549 Fig.QNdD.ndsu.7B.3 4-2. Genetic map121.6 and QTLwPt-665428 for 35 spike-related, agronomic, and quality traits of a RIL population derived from the cross of the elite line 123.7 wPt-9299 35.3 wPt-3359 WCB414 and the exotic lineWCB617wPt-5975 wPt-5892 with SS trait, over four to six locations in ND, USA during 2009, 2010, and 2011 (Continued) 52.7 wPt-743501 wPt-744300 124.0 wPt-7887 131.7 wPt-1266
7B-3 7D-1 7D-2
7B-3 7D-1 7D-2 0.0 wPt-744917 10.6 wPt-664368 11.6 wPt-744347 wPt-744521 14.9 wPt-665730 15.9 wPt-665471 7D-1 17.2 wPt-733729 0.0 wPt-744917 18.5 wPt-745062 10.6 wPt-664368 19.9 wPt-664047 11.6 wPt-744347 wPt-74452120.4 wPt-744512 14.9 wPt-665730 wPt-664412 wPt-743860 15.9 wPt-665471 20.8 wPt-743651 17.2 wPt-733729 wPt-743671 wPt-743380 18.5 wPt-745062 21.1 19.9 wPt-664047 wPt-745070 7D-2 20.4 wPt-744512 wPt-743140 wPt-663792 wPt-664412 wPt-743860 wPt-742911 wPt-5049 0.0 wPt-74489720.8 wPt-743651 21.4 wPt-745121 wPt-743666 0.0 wPt-664387 5.2 wPt-9072 wPt-743671 wPt-743380 wPt-0366 wPt-743306 wPt-9822 wPt-1628 21.1 5.6 wPt-1557 tPt-9518 wPt-745070 wPt-744124 1.6 wPt-2054 tPt-4614 30.3 wPt-2883 wPt-665112wPt-743140 wPt-66379221.7 wPt-745068 wPt-667548
31.1 wPt-664517 wPt-742911QMx.ndsu.7D wPt-504922.3 wPt-743384 0.0 wPt-74489732.4 wPt-67180121.4 wPt-745121QMMLPI.ndsu.7D wPt-74366622.9 wPt-7428580.0 wPt-664387 5.2 wPt-9072 wPt-0366 wPt-743306 wPt-743510wPt-9822wPt-744736wPt-1628 5.6 wPt-1557 tPt-9518 wPt-744124 23.5 wPt-7428401.6 wPt-2054 tPt-4614 30.3 wPt-2883 wPt-665112 21.7 wPt-745068 wPt-667548 24.1 wPt-743193 wPt-0992 31.1 wPt-664517 QMx.ndsu.7D 22.3 wPt-743384 32.4 wPt-671801 QMMLPI.ndsu.7D 22.9 wPt-742858 25.8 wPt-744818 wPt-743510 wPt-744736 wPt-664309 wPt-744675 23.5 wPt-669154 wPt-664317 wPt-742840 27.0 24.1 wPt-743193 wPt-0992 wPt-664971 wPt-744265 25.8 wPt-744818 wPt-744866 wPt-743790Consistent QTL wPt-664309 wPt-74467527.6 wPt-664286 wPt-744656 wPt-669154 wPt-664317 wPt-744388 wPt-664020 27.0 wPt-664971 wPt-74426527.9 wPt-663992 wPt-743269Putative QTL wPt-744866 wPt-74379028.6 wPt-663849 27.6 wPt-664286 wPt-74465631.2 wPt-0303 wPt-744388 wPt-664020 27.9 wPt-743096 wPt-665260SS-Related QTL wPt-663992 wPt-74326934.2 wPt-743549 wPt-663849 28.6 35.3 wPt-3359 31.2 wPt-0303 wPt-743096 wPt-66526052.7 wPt-743501 wPt-744300 34.2 wPt-743549 35.3 wPt-3359 52.7 wPt-743501 wPt-744300
Fig.4-2. Genetic map and QTL for 35 spike-related, agronomic, and quality traits of a RIL population derived from the cross of the elite line WCB414 and the exotic lineWCB617 with SS trait, over four to six locations in ND, USA during 2009, 2010, and 2011 (Continued)
The QTL mapping of MMLPT resulted in the identification of six QTL including two consistent
QTL (QMMLPT.ndsu.1D, and QMMLPT.ndsu.5B) (Table 4-3). The number of QTL identified in individual
environments for MMLPT ranged from two (Prosper 2009; Carrington 2009; and Prosper 2010) to four
(Carrington 2010). The PV explained by all the QTL identified in individual environments ranged from
23.3%-56.2%, while the PV explained by individual QTL ranged from 3.5% to 40.3%. Among all these
QTL, QMMLPT.ndsu.1D had a major effect on MMLPT as it explained up to 40.3% of PV and was
consistently detected in all the environments. The other five minor QTL explained between 3.5% and
7.9% of PV (Table 4-3). The elite parent WCB414 contributed alleles for increased values of MMLPT at
four loci, including both the major (QMMLPT.ndsu.1D) and minor consistent QTL (QMMLPT.ndsu.5B)
(Table 4-3).
The trait MMLPI was associated with two consistent QTL (QMMLPI.ndsu.1B, and
QMMLPI.ndsu.1D) and six putative QTL in this population (Table 4-3). The PV explained by individual
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QTL ranged from 3.8% to 37.1% and the PV explained by all QTL in individual ranged 32.4% to 52.1%. A major QTL (QMMLPI.ndsu.1D) explaining up to 37.1% of PV for MLPI, located on 1DL was consistently detected in all the environments. Another major QTL which explained up to 17.7% PV for MMLPI was located on 2DS (QMMLPI.ndsu.2D.1), but was identified in only one environment. The remaining minor
QTL explained 3.8% to 10.8% of PV (Table 4-3). The elite parent WCB414 contributed the alleles for increased values of MMLPI at QMMLPI.ndsu.1D, QMMLPI.ndsu.6B.1, and QMMLPI.ndsu.6B.2); while the exotic parent WCB617 provided the alleles that increased the values of this trait at the remaining loci
(Table 4-3).
CIM for Mx resulted in the detection of a total of 10 QTL which include two consistent QTL
(QMx.ndsu.6B.2 and QMx.ndsu.7D). The PV explained by individual QTL for Mx ranged from 5.3% to
19.9%, while PV explained by all QTL in individual environments ranged from 29.0% to 48.8%. Among all the consistent QTL located on 6BS (QMx.ndsu.6B.2) explained the highest PV by 19.9% for Mx. Another major QTL of Mx, explained up to 15.6% PV and was located on 3A (QMx.ndsu.3A.1), but could be detected in single environment only. The remaining eight QTL explained a minor portion of the PV, ranging from 5.3% to 11.0%. WCB 414 alleles increased the trait values at three loci (QMx.ndsu.3A.2,
QMx.ndsu.6A, and QMx.ndsu.6B.2), while WCB 617 alleles contributed to increase phenotypic values of
Mx at remaining seven loci (QMx.ndsu.2B.2, QMx.ndsu.3A.1, QMx.ndsu.6B.1, QMx.ndsu.7A,
QMx.ndsu.7B.1, QMx.ndsu.7B.2, and QMx.ndsu.7D).
4.5. Discussion
4.5.1. Phenotypic variations
A better performance of WCB414 for TKW, KVW, FE, and mixograph-related traits was expected, considering that this genotype is an elite line. In most of the environments, the RIL population showed transgressive segregation (Fig. 4-1; Supplementary Table 4-1) for all the traits, demonstrating the allelic contribution of both parents and the formation of new recombinant lines. As was reported in previous studies, the quality traits had genotype × environment interaction (Lukow and McVetty, 1991; Peterson et al., 1992; Gross et al., 2003; Huang et al., 2006; Raman et al., 2009; Sun et al., 2009; Wang et al., 2009;
Tsilo et al., 2010; Heidari et al,. 2011; Tsilo et al., 2011; Simons et al., 2012) (Table 4-1). The influence of
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the environment on the expression of the quality traits was also confirmed by the inconsistent correlations between some traits. For instance, TKW and GPC were negatively associated in two environments, but positively associated in one environment. Likewise, the low values of heritability observed in most of these traits (except FE) indicate a strong influence of the environment on the phenotypic the values.
The positive significant associations observed between FE and TKW, as well as between FE and
KVW are expected considering the impact of grain characteristics on milling characteristics. Similarly, the associations between GPC and mixograph related traits were expected considering that dough mixing properties are influenced by the quality and quantity of proteins (Finney, 1997). The positive and strong associations among several of the mixograph-related traits are suggesting the presence of common genes controlling these traits.
