Based on the estimates of accuracy, genomic selection would be useful for selecting for improved trait values and trait stability for agronomic and quality traits in wheat. Trait values and trait stability estimated by two methods were generally independent indicating a breeder could select for both simultaneously. Genomic selection (GS) is a new marker-assisted selection tool for breeders to achieve higher genetic gain faster and cheaper. Breeders face challenges posed by genotype by environment interaction (GEI) pattern and selecting for trait stability. Obtaining trait stability is costly, as it requires data from multiple environments. There are few studies that evaluate the efficacy of GS for predicting trait stability. A soft winter wheat population of 273 lines was genotyped with 90 K single nucleotide polymorphism markers and phenotyped for four agronomic and seven quality traits. Additive main effect and multiplicative interaction (AMMI) model and  Eberhart and Russell regression (ERR) were used to estimate trait stability. Significant GEI variation was observed and stable lines were identified for all traits in this study. The accuracy of GS ranged from 0.33 to 0.67 for most traits and trait stability. Accuracy of trait stability was greater than trait itself for yield (0.44 using AMMI versus 0.33) and heading date (0.65 using ERR versus 0.56). The opposite trend was observed for the other traits. GS did not predict the stability of the quality traits except for flour protein, lactic acid and softness equivalent. Significant GS accuracy for some trait stability indicated that stability was under genetic control for these traits. The magnitude of GS accuracies for all the traits and most of the trait stability index suggests the possibility of rapid selection for these trait and trait stability in wheat breeding.

Genotyping‐by‐sequencing (GBS), reduces genotyping costs and allows breeders to genotype large populations with thousands of markers. Genome‐wide association studies with GBS markers can be used to identify quantitative trait loci (QTL) for important traits in elite populations of soft red winter wheat (SRWW; Triticum aestivum L.). Our objective was to identify potential QTL for grain yield (GY), Fusarium head blight resistance (FHB), flour yield (FY), and softness equivalence (SE) in a set of 470 elite SWRR wheat lines that were genotyped with 33,169 GBS markers and phenotyped in multiple environments. For all traits, we found lines that were phenotypically superior to the elite checks. We identified four FHB QTL, nine QTL for quality, and 14 QTL for with R2 values ranging from 1.6 to 3.5%. The QTL with the largest effect for FHB resistance reduced disease by 1.76%. For quality, the largest‐effect QTL increased FY and SE by 0.37 and 0.67%, respectively. For GY the QTL with the largest effect in Wooster, OH, increased GY by 129.6 kg ha−1, for northwest Ohio, the largest‐effect QTL increased GY by 67.2 kg ha−1, and the largest‐effect QTL for GY over all environments increased GY by 48.8 kg ha−1. While marker‐assisted selection (MAS) for these QTL could be used to improve these traits, the preponderance of genetic variation appeared to be controlled by genes with small effect, suggesting that MAS should be used as a supplement to genomic selection.

Genomic selection (GS) is a breeding tool that estimates breeding values (GEBVs) of individuals based solely on marker data by using a model built using phenotypic and marker data from a training population (TP). The effectiveness of GS increases as the correlation of GEBVs and phenotypes (accuracy) increases. Using phenotypic and genotypic data from a TP of 470 soft winter wheat lines, we assessed the accuracy of GS for grain yield, Fusarium Head Blight (FHB) resistance, softness equivalence (SE), and flour yield (FY). Four TP data sampling schemes were tested: (1) use all TP data, (2) use subsets of TP lines with low genotype-by-environment interaction, (3) use subsets of markers significantly associated with quantitative trait loci (QTL), and (4) a combination of 2 and 3. We also correlated the phenotypes of relatives of the TP to their GEBVs calculated from TP data. The GS accuracy within the TP using all TP data ranged from 0.35 (FHB) to 0.62 (FY). On average, the accuracy of GS from using subsets of data increased by 54% relative to using all TP data. Using subsets of markers selected for significant association with the target trait had the greatest impact on GS accuracy. Between-environment prediction accuracy was also increased by using data subsets. The accuracy of GS when predicting the phenotypes of TP relatives ranged from 0.00 to 0.85. These results suggest that GS could be useful for these traits and GS accuracy can be greatly improved by using subsets of TP data.

Understanding genetic diversity within a breeding population is fundamental to its efficient exploitation. The advent of new high‐throughput marker systems offers the opportunity to expand the scope and depth of our investigation of diversity. Our objective was to analyze the genetic diversity of two populations of soft winter wheat (SW) adapted to the eastern United States. The historical population (HP) consisted of 187 lines released or developed from 1808 to 2005. The elite population (EP) consisted of 449 elite modern lines from the Ohio State University breeding program. The HP was genotyped with single nucleotide polymorphism (SNP) and diversity array technology (DArT) markers, while the EP was genotyped with DArTs. Population structure comprised of up to five subgroups was observed on the HP using either SNP or DArT markers and up to six subgroups on the EP. The subgroups could be partly explained by year of release (in HP), class (red versus white wheat) and pedigree (in the EP). Diversity appeared to increase with time; an estimated 11% of the genome exhibited a pronounced linear change over time, while evidence of more subtle changes abound. The DArT markers were associated with greater genome changes than SNP markers. Linkage disequilibrium (LD) that produced an r2 of 0.2 or greater extended to about 5 cM in both populations. The extent of LD decay varied widely across the genome. In conclusion, SW in the eastern United States has a moderate level of structure, appears quite diverse, and diversity may be increasing.