@article{winn_lyerly_brown-guedira_murphy_mason_2023, title={Utilization of a publicly available diversity panel in genomic prediction of Fusarium head blight resistance traits in wheat}, volume={5}, ISSN={["1940-3372"]}, DOI={10.1002/tpg2.20353}, abstractNote={Fusarium head blight (FHB) is an economically and environmentally concerning disease of wheat (Triticum aestivum L). A two-pronged approach of marker-assisted selection coupled with genomic selection has been suggested when breeding for FHB resistance. A historical dataset comprised of entries in the Southern Uniform Winter Wheat Scab Nursery (SUWWSN) from 2011 to 2021 was partitioned and used in genomic prediction. Two traits were curated from 2011 to 2021 in the SUWWSN: percent Fusarium damaged kernels (FDK) and deoxynivalenol (DON) content. Heritability was estimated for each trait-by-environment combination. A consistent set of check lines was drawn from each year in the SUWWSN, and k-means clustering was performed across environments to assign environments into clusters. Two clusters were identified as FDK and three for DON. Cross-validation on SUWWSN data from 2011 to 2019 indicated no outperforming training population in comparison to the combined dataset. Forward validation for FDK on the SUWWSN 2020 and 2021 data indicated a predictive accuracy r≈0.58$r \approx 0.58$ and r≈0.53$r \approx 0.53$ , respectively. Forward validation for DON indicated a predictive accuracy of r≈0.57$r \approx 0.57$ and r≈0.45$r \approx 0.45$ , respectively. Forward validation using environments in cluster one for FDK indicated a predictive accuracy of r≈0.65$r \approx 0.65$ and r≈0.60$r \approx 0.60$ , respectively. Forward validation using environments in cluster one for DON indicated a predictive accuracy of r≈0.67$r \approx 0.67$ and r≈0.60$r \approx 0.60$ , respectively. These results indicated that selecting environments based on check performance may produce higher forward prediction accuracies. This work may be used as a model for utilizing public resources for genomic prediction of FHB resistance traits across public wheat breeding programs.}, journal={PLANT GENOME}, author={Winn, Zachary J. J. and Lyerly, Jeanette H. H. and Brown-Guedira, Gina and Murphy, Joseph P. P. and Mason, Richard Esten}, year={2023}, month={May} }
 @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{ballen-taborda_lyerly_smith_howell_brown-guedira_babar_harrison_mason_mergoum_murphy_et al._2022, title={Utilizing genomics and historical data to optimize gene pools for new breeding programs: A case study in winter wheat}, volume={13}, ISSN={["1664-8021"]}, DOI={10.3389/fgene.2022.964684}, abstractNote={With the rapid generation and preservation of both genomic and phenotypic information for many genotypes within crops and across locations, emerging breeding programs have a valuable opportunity to leverage these resources to 1) establish the most appropriate genetic foundation at program inception and 2) implement robust genomic prediction platforms that can effectively select future breeding lines. Integrating genomics-enabled breeding into cultivar development can save costs and allow resources to be reallocated towards advanced (i.e., later) stages of field evaluation, which can facilitate an increased number of testing locations and replicates within locations. In this context, a reestablished winter wheat breeding program was used as a case study to understand best practices to leverage and tailor existing genomic and phenotypic resources to determine optimal genetics for a specific target population of environments. First, historical multi-environment phenotype data, representing 1,285 advanced breeding lines, were compiled from multi-institutional testing as part of the SunGrains cooperative and used to produce GGE biplots and PCA for yield. Locations were clustered based on highly correlated line performance among the target population of environments into 22 subsets. For each of the subsets generated, EMMs and BLUPs were calculated using linear models with the 'lme4' R package. Second, for each subset, TPs representative of the new SC breeding lines were determined based on genetic relatedness using the 'STPGA' R package. Third, for each TP, phenotypic values and SNP data were incorporated into the 'rrBLUP' mixed models for generation of GEBVs of YLD, TW, HD and PH. Using a five-fold