4.5.2. QTL for TKW, KVW and FE
The detection several QTL associated to TKW, KVW, and FE is in agreement with the quantitative genetic control and high environment influence on these traits, as demonstrated in previous studies (Campbell et al., 1999; Campbell et al., 2001; Börner et al., 2002; Gross et al., 2003; Huang et al.,
2003; Huang et al., 2004; Huang et al., 2006; McCartney et al., 2005; Kuchel et al., 2006; Kunert et al.,
2007; Li et al., 2007; Cuthbert et al., 2008; Sun et al., 2009; Sun et al., 2010; Raman et al., 2009; Wang et al., 2009; Tsilo et al., 2010; Heidari et al., 2011; Bennet et al., 2012; Simons et al., 2012; Maphosa et al., 2013). Breeders and millers use both TKW and KVW to determine the potential phenotypic values of
FE in their genotypes (Wheat Marketing Center, 2008). Indeed, in this study, these three traits were positively correlated (Table 4-3). Consequently, it is reasonable to expect QTL with pleiotropic effects or close linkage, for these traits. CIM indicated that 1B is the only chromosome bearing QTL for TKW
(QTKW.ndsu.1B), KVW (QKVW.ndsu.1B), and Fe (QFE.ndsu.1B). However, these QTL were located at different positions and could be considered different loci. Nevertheless, other chromosomes also harbored QTL for two of these traits as well. For example, chromosomes 2A and 6B had QTL for TKW
(QTKW.ndsu.2A) and KVW (QKVW.ndsu.2A.1 and QKVW.ndsu.2A.2); 1A and 6A had QTL for KVW
(QKVW.ndsu.1A) and FE (QFE.ndsu.1A); and 2B had QTL for TKW (QTKW.ndsu.2B) and FE
(QFE.ndsu.2B). However, in all of these cases, the QTL for different traits were located at different
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positions suggesting absence of linked genes or common genes with pleiotropic effects. Exception to this trend was observed on chromosome 4A, where the positions of QTKW.ndsu.4A and QFE.ndsu.4A overlapped suggesting a pleiotropic effect of this genetic region on TKW and FE (Table 4-3; Fig. 4-2).
QTKW.ndsu.4A was a major QTL for TKW located on 4AL (Table 4-3, Fig. 4-2). This could be an excellent target for the selection of TKW through molecular markers, considering the fact that this QTL was consistently detected in all the environments. Previous study also reported the presence of a QTL controlling TKW on 4AL (Araki et al., 1999; McCartney et al., 2005). In addition to the co-localization with
QFE.ndsu.4A; QTKW.ndsu.4A was also co-located with putative QTL associated to KNd
(QKNd.ndsu.4A), Nd (QNNd.ndsu.4A) and KS (QKS.ndsu.4A) identified earlier in the same population
(Chapter 3) (Fig. 4-2). This cluster of QTL suggests a pleiotropic effect of this genetic region on spike and kernel development with impact on grain-quality traits. Chromosome 4A is affected by a pericentrical inversion that moved several of the ancestral genes located on 4AS to 4AL and vice versa (Hernandez et al., 2012). QTL for KVW (Huang et al., 2006; Sun et al., 2009), GY (Gross et al., 2003; McCartney et al.,
2005; Marza et al., 2006), and Ld (Marza et al 2006) have been also located on 4AL.
Kernel volume weight (also called test weight) is a grade-determining factor of wheat quality defined as the weight of grain per unit of volume (Steve et al., 2004; Wheat Marketing Center, 2008). A major QTL QKVW.ndsu.6A.1 for KVW, detected consistently in all the environments, was located on the distal region of 6AL. The QTL allele from WCB414 increased KVW and could be considered for MAS in wheat breeding programs aimed at increasing KVW. In this population, QKVW.ndsu.6A.1 was also co- located with a minor and putative QTL for Mx detected in Carrington 2010. Indeed, a positive correlation was also observed between KVW and Mx (Table 4-2) in this environment. A previous study also detected a QTL associated to KVW on the distal region of 6AL in Chinese winter wheat varieties (Sun et al., 2009).
Another QTL associated to KVW under a water-limited environment was also detected on 6AL but in a different position (Bennett et al., 2012). The distal region of 6AL is also known to harbor QTL for grain weight per ear and leaf erectness (Börner et al., 2002).
Another major and consistent QTL for KVW was located 2A (QKVW.ndsu.2A.1), which explained up to 22.5% of PV for this trait. In this population, this genomic region has also been found to be associated with QTL for Aless and ALB (Chapter 3; Fig 4-2). Although phenotypically there was no
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evidence of association between Aless and KVW, there is possibility that either there are tightly linked different genes controlling different traits a common locus having pleiotropic effect on apical awnleted expression and KVW. Another novel and consistent QTL associated to KVW was located on the distal region of chromosome 1AL (QKVW.ndsu.1A). Although, this region was associated with only KVW in this population, the distal region of 1AL has been shown to harbor QTL associated to Fusarium head blight
(FHB) and deoxynivalenol (DON) toxin produced by FHB (Semagn et al., 2007).
The trait FE has the highest proportion of consistent QTL among all the eight quality traits studied in this population, which makes them suitable for MAS. All the chromosomes except 2B, where QTL for
FE were identified in this study, have been reported to carry QTL for FE (Nelson et al., 2006; Kuchel et al., 2006; Simons et al., 2012). The novel QTL located on 2BS (QFE.ndsu.2B), although had major effect, it was detected in one environment only and needs further validation before it could be used in MAS.
QFE.ndsu.1A was detected in all the four environments studied as well as across environments (Table 4-
3). A previous study (Kuchel et al. 2006) has also reported a QTL on 1A associated with FE in a double haploid population derived from two commercial wheat varieties. However this QTL was detected in one environment only. QFE.ndsu.1A was located on the long arm of chromosome 1A and its confidence interval (CI) overlapped a QTL rich region associated with ALT (QALT.ndsu.1A), NS (QNS.ndsu.1A), KNd
(QKNd.ndsu.1A), NNdISk (QNNdISk.ndsu.1A), Nd (QNd.ndsu.1A), and PP (QPP.ndsu.1A.3) (Chapter 2;
Fig 4-2).
Another consistent QTL QFE.ndsu.1B for FE was located on 1B and was detected in both locations (Prosper, Carrington) in the year 2010 as well as AE. Previous study also reported a QTL for FE in a population derived from the cross of a soft red spring wheat and a HRSW (Simons et al. 2012). The chromosome 3D also harbor a consistent QTL (QFE.ndsu.3D) for FE. Recently, a QTL for FE also was detected on the distal region of 3DL under water-limited conditions (Maphosa et al., 2013). QTL for other traits like DH (QDH.ndsu.3D) and DM (QDM.ndsu.3D) have also been identified in this population in the same region where QFE.ndsu.3D was located (Chapter 3; Fig.4-2). The QTL alleles for higher FE and early heading and maturity at those loci were contributed by WCB414 suggesting that the selection of
QTL alleles for higher FE could also result in early heading and maturity. This was also supported by negatives correlation observed between FE and DH, as well as between FE and DM (data not shown).
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QFE.ndsu.6A was the only QTL associated with FE, for which the alleles from the branched parent WCB617 were responsible for its increase. Considering the high stability in detection of
QFE.ndsu.6A (Table 4-3), alleles of the exotic parent at this QTL are excellent candidate to be introduced into the wheat breeding programs. QFE.ndsu.6A was located on the long arm of chromosome 6AL, where
QTL for DH was reported earlier in the same population (QDH.ndsu.6A). QTL for FE has also been reported on 6A in a previous study using a population derived from two commercial wheat varieties
(Kuchel et al., 2006).
4.5.3. QTL for GPC and mixograph-related traits
GPC affects mixing properties of wheat (Finney, 1997). Therefore, it is important for breeding purpose to recognize genetic regions controlling GPC and dough strength. For this purpose, this study detected a consistent QTL on 5BL with pleiotropic effect on GPC (QGPC.ndsu.5B), MEPT
(QMEPT.ndsu.5B), and MMLPT (QMMLPT.ndsu.5B) (Table 4-3; Fig. 4-2). For this QTL, alleles from the exotic parent increased GPC but decreased the mixograph-related values. These results are in agreement with the negative correlations observed between GPC among mixograph-related traits in the present study (Table 4-2) as well as with some previous studies that have showed negative associations among these traits (Huang et al., 2006; Simons et al., 2012). The QTL for GPC on 5BL by itself has been reported in several other studies (Gross et al., 2003; Kulwal et al., 2005; Conti et al., 2011; Bordes et al.,
2011, 2013). However, similar to our finding, a recent study also reported a common QTL for GPC and
MMLPT on 5BL (Simons et al., 2012). In both studies, the QTL were located in the distal region of 5BL.
This QTL represents probably the gene earlier identified on 5BL (Cane et al., 2008; Maphosa et al., 2013) that encode for the protein Serpin (Serine proteinase inhibitor) (Rasmussen et al., 1996). This protein represents up to 4% total protein in the grains of monocot cereals (Østergaard et al. 2000) and has been shown to affect quality traits such as FE (Cane et al., 2008).
Another closely linked QTL for GPC and mixograph-related traits also were observed on 6BS.
The QTL for GPC (QGPC.ndsu.6B) and Mx (QMx.ndsu.6B) had consistent and major effect, while another QTL found in this region for MMLPI (QMLPI.ndsu.6B) had minor effect. At these loci on 6BS, the elite parent WCB414 contributed alleles that increased phenotypic values of these traits. Probably the
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highly consistent QTL, QGPC.ndsu.6B.1, is associated to gliadins genes located on 6BS (Payne et al.,
1987).
