@article{dewitt_lyerly_guedira_holland_murphy_ward_boyles_mergoum_babar_shakiba_et al._2023, title={Bearded or smooth? Awns improve yield when wheat experiences heat stress during grain fill in the southeastern United States}, volume={74}, ISSN={["1460-2431"]}, url={https://doi.org/10.1093/jxb/erad318}, DOI={10.1093/jxb/erad318}, abstractNote={Abstract The presence or absence of awns—whether wheat heads are ‘bearded’ or ‘smooth’ – is the most visible phenotype distinguishing wheat cultivars. Previous studies suggest that awns may improve yields in heat or water-stressed environments, but the exact contribution of awns to yield differences remains unclear. Here we leverage historical phenotypic, genotypic, and climate data for wheat (Triticum aestivum) to estimate the yield effects of awns under different environmental conditions over a 12-year period in the southeastern USA. Lines were classified as awned or awnless based on sequence data, and observed heading dates were used to associate grain fill periods of each line in each environment with climatic data and grain yield. In most environments, awn suppression was associated with higher yields, but awns were associated with better performance in heat-stressed environments more common at southern locations. Wheat breeders in environments where awns are only beneficial in some years may consider selection for awned lines to reduce year-to-year yield variability, and with an eye towards future climates.}, number={21}, journal={JOURNAL OF EXPERIMENTAL BOTANY}, author={DeWitt, Noah and Lyerly, Jeanette and Guedira, Mohammed and Holland, James B. and Murphy, J. Paul and Ward, Brian P. and Boyles, Richard E. and Mergoum, Mohamed and Babar, Md Ali and Shakiba, Ehsan and et al.}, editor={Dreisigacker, SusanneEditor}, year={2023}, month={Nov}, pages={6749–6759} }
 @article{winn_lyerly_ward_brown-guedira_boyles_mergoum_johnson_harrison_babar_mason_et al._2022, title={Profiling of Fusarium head blight resistance QTL haplotypes through molecular markers, genotyping-by-sequencing, and machine learning}, volume={7}, ISSN={["1432-2242"]}, DOI={10.1007/s00122-022-04178-w}, journal={THEORETICAL AND APPLIED GENETICS}, author={Winn, Zachary J. and Lyerly, Jeanette and Ward, Brian and Brown-Guedira, Gina and Boyles, Richard E. and Mergoum, Mohamed and Johnson, Jerry and Harrison, Stephen and Babar, Ali and Mason, Richard E. and et al.}, year={2022}, month={Jul} }
 @article{ward_merrill_bulli_pumphrey_mason_mergoum_johnson_sapkota_lopez_marshall_et al._2021, title={Analysis of the primary sources of quantitative adult plant resistance to stripe rust in US soft red winter wheat germplasm}, volume={14}, ISSN={["1940-3372"]}, DOI={10.1002/tpg2.20082}, abstractNote={Stripe rust, or yellow rust (Puccinia striiformis Westend. f. sp. tritic), is a disease of wheat (Triticum aestivum L.) historically causing significant economic losses in cooler growing regions. Novel isolates of stripe rust with increased tolerance for high temperatures were detected in the United States circa 2000. This increased heat tolerance puts geographic regions, such as the soft red winter wheat (SRWW) growing region of the southeastern United States, at greater risk of stripe rust induced losses. In order to identify sources of stripe rust resistance in contemporary germplasm, we conducted genome‐wide association (GWA) studies on stripe rust severity measured in two panels. The first consisted of 273 older varieties, landraces, and some modern elite breeding lines and was evaluated in environments in the U.S. Pacific Northwest and the southeastern United States. The second panel consisted of 588 modern, elite SRWW breeding lines and was evaluated in four environments in Arkansas and Georgia. The analyses identified three major resistance loci on chromosomes: 2AS (presumably the 2NS:2AS alien introgression from Aegilops ventricosa Tausch; syn. Ae. caudata L.), 3BS, and 4BL. The 4BL locus explained a greater portion of variance in resistance than either the 2AS or 3BS loci in southeastern environments. However, its effects were unstable across different environments and sets of germplasm, possibly a result of its involvement in epistatic interactions. Relatively few lines carry resistance alleles at all three loci, suggesting that there is a pre‐existing reservoir of enhanced stripe rust resistance that may be further exploited by regional breeding programs.