Kernel hardness (KHA) is a major factor determining break flour yield (BFY) and end-use quality of common wheat (Triticum aestivum L.). Within the soft wheat class, genotypes with consistently softer grains than common soft wheat are considered to be 'extra-soft'. In addition, 'extra-soft' wheats have greater BFY than common soft wheat lines. In order to better understand this interrelationship, a set of 164 F6-recombinant inbred lines (RILs) developed from a soft x 'extra-soft' wheat cross was evaluated for KHA, BFY, and other related traits in six field environments. The estimates of broad-sense heritability for KHA and BFY ranged from 0.84 to 0.96 and 0.56 to 0.76, respectively. Significant environmental effects and genotype by environment interactions were detected for all traits evaluated. A comprehensive genetic linkage map was created with 650 molecular markers based on this mapping population. Three chromosome translocations, 1BL. 1RS, 2N^S-2AS. 2AL and 5B:7B, were identified during linkage analysis. A total of 47 quantitative trait loci (QTL) were identified for nine traits including KHA, BFY, bran yield (BRN), unground middling yield (MID), plant height (PHT), days to heading (HDD), thousand-kernel weight (TKW), grain protein content (GPC), and test weight (TWT). The number of QTL per trait ranged from three for MID to nine for GPC. The phenotypic variance explained by individual QTL ranged from 5.8 to 47.6%. Among five QTL identified for KHA, the two most important QTL were located on chromosomes 4DS (Xbarc1118-Rht-D1 interval) and 4BS (Xwmc617-Rht-B1 interval), indicating that the 'extra-soft' characteristic was not controlled by the 5DS Hardness (Ha) locus which encodes the two puroindoline genes pinA and pinB. The co-location of QTL for KHA, BFY, BRN, and MID on 4DS suggested that genetic factors affecting KHA may have a pleiotropic effect on BFY. Two co-located QTL for TWT, TKW and PHT were detected on 4DS and 4BS, and a QTL for HDD was detected on 4DS, indicating that these QTL may represent the consequence of the semi-dwarfing green-revolution genes Rht-D1 and Rht-B1 located on 4DS and 4BS, respectively. Additional analysis suggested that the QTL for KHA on 4DS and 4BS are the effects of genes linked to Rht-D1 and Rht-B1, rather than pleiotropic effects of these genes. Some coincident QTL for the traits that were evaluated represent the interrelationships of phenotypic traits, where both KHA and BFY were associated with HDD and TWT based on path coefficient analysis. Association mapping can be an effective means for identifying, validating, and fine mapping genes and QTL in crop plants. To test this approach, a set of 94 diverse elite wheat lines was phenotyped for five important traits and genotyped with 487 molecular markers. In this study, the marker-trait association analysis showed that the gene pinB (Ha locus) was significantly associated with KHA as it is known to define the difference between soft and hard wheat classes. Additionally, the significant associations of marker XwPt-7187 with KHA, XwPt-1250 and XwPt-4628 with TWT, and Xgwm512 with PHT mark the first report of such associations in these genomic regions. This study, aiming at the genetic dissection of wheat kernel extra-softness and related traits, enhanced our understanding of both genetic control of and environmental effects on these important traits. Path coefficient analysis showed the promise of an alternative phenotypic selection approach that is more cost effective than direct measurement of kernel quality. Three chromosome translocations were discovered and their approximate chromosome break points were located. Numerous QTL were identified for these important traits. The major QTL can serve as a start point for fine mapping that eventually lead to the cloning of the QTL through map-based or candidate gene approach. Association mapping, as an alternate approach and complementary tool to QTL mapping, was demonstrated feasible in wheat for identification of marker-trait associations and cross validation of QTL or genes identified from bi-parent mapping populations.