Past studies have also studied a large number of mixograph-related traits. Examples of these traits are mixograph midline peak value, mixograph line peak integral, mixograph weakening slope, and mixograph total energy. However, to the date, there is no consensus on the merits of each of these mixograph-related traits in the selection of the best dough properties (Ingelin, 1997). Moreover, often time, the information generated from different studies is not interchangeable since it is derived from devices designed by different manufactures (Ingelin, 1997). In this study, to the best of our knowledge, this is the first time that QTL for MEPT are reported. However, QTL for MMLPT and MMLPI have been identified in few earlier studies (Campbell et al., 2001; Huang et al., 2006; Tsilo et al., 2011; Li et al.,
2012b; Simons et al., 2012; Mergoum et al., 2013). QTL controlling MEPT (QMEPT.ndsu.1B), MMLPT
(QMMLPT.ndsu.1B) and MMLPI (QMMLPI.ndsu.1B) were detected on chromosome arm 1BS, which coincides with the location of Glu-B3 gene (McCartney et al., 2006, Mann et al., 2009; Maphosa et al.,
2013), a gene that encode for a LMW-GS (Simons et al., 2012). The positive alleles responsible for increasing MEPT, MMLPT, and MMLPI for loci located on 1BS were contributed by the exotic parent, demonstrating the potential use of WCB617 in improving wheat quality traits. This locus on 1BS controlling mixograph-related traits, also overlaps with a cluster of other QTL associated with GPC
(QGPC.ndsu.1B), and KVW (QKVW.ndsu.1B) as shown in the present study (Fig. 4-2) and PC
(QPC.ndsu.1B) identified previously in the same RIL population (chapter 3). The chromosome 1B is well documented to play an important role in wheat quality as it harbors several genes for gluten strength and other quality traits (Campbell et al., 2001; Huang et al., 2006; Mann et al., 2009; McCartney et al., 2006;
Tsilo et al., 2010; Kumar et al., 2013; Maphosa et al., 2013). Comparison of map position, however, suggests that the QTL identified in the present study on 1BS are probably different than Glu-B1. However further investigation may needed to clarify this result.
The most consistent genomic region associated with mixograph-related traits was identified on chromosome 1D, where major QTL for MEPT (QMEPT.ndsu.1D), MMLPT (QMMLPT.ndsu.1D), MMLPI
(QMMLPI.ndsu.1D) were identified. The position of these QTL coincides with the position of Glu-D1 gene at 73.0 cM (Mann et al., 2009) that encode for an HMW-GS. The high PV explained by these QTL
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(15.4%-41.2%) and their stability across environments confirms previous studies results which identified major QTL/QTL-clusters associated with mixograph properties as well as with other quality traits, in the region near by Glu-D1 (Campbell et al., 2001; Huang et al., 2006; Nelson et al., 2006; Mann et al., 2009;
Tsilo et al., 2010; Simons et al., 2012).
As there were high correlations between MEPT, MMLPT and MMLPI, it was expected to identify common loci controlling these traits. However, some of the QTL specific to only one mixograph-related traits were also identified. For example, two QTL located on 2BL (QMEPT.ndsu.2B) and 4AL
(QMEPT.ndsu.4A) were specific to MEPT; one QTL located on chromosome 3A (QMMLPT.ndsu.3A) was specific to MMLPT and three QTL located on 3DS (QMMLPI.ndsu.3D), 6BS (QMMLPI.ndsu.6B.1), and
7DS (QMMLPI.ndsu.7D) were specific to MMLPI. Most of these QTL, although reported first time, have minor effects and were unstable across environments. This suggests a need to further study these QTL, before any recommendations could be made for their use in improving wheat quality.
Interestingly, the trait Mx shared only one common QTL with MMLPI, which was located on chromosome 7DS at 0.01 cM (Table 4-3; Fig. 4-2). The lack of additional common QTL among Mx and mixograph-related traits could be due to difference in the methodologies conducted to assess each trait.
Mx is assessed through a visual scale; while the information of MEPT, MMLPT, and MMLPI is collected from the Mixmart software. Although, Mx is broadly used by the breeders to select germplasm with optimal dough strength, the genetic dissection of this trait have not received much attention in the past. In one study, the presence of four QTL located on chromosomes 1B, 1D, 3B, 6D controlling Mx was reported (Tsilo et al., 2011). However, all those QTL differ from the QTL described in our study, which were located on 2BS, 3A, 6AL, 6BS, 6BL, 7AS, 7BL, and 7DS. Two consistent QTL associated with Mx were identified; one on chromosome 6B (QMx.ndsu.6B.2) and the other on 7D (QMx.ndsu.7D). Once again, the exotic parent WCB617 played an important role in providing alleles that increased the values of
Mx at seven QTL, including the consistent QTL located on 7D (Table 4-3). This shows the potential use of
WCB617 as a source for improving some important wheat quality traits.
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4.5.4. Influence of SS loci on quality traits
The exotic parent (WCB617) used in the present study has SS phenotype. For this reason, it was used to compare the genomic locations of QTL identified for SS (Chapter 2) with QTL for quality traits identified in this study. This study identified QTL for TKW (QTKW.ndsu.2D), GPC (QGPC.ndsu.2D),
MEPT (QMEPT.ndsu.2D.1, QMEPT.ndsu.2D.2), MMLPT (QMMLPT.ndsu.2D), and MMLPI
(QMMLPI.ndsu.2D.1, QMMLPI.ndsu.2D.2) on chromosome 2D. The map location of these QTL have overlapping CI with a major QTL for the SS-phenotype and some other spike-related and agronomic traits mapped in the same region and/or they were mapped very close to each other on 2D (chapter 2 and chapter 3) (Fig. 4-2). This 2D region has been identified as a rich gene region previously (Erayman et al.
2004). Other genomic regions where minor quality QTL (KVW, Mx and GPC) were co-located/linked with
QTL for SS were on chromosome 6B and 7B. Interestingly, the co-localization of QTL for SS and quality related traits was also supported by significant correlations observed between SS and quality related traits (TKW, GPC, mixograph-related traits) (Appendix Table C3).
Except for TKW QTL on 2D genomic region, the positive QTL alleles for SS and quality traits were contributed by the exotic parent WCB617. Supporting our findings a study identified a cluster of QTL for mixograph and baking traits on 2D and also reported that the favorable QTL alleles were derived from wild wheat specie at those loci (Li et al., 2012). This clearly suggests the potential usefulness of exotic germplasm for quality traits improvement in wheat.
4.6. Conclusion
The use of a RIL population generated from an exotic germplasm with SS and a white elite wheat line in our study resulted in the identification of a large number of QTL for eight important wheat quality traits. A total of 69 QTL were detected for these traits, in which the exotic parent provided alleles with increasing effect in 51% of these QTL. These results suggest that germplasm with SS is a valuable resource to improve quality traits in wheat. Therefore, identifying molecular markers associated with consistent and/or major QTL detected in this study could be of great interest for wheat breeding programs.
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CHAPTER 5. GENERAL CONCLUSIONS
In this study, for the first time, a RIL population derived from an elite wheat line and an exotic wheat genotype with SS and glume pubescences was used to map QTL of 35 wheat traits. A total of 221
QTL were detected (Fig 5.1), out of them, 30% were spike-related QTL (including QTL for SS), 39% agronomic-related QTL, and 31% quality-related QTL. These QTL were distributed across the entire wheat genome, with 44% of the QTL on the B-genome, 37% of the QTL on A-genome, and only 19% of the QTL on the D-genome. Chromosome 6B had the highest number of QTL (23 QTL); meanwhile none
QTL were reported for chromosomes 4D, 5D and 6D. A polygenic control was found for most of the traits.
Most (81%) of individual QTL explained less than 15% of PV associated with the traits (minor QTL), and
19% of individual QTL explained more than 15% of PV (major QTL). Most of the QTL were putative (71%) demonstrating a high influence of the environment on their expression. In general, both parental genotypes contributed equally with favorable alleles that increased the effects of phenotypic values of the traits. Precisely, the elite parent (WCB414) provided 52% of these alleles; meanwhile the exotic parent provided 48%. Therefore, these results demonstrate the suitability of the exotic germplasm to discover new genes and alleles with potential use into the wheat breeding programs.
Whole genome QTL mapping of seven SS-related traits (PSS, Sk, SD, NdSS, SkNd, NdR, and
NdNonSS) resulted in the identification of seven stable QTL and showed a polygenic control of these traits. A major QTL was identified on 2DS (QSS.ndsu-2D) explained up to 25% of PV of SS trait. This
QTL was also reported on previous studies. The presence of another major QTL for SS on 7BL
(QSS.ndsu-7B.2), as well as the detection of five minor QTL located on 5BL (QSS.ndsu-5B), 6A
(QSS.ndsu-6A), 6BL (QSS.ndsu-6B.1 and QSS.ndsu-6B.2) and 7BL (QSS.ndsu-7B.1) were identified for the first time. Together these QTL explained up to 82% of PV of some traits such as NdSS). Digenic epistatic interaction observed between some QTL can be responsible for increase or decrease of SS expression in some genotypes. Further experiments will be required to physically detect and to clone
QSS.ndsu-2D, as well as to assess the high level interactions between the seven QTL detected in order to understand the nature of the SS inheritance in hexaploid wheat.
