@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}, 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}, abstractNote={Marker-assisted selection is important for cultivar development. We propose a system where a training population genotyped for QTL and genome-wide markers may predict QTL haplotypes in early development germplasm. Breeders screen germplasm with molecular markers to identify and select individuals that have desirable haplotypes. The objective of this research was to investigate whether QTL haplotypes can be accurately predicted using SNPs derived by genotyping-by-sequencing (GBS). In the SunGrains program during 2020 (SG20) and 2021 (SG21), 1,536 and 2,352 lines submitted for GBS were genotyped with markers linked to the Fusarium head blight QTL: Qfhb.nc-1A, Qfhb.vt-1B, Fhb1, and Qfhb.nc-4A. In parallel, data were compiled from the 2011-2020 Southern Uniform Winter Wheat Scab Nursery (SUWWSN), which had been screened for the same QTL, sequenced via GBS, and phenotyped for: visual Fusarium severity rating (SEV), percent Fusarium damaged kernels (FDK), deoxynivalenol content (DON), plant height, and heading date. Three machine learning models were evaluated: random forest, k-nearest neighbors, and gradient boosting machine. Data were randomly partitioned into training-testing splits. The QTL haplotype and 100 most correlated GBS SNPs were used for training and tuning of each model. Trained machine learning models were used to predict QTL haplotypes in the testing partition of SG20, SG21, and the total SUWWSN. Mean disease ratings for the observed and predicted QTL haplotypes were compared in the SUWWSN. For all models trained using the SG20 and SG21, the observed Fhb1 haplotype estimated group means for SEV, FDK, DON, plant height, and heading date in the SUWWSN were not significantly different from any of the predicted Fhb1 calls. This indicated that machine learning may be utilized in breeding programs to accurately predict QTL haplotypes in earlier generations.}, 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={Abstract}, 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={The majority of studies evaluating genomic selection (GS) for plant breeding have used single‐trait, single‐site models that ignore genotype × environment interaction (GEI) effects. However, such studies do not accurately reflect the complexities of many applied breeding programs, and previous papers have found that models that incorporate GEI effects and multiple traits can increase the accuracy of genomic estimated breeding values (GEBVs). This study's goal was to test GS methods for prediction in scenarios that simulate early‐generation yield testing by correcting for field spatial variation, and fitting multienvironment and multitrait models on data for 14 traits of varying heritability evaluated in unbalanced designs across four environments. Corrections for spatial variation increased across‐environment trait heritability by 25%, on average, but had little effect on model predictive ability. Results between all models were generally equivalent when predicting the performance of newly introduced genotypes. However, models incorporating GEI information and multiple traits increased prediction accuracy by up to 9.6% for low‐heritability traits when phenotypic data were sparsely collected across environments. The results suggest that GS models using multiple traits and incorporating GEI effects may best be suited to predicting line performance in new environments when phenotypic data have already been collected across a subset of the total testing environments.}, 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={Genomic selection (GS) uses training population (TP) data to estimate the value of lines in a selection population. In breeding, the TP and selection population are often grown in different environments, which can cause low prediction accuracy when the correlation of genetic effects between the environments is low. Subsets of TP data may be more predictive than using all TP data. Our objectives were (i) to evaluate the effect of using subsets of TP data on GS accuracy between environments, and (ii) to assess the accuracy of models incorporating marker × environment interaction (MEI). Two wheat (Triticum aestivum L.) populations were phenotyped for 11 traits in independent environments and genotyped with single‐nucleotide polymorphism markers. Within each population–trait combination, environments were clustered. Data from one cluster were used as the TP to predict the value of the same lines in the other cluster(s) of environments. Models were built using all TP data or subsets of markers selected for their effect and stability. The GS accuracy using all TP data was >0.25 for 9 of 11 traits. The between‐environment accuracy was generally greatest using a subset of stable and significant markers; accuracy increased up to 48% relative to using all TP data. We also assessed accuracy using each population as the TP and the other as the selection population. Using subsets of TP data or the MEI models did not improve accuracy between populations. Using optimized subsets of markers within a population can improve GS accuracy by reducing noise in the prediction data set.}, 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} }