}, number={1}, journal={PLANT GENOME}, author={Ward, Brian P. and Merrill, Keith and Bulli, Peter and Pumphrey, Mike and Mason, Richard Esten and Mergoum, Mohamed and Johnson, Jerry and Sapkota, Suraj and Lopez, Benjamin and Marshall, David and et al.}, year={2021}, month={Mar} }
 @article{larkin_mason_moon_holder_ward_brown-guedira_2021, title={Predicting Fusarium Head Blight Resistance for Advanced Trials in a Soft Red Winter Wheat Breeding Program With Genomic Selection}, volume={12}, ISSN={["1664-462X"]}, DOI={10.3389/fpls.2021.715314}, abstractNote={Many studies have evaluated the effectiveness of genomic selection (GS) using cross-validation within training populations; however, few have looked at its performance for forward prediction within a breeding program. The objectives for this study were to compare the performance of naïve GS (NGS) models without covariates and multi-trait GS (MTGS) models by predicting two years of F4:7 advanced breeding lines for three Fusarium head blight (FHB) resistance traits, deoxynivalenol (DON) accumulation, Fusarium damaged kernels (FDK), and severity (SEV) in soft red winter wheat and comparing predictions with phenotypic performance over two years of selection based on selection accuracy and response to selection. On average, for DON, the NGS model correctly selected 69.2% of elite genotypes, while the MTGS model correctly selected 70.1% of elite genotypes compared with 33.0% based on phenotypic selection from the advanced generation. During the 2018 breeding cycle, GS models had the greatest response to selection for DON, FDK, and SEV compared with phenotypic selection. The MTGS model performed better than NGS during the 2019 breeding cycle for all three traits, whereas NGS outperformed MTGS during the 2018 breeding cycle for all traits except for SEV. Overall, GS models were comparable, if not better than phenotypic selection for FHB resistance traits. This is particularly helpful when adverse environmental conditions prohibit accurate phenotyping. This study also shows that MTGS models can be effective for forward prediction when there are strong correlations between traits of interest and covariates in both training and validation populations.}, journal={FRONTIERS IN PLANT SCIENCE}, author={Larkin, Dylan L. and Mason, Richard Esten and Moon, David E. and Holder, Amanda L. and Ward, Brian P. and Brown-Guedira, Gina}, year={2021}, month={Oct} }
 @article{cowger_ward_brown-guedira_brown_2020, title={Role of Effector-Sensitivity Gene Interactions and Durability of Quantitative Resistance to Septoria Nodorum Blotch in Eastern US Wheat}, volume={11}, ISSN={["1664-462X"]}, DOI={10.3389/fpls.2020.00155}, abstractNote={Important advances have been made in understanding the relationship of necrotrophic effectors (NE) and host sensitivity (Snn) genes in the Parastagonospora nodorum-wheat pathosystem. Yet much remains to be learned about the role of these interactions in determining wheat resistance levels in the field, and there is mixed evidence on whether breeding programs have selected against Snn genes due to their role in conferring susceptibility. SNB occurs ubiquitously in the U.S. Atlantic seaboard, and the environment is especially well suited to field studies of resistance to natural P. nodorum populations, as there are no other important wheat leaf blights. Insights into the nature of SNB resistance have been gleaned from multi-year data on phenotypes and markers in cultivars representative of the region’s germplasm. In this perspective article, we review the evidence that in this eastern region of the U.S., wheat cultivars have durable quantitative SNB resistance and Snn–NE interactions are of limited importance. This conclusion is discussed in light of the relevant available information from other parts of the world.}, journal={FRONTIERS IN PLANT SCIENCE}, author={Cowger, Christina and Ward, Brian and Brown-Guedira, Gina and Brown, James K. M.}, year={2020}, month={Mar} }