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Considering the impact of the SS trait on spike morphology, the genetic dissection of 10 spike- related traits (PP, PC, SL, Nd, NdD, ALT, ALM, ALB, AAL, Aless), was conducted. A total of 60 QTL were associated to these spike-related traits. Among these, 35% were consistent QTL and 65% putative QTL.
Chromosome 1A and 5B had the highest number of QTL associated to spike-related traits (8 QTL each).
Indeed, it was on 1AS where we detected QPP.ndsu.1A1, a QTL for glume pubescences that explained the maximum PV (92%) explained by a single QTL. For the spike-related traits, 55% of the alleles that increased phenotypic values were derived from the exotic parent (WCB 617).
To identify new QTL and/or alleles with agronomic potential value, ten agronomic traits (KS, KSk,
KNd, NdImmSk, GY, NS, PH, Ld, DH and DM) were investigated in this study. A total of 85 agronomic- related QTL were identified of which 81% were putative QTL and 81% were minor QTL. Despite of this large number of putative and minor QTL, some agronomic-related QTL were exceptionally consistent and explained a high percentage of the PV. For instances, QKSk.ndsu.2D was detected in all the environments studied and explained up to 39% of PV of KSk; and QGY.ndsu.2D1, detected in three environments as well as AE, explained up to 40% of PV of GY (the highest PV explained so far for GY).
Chromosomes 1A and 2D had the highest number of QTL (8 QTL each) associated with agronomic traits.
The elite parent WCB617 provided 60% of the alleles that increased phenotypic values of agronomic traits.
The results obtained for quality traits assessed (TKW, KVW, GPC, FE, Mx, MEPT, MMLPT, and
MMLPI) showed that the exotic parent has valuable genes which could be considered by breeders for the wheat improvement of traits such as GPC and gluten strength. The improvement of quality traits in wheat is a daunting challenge because most of these traits are controlled by several genes located in the D- genome which is the least diverse genome in wheat. A total of 69 quality-related QTL were identified in this study of which 72% were putative QTL. The parent with SS provided 52% of the alleles that increased phenotypic values of these quality traits. Several quality-related QTL were major and highly consistent QTL. For instance QTKW.ndsu.4A was detected in all the environments studied and explained up to 17% of the PV of TKW. Chromosome 6B had the highest number of QTL (9 QTL) for quality traits.
The impact of QPP.ndsu.1A1 and QSS.ndsu.2D, two major QTL for PP and SS, on other traits was evidenced in the clusters of QTL where these QTL were located. QPP.ndsu.1A1 was clustered on
191
1AS with loci for SL, Nd, DH, KS, GPC, GY and DM; meanwhile QSS.ndsu.2D was clustered on 2DS with loci for SL, Nd, NdD, ALT, KSk, NNdISk, DH, KS, MMLPI, PH, GY,TKW, GPC, MEPT, MMLPT, NS.
These groups of QTL suggest either close linkage or pleiotropic effects of the major loci for glume pubescences and SS on different spike-related, agronomic, and quality traits. This could be advantage for breeder, who could take advance of these genetic linkage/associations. For instance, breeders could consider the selection of pubescent spikes considering that QPP.ndsu.1A1 has a positive pleiotropic effect on number of kernels per spike. Likewise, breeders could attempt to break the repulsion phase linkage between QSS.ndsu.2D and QGY.ndsu.2D, to improve GY in genotyped with SS.
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APPENDIX A. SUPPLEMENTARY TABLES AND FIGURE FOR CHAPTER 2
Table A1. Mean values and RIL ranges of supernumerary-spikelet-related in each environment Environments‡ Carrington Carrington Carrington Prosper Prosper Prosper Trait† Experiment Categories All 2009 2010 2011 2009 2010 2011 Experiment 0.4 0.4 0.4 0.5 0.5 0.4 0.4 Parent WCB414 0.0 0.0 0.0 0.0 0.1 0.0 0.0 PSS Parent WCB 617 3.2 3.0 3.0 4.0 3.1 2.5 3.5 RILs 0.4 0.4 0.4 0.5 0.5 0.4 0.4 Minimum RILs 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Maximum RILs 4.0 4.0 4.0 4.0 4.1 4.0 4.0 Checks 0.0 0.0 0.0 0.0 0.0 0.0 0.0 LSD 0.05 0.4 0.6 0.6 0.5 0.4 0.5 0.4 Experiment 19.9 19.3 19.7 - 19.6 21.3 - Parent WCB414 15.1 16.1 15.3 - 13.9 14.2 - 193 Sk Parent WCB 617 44.2 39.5 50.4 - 31.0 52.0 -
RILs 20.0 19.3 19.7 - 19.8 21.4 - Minimum RILs 13.5 13.5 13.2 - 13.4 13.4 - Maximum RILs 51.0 60.3 51.5 - 52.9 63.8 - Checks 15.1 15.1 15.2 - 14.4 15.7 - LSD 0.05 6.6 3.5 7.2 - 2.9 6.7 - Experiment 2.1 2.1 2.1 - 2.1 2.2 - Parent WCB414 1.7 1.7 1.6 - 1.7 1.6 - SD Parent WCB 617 4.3 4.0 4.9 - 2.7 5.1 - RILs 2.1 2.1 2.1 - 2.1 2.2 - Minimum RILs 1.5 1.4 1.5 - 1.5 1.5 - Maximum RILs 5.1 6.0 5.5 - 5.0 6.2 - Checks 1.8 1.8 1.9 - 1.8 1.8 - LSD 0.05 0.4 0.4 0.7 - 0.3 0.7 - (Continues)
Table A1. Mean values and RIL ranges of supernumerary-spikelet-related in each environment (continued) Environments‡ Carrington Carrington Carrington Prosper Prosper Prosper Trait† Experiment Categories All 2009 2010 2011 2009 2010 2011 Experiment 1.2 1.2 1.2 - 1.2 1.2 - Parent WCB414 1.0 1.0 1.0 - 1.0 1.0 - SkNd Parent WCB 617 2.6 3.1 2.5 - 1.8 3.0 - RILs 1.2 1.2 1.2 - 1.2 1.2 - Minimum RILs 1.0 1.0 1.0 - 1.0 1.0 - Maximum RILs 3.6 4.6 4.0 - 4.1 4.2 - Checks 1.0 1.0 1.0 - 1.0 1.0 - LSD 0.05 0.3 0.3 0.5 - 0.3 0.6 - Experiment 0.9 0.8 0.9 - 1.0 1.0 - Parent WCB414 0.0 0.0 0.1 - 0.0 0.0 - Parent WCB 617 10.0 8.8 9.9 - 9.3 11.0 - NdSS § RILs 0.9 0.8 0.9 - 1.0 1.0 - Minimum RILs 0.0 0.0 0.0 - 0.0 0.0 - 194 Maximum RILs 10.0 10.8 10.2 - 11.5 12.9 -
Checks 0.0 0.0 0.0 - 0.0 0.0 - LSD 0.05 1.2 1.2 1.8 - 0.5 1.0 - Experiment 0.3 0.2 0.3 - 0.3 0.4 - Parent WCB414 0.0 0.0 0.0 - 0.0 0.0 - Parent WCB 617 3.2 3.5 4.4 - 0.5 3.6 - NdR RILs 0.3 0.2 0.3 - 0.3 0.4 - Minimum RILs 0.0 0.0 0.0 - 0.0 0.0 - Maximum RILs 5.0 5.8 5.7 - 5.1 6.9 - Checks 0.0 0.0 0.0 - 0.0 0.0 - LSD 0.05 0.7 0.6 1.1 - 0.5 1.3 - (Continues)
Table A1. Mean values and RIL ranges of supernumerary-spikelet-related in each environment (continued) Environments‡ Carrington Carrington Carrington Prosper Prosper Prosper Trait† Experiment Categories All 2009 2010 2011 2009 2010 2011 Experiment 16.6 16.5 16.2 - 16.4 17.2 - Parent WCB414 15.1 16.0 15.6 - 13.9 14.6 - NdNoSS Parent WCB 617 9.5 7.5 9.6 - 11.6 9.5 - RILs 16.6 16.6 16.3 - 16.6 17.3 - Minimum RILs 7.8 8.0 7.2 - 7.0 7.7 - Maximum RILs 22.1 21.6 20.8 - 23.1 25.4 - Checks 15.1 15.1 15.2 - 14.4 13.3 - LSD 0.05 1.7 2.3 2.7 - 2.0 2.4 - †PSS level of penetrance of supernumerary spikelets (scale from 0 to 4); Sk, number of spikelets (spikelets spike-1); SD spike density (Sk spike- longitude-1); SkNd spikelets per node (spikelet node-1); NdSS number of nodes with supernumerary spikelets per spike; NNdR number of nodes with extended rachillas (extended rachillas spike-1); NdNoSS number of nodes with no supernumerary spikelets per spike ‡In Carrington 2011 and Prosper 2011 only were conducted measurements of PSS §The four environments were not homogeneous in NdSS. Combined analysis for NdSS was performed with the three more homogeneous environments (Carrington 2009, Carrington 2010, and Prosper 2010)
