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.

Stem rust, caused by the fungus Puccinia graminis Pers. f. sp. tritici Ericks, is one of the most damaging diseases of wheat (Triticum aestivum L.). The recent emergence of the stem rust Ug99 race group poses a serious threat to world wheat production. Utilization of genetic resistance in cultivar development is the optimal way to control stem rust. Here, we report association mapping of stem rust resistance in a global spring wheat germplasm collection (2152 accessions) genotyped with the wheat iSelect 9K single‐nucleotide polymorphism array. Using a unified mixed model method (or QK method), we identified a total of 47 loci that were significantly associated with various stem rust resistance traits including field disease resistance and seedling resistance against multiple stem rust pathogen races including BCCBC, TRTTF, TTKSK (Ug99), and TTTTF. The 47 loci could be further condensed into 11 quantitative trait locus (QTL) regions according to linkage disequilibrium information among adjacent markers. We postulate that these QTLs represent known stem rust resistance genes including Sr2, Sr6, Sr7a, Sr8a, Sr9h, Sr13, Sr28, and Sr36. We further employed a multilocus mixed model to explore marker‐trait associations and identified two additional QTLs (one potentially represents Sr31) that were significantly associated with stem rust resistance against various races. Combinations of the most significant loci for each trait explained up to 38.6% of the phenotypic variance. Markers identified through this study could be used to track the genes or QTLs. Accessions with high numbers of resistance‐associated alleles may serve as important breeding materials for stem rust resistance.

Powdery mildew is among the most common diseases of both hemp- and marijuana-type cultivated Cannabis sativa. Despite its prevalence, no documented studies have characterized sources of natural genetic resistance in this pathosystem. Here we provide evidence for the first resistance (R) gene in C. sativa, represented by a single dominant locus that confers complete resistance to an isolate of the powdery mildew pathogen Golovinomyces ambrosiae, found in the Pacific Northwest of the United States. Linkage mapping with nearly 10,000 single nucleotide polymorphism (SNP) markers revealed that this R gene (designated PM1) is located on the distal end of the long arm of one of the largest chromosomes in the C. sativa genome. According to reference whole genome sequences and Sanger sequencing, the marker was tentatively placed in a cluster of R genes of the nucleotide-binding site (NBS) and leucine-rich repeat (LRR) protein type. PM1's dominant behavior, qualitative penetrance, and a co-segregating qPCR marker to track its inheritance were confirmed in two separate genetic backgrounds totaling 185 recombinant F1 plants. The goal of this study is to provide a foundation for the discovery and characterization of additional sources of genetic resistance to pathogens that infect C. sativa.

Genome-wide association mapping is a powerful tool for dissecting the relationship between phenotypes and genetic variants in diverse populations. With the improved cost efficiency of high-throughput genotyping platforms, association mapping is a desirable method of mining populations for favorable alleles that hold value for crop improvement. Stem rust, caused by the fungus f. sp. is a devastating disease that threatens wheat ( L.) production worldwide. Here, we explored the genetic basis of stem rust resistance in a global collection of 1411 hexaploid winter wheat accessions genotyped with 5390 single nucleotide polymorphism markers. To facilitate the development of resistant varieties, we characterized marker-trait associations underlying field resistance to North American races and seedling resistance to the races TTKSK (Ug99), TRTTF, TTTTF, and BCCBC. After evaluating several commonly used linear models, a multi-locus mixed model provided the maximum statistical power and improved the identification of loci with direct breeding application. Ten high-confidence resistance loci were identified, including SNP markers linked to and and at least three newly discovered resistance loci that are strong candidates for introgression into modern cultivars. In the present study, we assessed the power of multi-locus association mapping while providing an in-depth analysis for its practical ability to assist breeders with the introgression of rare alleles into elite varieties.