Genomics and high throughput phenomics have the potential to revolutionize the field of wheat (Triticum aestivum L.) breeding. Genomic selection (GS) has been used for predicting various quantitative traits in wheat, especially grain yield. However, there are few GS studies for grain protein content (GPC), which is a crucial quality determinant. Incorporation of secondary correlated traits in GS models has been demonstrated to improve accuracy. The objectives of this research were to compare performance of single and multi-trait GS models for predicting GPC and grain yield in wheat and to identify optimal growth stages for collecting secondary traits. We used 650 recombinant inbred lines from a spring wheat nested association mapping (NAM) population. The population was phenotyped over 3 years (2014–2016), and spectral information was collected at heading and grain filling stages. The ability to predict GPC and grain yield was assessed using secondary traits, univariate, covariate, and multivariate GS models for within and across cycle predictions. Our results indicate that GS accuracy increased by an average of 12% for GPC and 20% for grain yield by including secondary traits in the models. Spectral information collected at heading was superior for predicting GPC, whereas grain yield was more accurately predicted during the grain filling stage. Green normalized difference vegetation index had the largest effect on the prediction of GPC either used individually or with multiple indices in the GS models. An increased prediction ability for GPC and grain yield with the inclusion of secondary traits demonstrates the potential to improve the genetic gain per unit time and cost in wheat breeding.

Grain protein content (GPC) is controlled by complex genetic systems and their interactions and is an important quality determinant for hard spring wheat as it has a positive effect on bread and pasta quality. GPC is variable among genotypes and strongly influenced by the environment. Thus, understanding the genetic control of wheat GPC and identifying genotypes with improved stability is an important breeding goal. The objectives of this research were to identify genetic backgrounds with less variation for GPC across environments and identify quantitative trait loci (QTLs) controlling the stability of GPC. A spring wheat nested association mapping (NAM) population of 650 recombinant inbred lines (RIL) derived from 26 diverse founder parents crossed to one common parent, ‘Berkut’, was phenotyped over three years of field trials (2014–2016). Genomic selection models were developed and compared based on predictions of GPC and GPC stability. After observing variable genetic control of GPC within the NAM population, seven RIL families displaying reduced marker-by-environment interaction were selected based on a stability index derived from a Finlay–Wilkinson regression. A genome-wide association study identified eighteen significant QTLs for GPC stability with a Bonferroni-adjusted p-value < 0.05 using four different models and out of these eighteen QTLs eight were identified by two or more GWAS models simultaneously. This study also demonstrated that genome-wide prediction of GPC with ridge regression best linear unbiased estimates reached up to r = 0.69. Genomic selection can be used to apply selection pressure for GPC and improve genetic gain for GPC.

Eyespot, caused by the soil-borne necrotrophic fungi Oculimacula yallundae and O. acuformis, is a disease of major economic significance for wheat, barley and rye. Pacific Northwest (PNW) winter wheat (Triticum aestivum L.) grown in areas of high rainfall and moderate winters is most vulnerable to infection. The objective of this research was to identify novel genomic regions associated with eyespot resistance in winter wheat adapted to the PNW. Two winter wheat panels of 469 and 399 lines were compiled for one of the first genome-wide association studies (GWAS) of eyespot resistance in US winter wheat germplasm. These panels were genotyped with the Infinium 9K and 90K iSelect SNP arrays. Both panels were phenotyped for disease resistance in a two-year field study and in replicated growth chamber trials. Growth chamber trials were used to evaluate the genetic resistance of O. acuformis and O. yallundae species separately. Best linear unbiased predictors (BLUPs) were calculated across all field and growth chamber environments. A total of 73 marker-trait associations (MTAs) were detected on nine different chromosomes (1A, 2A, 2B, 4A, 5A, 5B, 7A, 7B and 7D) that were significantly associated (p-value <0.001) with eyespot resistance in Panel A, and 19 MTAs on nine different chromosomes (1A, 1B, 2A, 2D, 3B, 5A, 5B, 7A, and 7B) in Panel B. The most significant SNPs were associated with Pch1 and Pch2 resistance genes on the long arms of chromosome 7D and 7A. Most of the novel MTAs appeared to have a minor effect on reducing eyespot disease. Nevertheless, eyespot disease scores decreased as the number of resistance alleles increased. Seven SNP markers, significantly associated with reducing eyespot disease across environments and in the absence and presence of Pch1 were identified. These markers were located on chromosomes 2A (IWB8331), 5A (IWB73709), 5B (IWB47298), 7AS (IWB47160), 7B (IWB45005) and two SNPs (Ex_c44379_2509 and IAAV4340) had unknown map positions. The additive effect of the MTAs explained most of the remaining phenotypic variation not accounted for by Pch1 or Pch2. This study provides breeders with adapted germplasm and novel sources of eyespot resistance to be used in the development of superior cultivars with increased eyespot resistance.

End‐use quality and yield potential are the most important traits for hard white winter wheat (Triticum aestivum L.) cultivars produced in the Pacific Northwest region of the United States. The objective for the development of ‘Earl’ (Reg. No. CV‐1127, PI 680639) was to provide a hard white winter cultivar that meets end‐use quality expectations and performs well agronomically. Earl was developed and released in September 2014 by the Agricultural Research Center of Washington State University. It was tested under the experimental designations LasWA8061‐75, and WA8184, which were assigned through progressive generations of advancement. Earl is a semidwarf cultivar adapted for the intermediate‐ to high‐rainfall (>400 mm of average annual precipitation), unirrigated wheat production regions of Washington. Earl has high‐temperature, adult‐plant resistance to stripe rust, above‐average grain protein concentration, and high grain volume weight with end‐use quality properties similar to those of hard red winter wheat cultivars ‘Bauermeister’, ‘Finley’, and ‘Whetstone’.