195
Table A2. Coefficient of variance (CV%), and lattice efficiency (EF%) of supernumerary-spikelets-related traits Carrington 2009 Carrington 2010 Carrington 2011 Prosper 2009 Prosper 2010 Prosper 2011 Trait† CV% EF% CV% EF% CV% EF% CV% EF% CV% EF% CV% EF%
PSS 78.00 100.80 76.01 99.44 48.50 99.18 51.43 99.80 58.10 102.18 41.01 99.03
Sk 9.08 101.35 18.84 105.03 - - 7.52 101.73 16.29 98.95 - -
SD 9.19 99.34 16.10 103.00 - - 8.07 99.19 17.10 99.91 - -
SkNd 10.94 100.36 22.50 101.89 - - 10.96 100.06 24.79 99.47 - -
NdSS 78.62 99.67 96.27 105.75 - - 27.12 99.12 51.54 99.46 - -
NNdR 143.82 99.84 164.92 108.05 - - 95.40 102.44 177.97 99.09 - -
NdNoSS 6.90 101.21 8.41 100.05 - - 5.97 106.09 6.87 105.28 - -
† PSS level of penetrance of supernumerary spikelets (scale from 0 to 4); Sk, number of spikelets (spikelets spike-1); SD spike density (Sk spike- longitude-1); SkNd spikelets per node (spikelet node-1); NdSS number of nodes with supernumerary spikelets per spike; NNdR number of nodes with extended rachillas (extended rachillas spike-1);NdNoSS number of nodes with no superpernumerary spikelets per spike - non information collected
196
Table A3. Error mean square (EMS) and Fmax for supernumerary-spikelet-related traits Carrington Carrington Carrington Prosper Prosper Prosper Fmax Trait † 2009 2010 2011 2009 2010 2011 EMS EMS EMS EMS EMS EMS ratioa
PSS 0.082 0.099 0.061 0.054 0.061 0.034 2.91
Sk 3.054 13.752 - 2.183 12.089 - 6.30
SD 0.036 0.113 - 0.028 0.135 - 4.82
SkNd 0.016 0.071 - 0.017 0.092 - 5.75
NdSSb 0.400 0.786 - 0.070 0.279 - 2.81
NNdR 0.1005 0.270 - 0.061 0.481 - 7.89
NdNoSS 1.290 1.852 - 0.962 1.397 - 1.93
† PSS level of penetrance of supernumerary spikelets (scale from 0 to 4); Sk, number of spikelets (spikelets spike-1); SD spike density (Sk spike- longitude-1); SkNd spikelets per node (spikelet node-1); NdSS number of nodes with supernumerary spikelets per spike; NNdR number of nodes with extended rachillas (extended rachillas spike-1);NdNoSS number of nodes with non- supernumerary spikelets per spike - non information collected a Ratio: test of homogeneity (greatest EMS / smallest EMS) should be smaller than 10-fold b The four environments were not homogeneous in NdSS. Combined analysis of variance for NdSS was performed with the three more homogeneous environments (Carr09, Carr10, and Pros10).
197
Table A4. Correlation coefficients (r) among SS-related traits a Traits PSS Sk SD SkNd NdSS NdR NdNoSS 0.85**§ 0.80**§ 0.88**§ 0.74**§ PSSb 1 0.91**¶ -0.71**¶ 0.89**† 0.89**† 0.78**† 0.83**†
0.97**§ 0.94** § Sk 1 0.98**¶ 0.92**¶ -0.62**¶ 0.94**† 0.90**†
SD 1 0.96**¶ 0.91**¶ 0.94**¶ -0.66**¶
SkNd 1 0.91**¶ 0.96**¶ -0.76**¶
¶ ¶ 198 NdSS 1 0.82** -0.80**
NdR 1 -0.69**¶
** Significant at P < 0.01; †r from one environment; §, ¶ r pooled from three and four environments, respectively b Alternative pooled correlations were observed between the traits PSS-Sk, PSS-SD, and PSS-NdR. The lowest pooled r value is presented.
0.92 WCB414 0.93 WCB414
0.69 0.70
0.46 0.47
Relative frequency Relative frequency Relative
0.23 0.23
WCB617 WCB617
0.00 0.00 0.00 0.90 1.80 2.70 3.60 4.50 8.80 18.18 27.57 36.95 46.34 55.72 Penetrance of supernumerary spikelets (0-4) Number of spikelets per spike
0.94 WCB414 0.95 WCB414
0.70 0.71
0.47 0.47
Relative frequency Relative frequency Relative
0.23 0.24
WCB617 WCB617
0.00 0.00 1.08 1.98 2.89 3.79 4.70 5.60 0.66 1.07 1.48 1.89 2.30 2.71 3.12 3.53 3.94 Spike density (Number of spikelets/spike length) Number of spikelets per node
0.97 WCB414 0.95 WCB414
0.73 0.71
0.49 0.47
Relative frequency Relative Relative Relative frequency 0.24 0.24
WCB617 WCB617 0.00 0.00 0.00 1.14 2.27 3.41 4.55 5.69 0.00 2.92 5.84 8.76 11.69 Number of nodes with extended rachillas Number of nodes with supernumerary spikelets
0.56
0.42
0.28 Relative frequency Relative
0.14 WCB617
WCB414
0.00 6.36 8.51 10.66 12.81 14.96 17.12 19.27 21.42 23.57 Number of nodes with non supernumerary spikelets
Fig. A1. Frequency distribution of 163 RIL for SS-related traits over four to six environments
199
APPENDIX B. SUPPLEMENTARY TABLES AND FIGURES FOR CHAPTER 3
Table B1. Mean values and RIL ranges of supernumerary-spikelet-related traits in each environment Environments Carrington Carrington Carrington Prosper Prosper Prosper Trait† Experiment Categories All 2009 2010 2011 2009 2010 2011 All Experiment 1.1 1.1 1.1 1.1 1.1 1.1 1.1 Parent WCB414 0.0 0.0 0.0 0.0 0.0 0.0 0.0 PP Parent WCB 617 4.0 3.9 4.0 4.0 4.0 4.0 4.0 (0-4) RILs 1.2 1.1 1.2 1.1 1.2 1.1 1.1 Minimum RILs 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Maximum RILs 4.0 4.0 4.0 4.0 4.0 4.0 4.0 Checks 0.0 0.0 0.0 0.0 0.0 0.0 0.0 LSD 0.05 0.6 0.7 0.3 0.5 0.6 0.3 0.4 All Experiment 0.2 0.3 0.2 0.1 0.3 0.2 0.1 Parent WCB414 0.0 0.0 0.0 0.0 0.0 0.0 0.0 200 PC Parent WCB 617 0.0 0.0 0.0 0.0 0.0 0.0 0.0
(0-4) RILs 0.2 0.3 0.2 0.1 0.3 0.2 0.1 Minimum RILs 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Maximum RILs 3.8 4.0 4.0 4.0 4.0 4.0 4.0 Checks 0.0 0.0 0.0 0.0 0.0 0.0 0.0 LSD 0.05 0.5 0.8 0.8 0.3 0.6 0.6 0.5 All Experiment 9.5 9.4 9.4 - 9.5 9.9 - Parent WCB414 9.1 9.3 9.5 - 8.2 9.2 - SL Parent WCB 617 10.3 9.8 10.3 - 11.1 10.1 - (cm) RILs 9.6 9.4 9.4 - 9.5 9.9 - Minimum RILs 7.9 7.3 7.4 - 7.8 7.8 - Maximum RILs 11.6 11.6 11.6 - 11.8 12.6 - Checks 8.3 8.3 8.1 - 8.1 8.7 - LSD 0.05 0.7 1.0 0.9 - 0.9 0.9 (Continues)
Table B1. Mean values and RIL ranges of supernumerary-spikelet-related traits in each environment (continued) Environments Carrington Carrington Carrington Prosper Prosper Prosper Trait† Experiment Categories All 2009 2010 2011 2009 2010 2011 All Experiment 19.0 18.2 18.4 - 19.4 20.0 - Parent WCB414 16.1 16.4 16.7 - 15.2 16.2 - Nd Parent WCB 617 24.0 22.2 22.6 - 26.3 25.1 - (nodes spike-1) RILs 19.1 18.3 18.5 - 19.5 20.1 - Minimum RILs 15.2 14.4 14.9 - 14.8 14.5 - Maximum RILs 23.9 22.4 22.8 - 26.7 26.0 - Checks 16.4 16.0 16.4 - 16.1 17.3 - LSD 0.05 1.6 1.7 1.5 1.8 1.8 All Experiment 2.0 1.95 2.0 - 2.0 2.0 - Parent WCB414 1.8 1.8 1.7 - 1.8 1.8 - NdD Parent WCB 617 2.3 2.2 2.2 - 2.4 2.5 - (nodes cm-1) RILs 2.0 1.9 2.0 - 2.0 2.0 - Minimum RILs 1.5 1.4 1.6 - 1.5 1.6 -