Wheat (Triticum aestivum L.) is used in diverse baked products that require specific end use quality traits. Kernel texture, flour water absorption capacity, gluten strength, starch composition, and other flour constituents all influence overall flour functionality and dough rheology, specifying both wheat market class and intended end product. Wheat breeders need to develop cultivars with superior end-use quality traits, while also optimizing important agronomic traits. Our first objective was to use a genetic linkage map and 207 recombinant inbred lines (RIL) from a soft white 'Coda' by 'Brundage' cross to identify quantitative trait loci (QTL) for grain, milling, and baking traits. The linkage map was developed using 570 single nucleotide polymorphisms (SNP) and 136 simple sequence repeat markers. The RILs were grown in five locations in Idaho and Washington from 2006 to 2013. We detected three QTL on chromosomes 2D, 4B, and 6B that were consistently associated with multiple end-use quality traits. Our second objective was to use a genetic linkage map and 131 RILs from a soft white 'Louise' by 'Alpowa' cross to identify QTL associated with arabinoxylan content and milling traits. The linkage map consisted of 924 SNPs and 41 linkage groups. This population was grown in three Washington locations from 2011 to 2012. We detected 28 QTL associated with seven arabinoxylan content and milling traits. Our third objective was to use 480 advanced breeding lines and Pacific Northwest cultivars to identify molecular markers associated with 21 end-use quality traits. Genotypic data from the iSelect 90K SNP chip was combined with best linear unbiased predictions of historic phenotypic data from the USDA-ARS Western Wheat Quality Laboratory. Genome-wide association mapping in the R package, genome association and prediction integrated tool (GAPIT), detected significant markers for multiple end-use quality traits on chromosomes1B, 1D, 2D, 5A, 5B, and 7A. An improved understanding of the genetic architecture underlying end-use quality traits in wheat may assist breeders with cultivar development for superior end-use quality, particularly by increasing frequencies of favorable alleles in breeding populations. Cultivars with superior end-use quality will allow US wheat producers to maintain domestic and international markets.

In Quantitative Trait Loci: Methods and Protocols, a panel of highly experienced statistical geneticists demonstrate in a step-by-step fashion how to successfully analyze quantitative trait data using a variety of methods and software for the detection and fine mapping of quantitative trait loci (QTL). Writing for the nonmathematician, these experts guide the investigator from the design stage of a project onwards, providing detailed explanations of how best to proceed with each specific analysis, to find and use appropriate software, and to interpret results. Worked examples, citations to key papers, and variations in method ease the way to understanding and successful studies. Among the cutting-edge techniques presented are QTDT methods, variance components methods, and the Markov Chain Monte Carlo method for joint linkage and segregation analysis.

Increasing grain yield in wheat (Triticum aestivum L.) is a challenging task, because yield is a complex trait controlled by many genes and highly influenced by environmental factors. The genetic control of yield components and other traits associated with yield may be less complex and thus more manageable for breeding. This study seeks to identify quantitative trait loci (QTLs) for these traits. Two new genetic linkage maps were constructed from recombinant inbred lines (RILs) derived from crosses between the Oregon soft white winter wheat variety Tubbs and a Western European hard red winter wheat variety, Einstein. A third linkage map was constructed from RILs from a cross with Tubbs and a Western European experimental hard red winter wheat line. A combination of Diversity Arrays Technology (DArT), Simple Sequence Repeat (SSR), orw5, and B1 markers were used to construct genetic linkage maps. Two replications of the RIL populations were grown in yield trial sized plots at Corvallis, OR and Pendleton, OR in 2009. The RILs were evaluated for grain yield, spikes per m2, fertile spikelets per spike, sterile spikelets per spike, seeds per spike, seeds per fertile spikelet, average seed weight, growing degree days (GDD) to flowering, GDD to physiological maturity, GDD of grain fill, plant height, test weight, and percent grain protein. Composite interval mapping (CIM) detected 146 QTLs for these traits spread across all chromosomes except for 6D. Thirty six percent of all of the QTLs detected were in close proximity to four loci: Rht-B1, Rht-D1, B1, and Xgwm372. The use of factor analysis to aid in QTL mapping for correlated traits related to spike morphology was explored. Quantitative trait loci mapping of factor scores for these traits potentially showed an increase in statistical power to detect QTLs and a decrease in the probability of type I error over mapping the traits individually.

Quantitative Trait Loci (QTL) is a topic of major agricultural significance for efficient livestock production. This book covers various statistical methods that have been used or proposed for detection and analysis of QTL and marker-and gene-assisted selection in animal genetics and breeding.

Two bread wheat (T. aestivum L.) accessions were selected as parental lines. Population genotyping was conducted on 143 F2 plants and phenotyping was carried out on 133 F2:3 families. The molecular genetic linkage map was constructed including 293 loci associated to 19 wheat chromosomes. There are 76 new loci compared to the ITMI map. The analysis revealed eight QTLs for days to flowering and seven QTLs for plant height. Five QTLs for spike length were identified. The QTL for seed length on chromosome 5B was mapped for all trait measurements under both conditions. The present study revealed four and six QTLs for thousand-grain weight under control and stress conditions, respectively. Only one QTL on chromosome 4BL was common for both conditions. Five QTLs for thousand-grain weight were found to be specific to stress condition on chromosomes 1B, 4AL, 7AS, and 7DS. Identifying QTLs for thousand-grain weight under post-anthesis drought stress on chromosomes 7DS, 7AS, and 4AL and considering the known reciprocal translocation of 4AL/7BS in wheat, revealed the importance of the chromosomes from the homoeologous group 7 of Triticeae.