Identification of quantitative trait loci for Fusarium head blight (FHB) resistance from different sources and pyramiding them into cultivars could provide effective protection against FHB. The objective of this study was to characterize a soft red winter wheat (SRWW) breeding population that has been subjected to intense germplasm introduction and alien introgression for FHB resistance in the past. The population was evaluated under misted FHB nurseries inoculated with Fusarium graminearum infested corn spawn for two years. Phenotypic data included disease incidence (INC), disease severity (SEV), Fusarium damaged kernels (FDK), FHB index (FHBdx), and deoxynivalenol concentration (DON). Genome-wide association studies by using 13,784 SNP markers identified twenty-five genomic regions at -logP ≥ 4.0 that were associated with five FHB-related traits. Of these 25, the marker trait associations that explained more than 5% phenotypic variation were localized on chromosomes 1A, 2B, 3B, 5A, 7A, 7B, and 7D, and from diverse sources including adapted SRWW lines such as Truman and Bess, and unadapted common wheat lines such as Ning7840 and Fundulea 201R. Furthermore, individuals with favorable alleles at the four loci Fhb1, Qfhb.nc-2B.1 (Q2B.1), Q7D.1, and Q7D.2 showed better FDK and DON scores (but not INC, SEV, and FHBdx) compared to other allelic combinations. Our data also showed while pyramiding multiple loci provides protection against FHB disease, it has significant trade-off with grain yield.

Multi‐trait genomic prediction (MTGP) can improve selection accuracy for economically valuable ‘primary’ traits by incorporating data on correlated secondary traits. Resistance to Fusarium head blight (FHB), a fungal disease of wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.), is evaluated using four genetically correlated traits: incidence (INC), severity (SEV), Fusarium damaged kernels (FDK), and deoxynivalenol content (DON). Both FDK and DON are primary traits; DON evaluation is expensive and usually requires several months for wheat breeders to get results from service laboratories performing the evaluations. We evaluated MTGP for DON using three soft red winter wheat breeding datasets: two diversity panels from the University of Illinois (IL) and Purdue University (PU) and a dataset consisting of 2019–2020 University of Illinois breeding cohorts. For DON, relative to single‐trait (ST) genomic prediction, MTGP including phenotypic data for secondary traits on both validation and training sets, resulted in 23.4 and 10.6% higher predictive abilities in IL and PU panels, respectively. The MTGP models were advantageous only when secondary traits were included in both training and validation sets. In addition, MTGP models were more accurate than ST models only when FDK was included, and once FDK was included in the model, adding additional traits hardly improved accuracy. Evaluation of MTGP models across testing cohorts indicated that MTGP could increase accuracy by more than twofold in the early stages. Overall, we show that MTGP can increase selection accuracy for resistance to DON accumulation in wheat provided FDK is evaluated on the selection candidates.

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Although grain yield is the most important trait for growers, milling and baking industries demand high‐quality and scab‐free grains for various end products. To accelerate breeding of wheat (Triticum aestivum L.) cultivars with high grain quality, genetic dissection of quality traits is necessary. Genome‐wide association studies (GWAS) were conducted to identify genomic regions responsible for milling and baking quality traits in soft red winter wheat (SRWW). Seven quality traits were evaluated in two locations and 2 yr for 270 elite SRWW lines. These traits include flour yield, softness equivalent, flour protein, and four solvent (lactose, sodium carbonate, sucrose, and water) retention capacity measurements. In this study, 27,449 single nucleotide polymorphism (SNP) markers were developed by using both genotyping‐by‐sequencing and 90K SNP array technologies. A linear mixed model in GWAS, accounting for population structure and kinship, was fitted to identify 18 [−log(P) ≥ 4.0] genomic regions on 12 different chromosomes associated with the quality traits. Only one of these associations seems to be a previously identified quantitative trait locus, whereas others are novel associations. The most significant associations for quality traits were identified on chromosomes 1B, 2A, 2B, 4B, 5A, 7A, and 7D. Candidate gene searches, facilitated by the wheat genome assembly, led us to the identification of putative genes related to SRWW quality traits including fasciclin‐like arabinogalactan. The results of this study can be used in developing high‐quality SRWW varieties for the eastern region of the United States.