201 Maximum RILs 2.5 2.6 2.4 - 2.6 2.7 -
Checks 2.0 1.9 2.0 - 2.0 2.0 - LSD 0.05 0.2 0.2 0.2 0.2 0.2 All Experiment 2.9 2.7 3.3 - 3.4 2.3 - Parent WCB414 3.2 3.4 2.5 - 4.0 3.1 - Parent WCB 617 1.9 2.0 1.8 - 1.8 1.8 - ALB RILs 2.9 2.7 3.3 - 3.4 2.3 - (cm) Minimum RILs 0.2 0.2 0.1 - 0.0 0.3 - Maximum RILs 4.3 4.6 6.0 - 5.1 3.8 - Checks 3.4 3.0 3.5 - 4.2 2.7 - LSD 0.05 0.7 1.0 1.1 1.2 0.9
Table B1. Mean values and RIL ranges of supernumerary-spikelet-related traits in each environment (continued) Environments Carrington Carrington Carrington Prosper Prosper Prosper Trait† Experiment Categories All 2009 2010 2011 2009 2010 2011 All Experiment 3.2 3.2 3.7 - 3.7 2.3 - Parent WCB414 3.5 3.7 2.5 - 4.8 2.6 - Parent WCB 617 2.0 1.8 2.4 - 1.8 1.8 - ALT RILs 3.2 3.2 3.7 - 3.6 2.3 - (cm) Minimum RILs 1.1 0.7 1.3 - 1.1 0.8 - Maximum RILs 4.8 5.0 6.3 - 5.9 4.2 - Checks 4.3 4.4 4.7 - 5.1 2.9 - LSD 0.05 0.8 1.0 1.2 1.2 1.0 All Experiment 6.3 6.0 6.6 - 6.2 6.2 - Parent WCB414 6.9 6.2 7.2 - 7.0 7.4 - Parent WCB 617 3.8 3.6 3.9 - 4.1 3.5 - ALM RILs 6.3 6.0 6.6 - 6.2 6.2 - (cm) Minimum RILs 0.6 0.4 0.2 - 0.4 0.6 -
202 Maximum RILs 8.1 8.3 9.4 - 8.2 9.3 -
Checks 6.5 6.1 6.6 - 6.5 6.8 - LSD 0.05 0.8 1.3 1.2 - 1.2 1.6 - All Experiment 4.1 4.0 4.5 - 4.4 3.6 - Parent WCB414 4.5 4.4 4.1 - 5.3 4.4 - ALTA Parent WCB 617 2.6 2.5 2.7 - 2.7 2.4 - (cm) RILs 4.1 4.0 4.5 - 4.4 3.6 - Minimum RILs 0.7 0.4 0.6 - 0.5 0.6 - Maximum RILs 5.3 5.2 6.8 - 6.2 4.8 - Checks 4.7 4.5 4.9 - 5.3 4.1 - LSD 0.05 0.55 0.72 0.89 - 0.87 0.78 - - non information collected †PP, penetrance of pubescences; PC, penetrance of clavate architecture; SL, spike length; Nd, number of nodes; NdD, node density; ALB, awns length at the bottom of spike; ALT, awns length at the top of spike; ALM, awns length at middle of the spike; ALTA, awns length total averaged.
Table B2. Mean values and RIL ranges of agronomic traits in each environment Environments Carrington Carrington Carrington Prosper Prosper Prosper Trait† Experiment Categories All 2009 2010 2011 2009 2010 2011 All Experiment 55.8 58.9 66.3 - 51.6 50.0 52.1 Parent WCB414 53.3 57.9 65.5 - 48.6 45.5 49.4 Parent WCB 617 57.3 53.9 66.1 - 58.8 50.6 56.7 DH RILs 55.9 59.0 66.4 - 51.7 50.2 52.2 (days) Minimum RILs 49.8 53.8 61.5 - 46.6 43.0 43.0 Maximum RILs 65.0 64.0 74.0 - 61.7 61.2 66.5 Checks 52.8 57.2 64.2 - 48.8 45.9 48.0 LSD 0.05 2.7 1.5 2.1 - 1.8 2.5 3.1 All Experiment 87.4 84.9 93.2 91.9 91.6 87.9 75.7 Parent WCB414 86.7 86.6 92.4 89.9 83.1 90.2 80.5 Parent WCB 617 73.5 81.3 77.3 76.3 70.7 72.4 64.1 PH RILs 87.6 85.2 93.6 92.4 92.0 88.2 75.9 (cm) Minimum RILs 60.2 60.4 69.9 69.5 62.0 62.3 53.2
203 Maximum RILs 103.6 111.8 110.9 108.7 115.5 111.8 97.6
Checks 81.8 79.4 86.1 83.4 86.4 82.5 72.8 LSD 0.05 6.1 11.0 7.8 7.9 12.5 9.9 11.0 All Experiment 89.0 - 99.6 93.7 87.8 82.2 81.6 Parent WCB414 86.6 - 98.9 93.4 82.4 81.6 76.7 Parent WCB 617 91.1 - 97.0 94.3 100.2 80.7 85.1 DM RILs 89.0 - 99.6 93.6 87.8 82.2 81.6 (Days) Minimum RILs 81.6 - 94.6 84.1 80.0 78.1 74.3 Maximum RILs 98.0 - 107.1 103.2 96.5 90.8 93.3 Checks 89.1 - 99.5 96.0 80.0 82.3 80.0 LSD 0.05 3.0 - 2.5 2.5 3.4 2.2 4.1 (Continues)
Table B2. Mean values and RIL ranges of agronomic traits in each environment (continued) Environments Carrington Carrington Carrington Prosper Prosper Prosper Trait† Experiment Categories All 2009 2010 2011 2009 2010 2011 All Experiment 254.9 199.2 255.6 264.5 280.9 276.7 - Parent WCB414 337.0 280.9 335.3 349.6 363.1 352.6 - Parent WCB 617 224.8 242.3 230.1 237.9 199.8 235.7 - NS RILs 250.7 195.5 251.3 260.1 277.0 271.5 - (spikes m-2) Minimum RILs 159.2 95.0 172.6 164.5 155.0 148.4 - Maximum RILs 345.3 294.3 343.4 410.3 440.3 430.5 - Checks 345.2 264.4 345.7 356.7 368.9 391.3 - LSD 0.05 40.6 70.2 70.7 72.8 84.5 78.0 - All Experiment 43.0 0.0 0.0 72.9 32.6 60.7 7.7 Parent WCB414 28.5 0.0 0.0 67.9 0.0 35.3 12.0 Parent WCB 617 36.5 0.0 0.0 46.6 6.0 90.3 0.0 Ld RILs 43.8 0.0 0.0 74.7 32.5 62.4 7.7 % Minimum RILs 6.2 0.0 0.0 13.67 0.0 6.0 0.0
204 Maximum RILs 72.8 0.0 0.0 102.16 98.7 101.8 55.0
Checks 26.0 0.0 0.0 35.80 41.6 19.2 7.4 LSD 0.05 31.0 0.0 0.0 22.75 38.3 33.1 19.4 All Experiment 2.5 2.6 2.7 - 2.4 2.3 - Parent WCB414 2.6 2.8 2.9 - 2.5 2.5 - KSk Parent WCB 617 1.2 1.0 1.8 - 1.0 0.8 - (Kernels RILs 2.5 2.6 2.7 - 2.4 2.3 - spikelet-1) Minimum RILs 1.3 1.4 1.4 - 0.9 1.0 - Maximum RILs 3.0 3.7 4.0 - 3.2 3.0 - Checks 2.6 2.8 2.7 - 2.4 2.4 - LSD 0.05 0.4 0.5 0.5 - 0.5 0.5 - (Continues)
Table B2. Mean values and RIL ranges of agronomic traits in each environment (continued) Environments Carrington Carrington Carrington Prosper Prosper Prosper Trait† Experiment Categories All 2009 2010 2011 2009 2010 2011 All Experiment 2.6 2.8 2.8 - 2.5 2.4 - Parent WCB414 2.7 2.8 2.9 - 2.5 2.5 - KNd Parent WCB 617 2.1 2.2 2.8 - 1.5 1.7 - (Kernels Node-1) RILs 2.6 2.8 2.8 - 2.5 2.4 - Minimum RILs 2.0 1.8 2.0 - 1.6 1.3 - Maximum RILs 3.6 4.9 4.0 - 4.2 3.4 - Checks 2.6 2.8 2.7 - 2.4 2.4 - LSD 0.05 0.4 0.5 0.6 - 0.6 0.6 - All Experiment 1.5 0.9 1.3 - 1.9 1.8 - Parent WCB414 1.1 0.5 1.0 - 1.2 1.7 - NNdISk Parent WCB 617 4.6 6.1 3.3 - 5.4 4.7 - (nodes RILs 1.5 0.9 1.3 - 1.9 1.8 - with Minimum RILs 0.1 0.0 0.0 - 0.2 0.0 -
205 Immature spike-1) Maximum RILs 4.9 4.2 4.8 - 6.1 5.8 -
Checks 1.3 0.8 1.2 - 1.7 1.6 - LSD 0.05 0.7 0.9 1.0 1.1 1.2 All Experiment 33.6 37.5 38.7 - 33.6 24.6 - Parent WCB414 30.8 34.2 34.7 - 30.3 23.9 - Parent WCB 617 25.9 27.5 42.1 - 14.2 14.7 - KS RILs 33.7 37.7 38.8 - 33.9 24.4 - (Kernels spike-1) Minimum RILs 25.1 23.2 23.8 - 21.4 10.3 - Maximum RILs 43.6 52.9 62.6 - 45.7 35.4 - Checks 32.0 34.2 35.4 - 29.3 29.0 - LSD 0.05 6.8 7.9 7.7 - 10.0 8.3 - (Continues)
Table B2. Mean values and RIL ranges of agronomic traits in each environment (continued) Environments Carrington Carrington Carrington Prosper Prosper Prosper Trait† Experiment Categories All 2009 2010 2011 2009 2010 2011 All Experiment 2688.8 3119.0 2952.1 - 2632.0 2051.9 - Parent WCB414 3320.4 3954.1 3116.5 - 2943.1 3279.1 - Parent WCB 617 1359.0 972.5 2384.1 - 545.2 1226.9 - GY RILs 2641.3 3080.1 2904.4 - 2596.5 1981.9 - (Kg ha-1) Minimum RILs 1561.8 1375.4 1317 - 1275.6 450.9 - Maximum RILs 4086.7 4951.5 4972.5 - 3787.7 4331.4 - Checks 3894.2 4195.7 4098.1 - 3696.1 3593.9 - LSD 0.05 648.0 592.7 566.2 - 854.0 514.5 - - non information collected † DH, days to heading; PH, plant height; DM, days to maturity; NS, number of spikes; Ld, lodging; KSk, kernels per spikelet; KNd, kernels per node; KS, kernel spike; NNdISk, number of nodes with immature spikelets at the spike base; GY, grain yield.