cross-validation strategy, an average accuracy of r = 0.42 was obtained for yield between all TPs. The validation performed with 58 SC elite breeding lines resulted in an accuracy of r = 0.62 when the TP included complete historical data. Lastly, QTL-by-environment interaction for 18 major effect genes across three geographic regions was examined. Lines harboring major QTL in the absence of disease could potentially underperform (e.g., Fhb1 R-gene), whereas it is advantageous to express a major QTL under biotic pressure (e.g., stripe rust R-gene). This study highlights the importance of genomics-enabled breeding and multi-institutional partnerships to accelerate cultivar development.}, journal={FRONTIERS IN GENETICS}, author={Ballen-Taborda, Carolina and Lyerly, Jeanette and Smith, Jared and Howell, Kimberly and Brown-Guedira, Gina and Babar, Md. Ali and Harrison, Stephen A. A. and Mason, Richard E. E. and Mergoum, Mohamed and Murphy, J. Paul and et al.}, year={2022}, month={Oct} }
 @article{winn_acharya_merrill_lyerly_brown-guedira_cambron_harrison_reisig_murphy_2021, title={Mapping of a novel major effect Hessian fly field partial-resistance locus in southern soft red winter wheat line LA03136E71}, volume={8}, ISSN={["1432-2242"]}, DOI={10.1007/s00122-021-03936-6}, journal={THEORETICAL AND APPLIED GENETICS}, author={Winn, Z. J. and Acharya, R. and Merrill, K. and Lyerly, J. and Brown-Guedira, G. and Cambron, S. and Harrison, S. H. and Reisig, D. and Murphy, J. P.}, year={2021}, month={Aug} }
 @article{carmack_clark_lyerly_dong_brown-guedira_van sanford_2021, title={Optical sorter-augmented genomic selection lowers deoxynivalenol accumulation in wheat}, volume={6}, ISSN={["1435-0653"]}, DOI={10.1002/csc2.20494}, journal={CROP SCIENCE}, author={Carmack, W. Jesse and Clark, Anthony J. and Lyerly, H. Jeanette and Dong, Yanhong and Brown-Guedira, Gina and Van Sanford, David Anthony}, year={2021}, month={Jun} }
 @article{verges_lyerly_dong_van sanford_2020, title={Training Population Design With the Use of Regional Fusarium Head Blight Nurseries to Predict Independent Breeding Lines for FHB Traits}, volume={11}, ISSN={["1664-462X"]}, DOI={10.3389/fpls.2020.01083}, abstractNote={Fusarium head blight (FHB) is a devastating disease in cereals around the world. Because it is quantitatively inherited and technically difficult to reproduce, breeding to increase resistance in wheat germplasm is difficult and slow. Genomic selection (GS) is a form of marker-assisted selection (MAS) that simultaneously estimates all locus, haplotype, or marker effects across the entire genome to calculate genomic estimated breeding values (GEBVs). Since its inception, there have been many studies that demonstrate the utility of GS approaches to breeding for disease resistance in crops. In this study, the Uniform Northern (NUS) and Uniform Southern (SUS) soft red winter wheat scab nurseries (a total 452 lines) were evaluated as possible training populations (TP) to predict FHB traits in breeding lines of the UK (University of Kentucky) wheat breeding program. DON was best predicted by the SUS; Fusarium damaged kernels (FDK), FHB rating, and two indices, DSK index and DK index were best predicted by NUS. The highest prediction accuracies were obtained when the NUS and SUS were combined, reaching up to 0.5 for almost all traits except FHB rating. Highest prediction accuracies were obtained with bigger TP sizes (300-400) and there were not significant effects of TP optimization method for all traits, although at small TP size, the PEVmean algorithm worked better than other methods. To select for lines with tolerance to DON accumulation, a primary breeding target for many breeders, we compared selection based on DON BLUES with selection based on DON GEBVs, DSK GEBVs, and DK GEBVs. At selection intensities (SI) of 30-40%, DSK index showed the best performance with a 4-6% increase over direct selection for DON. Our results confirm the usefulness of regional nurseries as a source of lines to predict GEBVs for local breeding programs, and shows that an index that includes DON, together with FDK and FHB rating could be an excellent choice to identify lines with low DON content and an overall improved FHB resistance.}, journal={FRONTIERS IN PLANT SCIENCE}, author={Verges, Virginia L. and Lyerly, Jeanette and Dong, Yanhong and Van Sanford, David A.}, year={2020}, month={Jul} }