 @article{ward_brown-guedira_kolb_van sanford_tyagi_sneller_griffey_2019, title={Genome-wide association studies for yield-related traits in soft red winter wheat grown in Virginia}, volume={14}, ISSN={["1932-6203"]}, DOI={10.1371/journal.pone.0208217}, abstractNote={Grain yield is a trait of paramount importance in the breeding of all cereals. In wheat (Triticum aestivum L.), yield has steadily increased since the Green Revolution, though the current rate of increase is not forecasted to keep pace with demand due to growing world population and increasing affluence. While several genome-wide association studies (GWAS) on yield and related component traits have been performed in wheat, the previous lack of a reference genome has made comparisons between studies difficult. In this study, a GWAS for yield and yield-related traits was carried out on a population of 322 soft red winter wheat lines across a total of four rain-fed environments in the state of Virginia using single-nucleotide polymorphism (SNP) marker data generated by a genotyping-by-sequencing (GBS) protocol. Two separate mixed linear models were used to identify significant marker-trait associations (MTAs). The first was a single-locus model utilizing a leave-one-chromosome-out approach to estimating kinship. The second was a sub-setting kinship estimation multi-locus method (FarmCPU). The single-locus model identified nine significant MTAs for various yield-related traits, while the FarmCPU model identified 74 significant MTAs. The availability of the wheat reference genome allowed for the description of MTAs in terms of both genetic and physical positions, and enabled more extensive post-GWAS characterization of significant MTAs. The results indicate a number of promising candidate genes contributing to grain yield, including an ortholog of the rice aberrant panicle organization (APO1) protein and a gibberellin oxidase protein (GA2ox-A1) affecting the trait grains per square meter, an ortholog of the Arabidopsis thaliana mother of flowering time and terminal flowering 1 (MFT) gene affecting the trait seeds per square meter, and a B2 heat stress response protein affecting the trait seeds per head.}, number={2}, journal={PLOS ONE}, author={Ward, Brian P. and Brown-Guedira, Gina and Kolb, Frederic L. and Van Sanford, David A. and Tyagi, Priyanka and Sneller, Clay H. and Griffey, Carl A.}, year={2019}, month={Feb} }
 @article{ward_brown-guedira_tyagi_kolb_van sanford_sneller_griffey_2019, title={Multienvironment and Multitrait Genomic Selection Models in Unbalanced Early-Generation Wheat Yield Trials}, volume={59}, ISSN={["1435-0653"]}, DOI={10.2135/cropsci2018.03.0189}, abstractNote={Genotyping costs for this project were supported by the Agriculture and Food Research Initiative Competitive Grant 2017-67007-25939 from the USDA National Institute of Food and Agriculture. The lead author was supported by the Virginia Agricultural Council Grant 617, and by funding from the Virginia Small Grains Board.}, number={2}, journal={CROP SCIENCE}, author={Ward, Brian P. and Brown-Guedira, Gina and Tyagi, Priyanka and Kolb, Frederic L. and Van Sanford, David A. and Sneller, Clay H. and Griffey, Carl A.}, year={2019}, pages={491–507} }
 @article{huang_ward_griffey_van sanford_mckendry_brown-guedira_tyagi_sneller_2018, title={The Accuracy of Genomic Prediction between Environments and Populations for Soft Wheat Traits}, volume={58}, ISSN={["1435-0653"]}, DOI={10.2135/cropsci2017.10.0638}, abstractNote={Triticeae Coordinated Agricultural Project of the USDA National Institute of Food and Agriculture [2011-68002-30029]}, number={6}, journal={CROP SCIENCE}, author={Huang, Mao and Ward, Brian and Griffey, Carl and Van Sanford, David and McKendry, Anne and Brown-Guedira, Gina and Tyagi, Priyanka and Sneller, Clay}, year={2018}, pages={2274–2288} }