206
Table B3. Coefficient of variance (CV%), and lattice efficiecy (EF%) of spike-related and agronomic traits Carrington 2009 Carrington 2010 Carrington 2011 Prosper 2009 Prosper 2010 Prosper 2011 Trait† CV% EF% CV% EF% CV% EF% CV% EF% CV% EF% CV% EF% Spike-related Traits
PP 31.81 101.66 14.42 99.87 21.15 102.73 29.04 99.36 12.51 100.6 20.24 100.38 PC 151.41 98.95 275.91 99.15 107.1 100.21 111.25 101.49 181.78 99.36 339.22 99.59 SL 5.44 101.66 4.86 103.47 - - 4.88 102.69 4.38 131.57 - - Nd 4.47 109.36 4.09 102.37 - - 4.66 105.8 4.47 104.44 - - NdD 5.08 102.9 4.25 98.96 - - 5.26 101.1 4.89 109.32 - - ALB 17.38 109.27 15.92 104.29 - - 17.84 108.73 18.58 110.55 - - ALT 15.93 103.94 15.91 108.47 - - 16.91 101.08 21.82 109.12 - - ALM 11.06 100.17 9.47 99.18 - - 9.76 104.29 13 100.39 - - ALTA 9.22 99.47 9.78 104.08 - - 9.91 101.13 10.65 111.04 - - Agonomic Traits DH 1.25 110.03 1.6 102.09 - - 1.67 114.49 2.12 106.44 3 101.62 PH 6.48 103.38 4.12 109.35 4.22 117.6 6.76 107.09 5.73 99.51 7.05 136.38 DM nd nd 1.22 110.03 1.32 115.15 1.89 105.46 1.34 106.22 2.49 113.48
207 NS 17.76 101.25 13.53 117.86 13.74 103.78 15.01 103.36 13.9 107.91 - -
Ld - - - - 15.3 111.6 58.2 106.5 26.76 112.58 126.01 105.99 KSk 10 101.61 2.69 111.66 - - 10.93 101.33 11.07 103.5 - - KNd 9.52 103.81 9.67 119.74 - - 11.37 100.1 12.83 99.49 - - KS 10.78 99.73 9.83 106.59 - - 14.65 107.89 16.9 103.03 - - NNdISk 50.57 99.13 40.75 99.98 - - 28.85 104.55 35.07 99.05 - - GY 9.24 121.92 9.39 116.03 - - 15.82 117.96 12.3 111.38 - - - non information collected † PP, penetrance of pubescences; PC, penetrance of clavate architecture; SL, spike length; Nd, number of nodes; NdD, node density; ALB, awns length at the bottom of spike; ALT, awns length at the top of spike; ALM, awns length at middle of the spike; ALTA, awns length total averaged, DH, days to heading; PH, plant height; DM, days to maturity; NS, number of stems; Ld, lodging susceptibility; KSk, kernels per spikelet; KNd, Kernels per node; KS, kernel per spike; NNdISk, number of nodes with immature spikelets at the spike base; GY, grain yield.
Table B4. Error mean square (EMS) and Fmax ratio for spike-related and agronomic traits Carrington Carrington Carrington Prosper Prosper Prosper Fmax Trait† 2009 2010 2011 2009 2010 2011 EMS EMS EMS EMS EMS EMS ratioa Spike-related Traits PP 0.12 0.03 0.05 0.11 0.02 0.05 6.61 PC 0.17 0.17 0.02 0.08 0.1 0.06 9.61 SL 0.26 0.21 - 0.21 0.19 - 1.4 Nd 0.66 0.57 - 0.81 0.8 - 1.43 NdD 0.01 0.01 - 0.01 0.01 - 1.57 ALB 0.22 0.28 - 0.37 0.18 - 2.03 ALT 0.26 0.35 - 0.39 0.25 - 1.53 ALM 0.45 0.39 - 0.36 0.66 - 1.81 ALTA 0.14 0.2 - 0.19 0.15 - 1.47 Agonomic Traits 208 DH 0.54 1.13 - 0.74 1.53 2.44 4.54
PH 30.29 14.72 15.06 38.39 25.36 28.5 2.61 DM - 1.48 1.53 2.77 1.2 4.11 3.41 NS 1251.06 1195.96 1321.18 1778.24 1479.01 - 1.49 LD - - 125.25 359.63 263.43 93.05 3.86 KSk 0.07 0.06 - 0.07 0.06 - 1.11 KNd 0.07 0.08 - 0.08 0.09 - 1.32 KS 16.37 14.45 - 24.21 17.22 - 1.68 NNdISk 0.21 0.27 - 0.32 0.39 - 1.85 GY 83068 76883 - 173370 63723 - 2.72 - non information collected † PP, penetrance of pubescences; PC, penetrance of clavate architecture; SL, spike length; Nd, number of nodes; NdD, node density; ALB, awns length at the bottom of spike; ALT, awns length at the top of spike; ALM, awns length at middle of the spike; ALTA, awns length total averaged, NS, number of stems; PH, plant height; Ld, lodging susceptibility; DH, days to heading; DM, days to maturity; KSk, kernels per spikelet; KNd, Kernels per node; KS, kernel spike; NNdISk, number of nodes with immature spikelets at the spike base; GY, grain yield
0.99 WCB414 0.71 WCB414 0.41 WCB617
0.32
0.74 0.53 0.31 WCB414 0.24
0.49 0.35 0.21 WCB414
WCB617 WCB617 0.16
Relativefrequency
Relativefrequency Relativefrequency
0.25 0.18 0.10 Relativefrequency 0.08
-1 WCB617 0.00 0.00 0.00 0.00) 0.00 0.85 1.69 2.54 3.38 4.23 0.00 0.90 1.80 2.70 3.60 4.50 7.47 8.23 8.98 9.74 10.50 11.25 12.01 14.29 16.03 17.77 19.52 21.26 23.00 24.74 PC (0-4) PP (0-4) SL (cm) Nd (Nodes/spike)
WCB414 0.35 0.64 WCB414 0.46 0.47 WCB414
0.27 0.48 0.34 WCB414 0.35
0.18 0.32 0.23 0.23
WCB617
209 WCB617
Relativefrequency
Relativefrequency Relativefrequency Relativefrequency WCB617 0.12 0.09 0.16 0.11 WCB617
0.00 0.00 0.00 0.00 1.44 1.62 1.81 1.99 2.18 2.37 2.55 0.00 0.79 1.58 2.37 3.16 3.96 4.75 0.70 1.44 2.19 2.94 3.69 4.44 5.18 0.00 1.48 2.97 4.45 5.94 7.42 8.91 NdD (Nodes/cm) ALB (cm) ALT (cm) ALM (cm)
0.46 WCB414
0.34
0.23 Relativefrequency 0.11 WCB617
0.00 0.00 0.96 1.92 2.88 3.84 4.80 5.76 ALTA (cm)
Fig. B1. Frequency distribution of 163 RIL for mean of spike-related traits over four to six environments
0.39 WCB414 0.34 WCB414 0.41 WCB414 WCB617 0.50
0.29 WCB617 0.26 0.30 0.38
0.20 0.17 0.20 0.25 WCB617
WCB617
Relativefrequency
Relativefrequency Relativefrequency Relativefrequency 0.10 0.09 0.10 0.13
WCB414 0.00 0.00 0.00 0.00 48.27 51.31 54.36 57.41 60.46 63.51 66.55 55.81 64.50 73.19 81.88 90.57 99.27 107.95 81.75 84.69 87.62 90.55 93.48 96.41 99.35 138.4 173.4 208.5 243.5 278.5 313.5 348.5 DH (days) PH (cm) DM (days) NS (Spike/m^2)
0.46 0.35 WCB617 0.41 0.44 WCB414 WCB414 WCB414 0.34 0.27 0.30 0.33
0.23
0.18 WCB414 0.20 0.22 Relativefrequency
210 WCB617
Relativefrequency Relativefrequency Relative Relative frequency 0.11 0.09 0.10 0.11 WCB617
WCB617 0.00 0.00 0.00 0.00 -0.43 12.89 26.20 39.51 52.82 66.13 79.45 23.29 26.99 30.69 34.40 38.10 41.80 45.50 0.90 1.29 1.67 2.06 2.45 2.84 3.22 1.85 2.17 2.50 2.83 3.16 3.49 3.81 Ld (%) KS (Kernels/spike) KSk (Kernels/spikelet) KNd (Kernels/node)
0.43 0.55 WCB414
0.32 0.41
0.22 0.27 Relativefrequency Relativefrequency WCB414 0.11 0.14
WCB617 WCB617 0.00 0.00 1000.0 1556.5 2113.0 2669.6 3226.1 3782.6 4339.1 0.00 0.90 1.80 2.71 3.61 4.51 5.41 GY (Kg/Ha) NNdISk
Fig. B2. Frequency distribution of 163 RIL for mean of agronomic traits over four to six environments
APPENDIX C. SUPPLEMENTARY TABLES FOR CHAPTER 4
Table C1. Estimated means of eight quality traits in a RIL population, their parents and seven checks evaluated in four environments Environments Trait† Experiment Categories All Carrington 2009 Carrington 2010 Prosper 2009 Prosper 2010 All Experiment 30.1 32.4 32.8 29.1 26.1 Parent WCB414 32.9 34.4 34.1 33.3 30.0 Parent WCB 617 24.9 20.5 29.0 26.2 22.9 TKW RILs 30.1 32.4 32.8 29.1 26.0 (g) Minimum RILs 23.3 24.0 23.8 22.6 15.2 Maximum RILs 37.8 39.7 42.6 35.7 34.7 Checks 30.6 32.8 32.7 28.9 28.1 LSD 0.05 3.2 2.3 2.6 3.1 3.3 All Experiment 716.6 756.7 736.3 715.0 659.4
211 Parent WCB414 761.1 789.3 767.7 756.4 731.6 KVW Parent WCB 617 692.0 710.9 713.9 723.1 620.5
(kg m-3) All RILs 714.0 755.0 734.4 712.6 654.9 Minimum RIL 656.3 702.3 675.3 637.9 543.6 Maximum RIL 803.5 824.9 811.9 798.7 787.9 Checks 774.6 797.6 778.6 763.1 759.1 LSD 0.05 35.4 14.5 15.0 22.3 37.3 All Experiment 43.2 47.8 44.5 40.8 39.7 Parent WCB414 63.0 66.9 59.9 61.9 61.7 FE Parent WCB 617 26.0 38.7 20.7 - 22.2 % All RILs 42.4 47.1 43.8 40.0 38.8 Minimum RIL 15.5 18.4 11.2 7.3 11.2 Maximum RIL 62.6 64.5 68.5 61.6 68.2 Checks 60.0 62.2 61.3 58.3 59.0 LSD 0.05 8.5 5.2 8.6 4.8 9.3 (Continues)
Table C1. Estimated means of eight quality traits in a RIL population, their parents and seven checks evaluated in four environments (Continued) Environments Trait† Experiment Categories All Carrington 2009 Carrington 2010 Prosper 2009 Prosper 2010 All Experiment 15.8 15.2 15.4 15.7 17.0 Parent WCB414 15.1 14.6 15.5 14.2 16.0 GPC Parent WCB 617 16.5 16.8 15.1 16.8 17.5 % All RILs 15.9 15.1 15.4 15.8 17.1 Minimum RIL 14.0 12.4 12.9 13.6 13.8 Maximum RIL 17.7 17.2 18.2 17.6 20.2 Checks 15.5 15.7 15.7 15.1 15.7 LSD 0.05 1.1 0.7 0.8 0.9 1.3 All Experiment 4.4 4.4 4.3 4.8 4.1 Parent WCB414 4.8 5.0 4.8 4.5 5.0 MX Parent WCB 617 4.2 4.0 4.5 - 3.5
212 (1-8) All RILs 4.4 4.3 4.3 4.8 4.1
Minimum RIL 3.0 1.9 2.7 2.5 2.5 Maximum RIL 5.9 6.9 6.1 7.0 5.5 Checks 4.4 4.6 4.4 4.4 4.4 LSD 0.05 0.9 1.5 2.0 1.3 1.2 All Experiment 6.0 4.4 7.0 5.1 7.3 Parent WCB414 6.3 4.8 6.9 5.9 7.9 MEPT Parent WCB 617 3.5 2.6 5.0 - 3.6 min All RILs 6.0 4.5 7.1 5.1 7.2 Minimum RIL 1.9 1.6 1.6 1.2 2.2 Maximum RIL 14.0 11.9 20.0 11.3 18.3 Checks 6.2 4.0 6.2 6.0 8.7 LSD 0.05 2.6 2.8 3.3 3.0 3.3 (Continues)
Table C1. Estimated means of eight quality traits in a RIL population, their parents and seven checks evaluated in four environments (Continued) Environments Trait† Experiment Categories All Carrington 2009 Carrington 2010 Prosper 2009 Prosper 2010 All Experiment 6.4 5.0 7.3 5.6 7.5 Parent WCB414 6.4 4.9 7.1 6.0 7.8 MMLPT Parent WCB 617 3.9 2.4 5.6 - 3.9 min All RILs 6.4 5.0 7.4 5.5 7.5 Minimum RIL 2.2 1.9 1.8 1.7 2.3 Maximum RIL 15.3 13.7 20.1 11.9 18.4 Checks 6.7 4.6 6.6 6.6 9.1 LSD 0.05 2.6 2.9 3.4 3.1 3.5 All Experiment 249.0 201.0 268.0 250.6 273.5 Parent WCB414 271.5 201.8 289.1 277.7 318.6 MMLPI Parent WCB 617 176.9 158.6 206.5 - 176.4 %TQ*min. All RILs 248.5 201.3 268.8 248.4 271.7
213 Minimum RIL 99.7 84.3 82.8 101.3 99.0
Maximum RIL 569.7 612.5 658.2 567.1 549.4 Checks 267.7 198.1 256.0 296.9 321.5 LSD 0.05 70.8 75.1 84.1 88.6 93.7 - non-data collected. †TKW, Thousand kernel weight; KVW, grain-volume weight; GPC, grain protein content; FE,flour extraction; MX, mixogram score; MEPT, mixogram envelope peak time; MMLPT, mixogram mid line peak time; MMLPI, mixogram mid line peak integral.
Table C2. Coefficient of variance, lattice efficiency, error mean square, and FMax ratio for eight for quality traits Carrington 2009 Carrington 2010 Prosper 2009 Prosper 2010 Fmax Trait† CV%‡ EF%§ EMS¶ CV%‡ EF%§ EMS¶ CV%‡ EF%§ EMS¶ CV%‡ EF%§ EMS¶ ratio
TKW 0.8 116.5 40.4 1.0 120.6 53.1 1.4 106.7 98.5 2.8 120.5 329.9 8.2 GVW 2.3 118.0 0.1 2.4 126.0 0.1 2.6 112.8 0.2 3.7 111.6 0.4 3.3 GPC 5.4 107.7 6.6 9.4 158.6 17.4 6.0 - 6.0 11.3 146.2 20.2 3.4 FE 15.4 105.2 0.5 23.3 105.3 1.0 13.8 - 0.4 15.1 100.5 0.4 2.6 MX 32.0 99.3 2.0 23.1 106.2 2.6 30.0 - 2.4 22.9 99.9 2.8 1.4 MEPT 28.7 101.1 2.0 22.8 107.0 2.8 28.7 - 2.6 23.4 98.8 3.1 1.5 MMLPT 16.8 102.7 1146.5 15.8 101.8 1788.9 18.0 - 2045.5 17.7 98.6 2345.3 2.0 - non-data collected. †TKW, Thousand kernel weight; KVW, grain-volume weight; GPC, grain protein content; FE, flour extraction; MX, mixogram score; MEPT, mixogram envelope peak time; MMLPT, mixogram mid line peak time; MMLPI, mixogram mid line peak integral. ‡Coefficient of variance §Lattice efficiency ¶ Error mean square
214
Table C3. Significant Pearson's correlation coefficients among four spike-related traits and eight quality traits in wheat Quality Traits TKW KVW GPC FE MX MEPT MMLPT MMLPI Spikes-Related Traitsa
PSS -0.23**§ ns 0.31**§ ns 0.25**‡ 0.19**‡ 0.26**† 0.25**‡
PP -0.26**§ -0.22**‡ 0.36**† -0.18**§ ns 0.17*† 0.19*† 0.21**†
PC ns -0.19**‡ 0.26**† ns ns ns ns ns
Aless ns ns -0.16*† ns ns ns ns ns
215