@article{boyles_ballen-taborda_brown-guedira_costa_cowger_dewitt_griffey_harrison_ibrahim_johnson_et al._2023, title={Approaching 25 years of progress towards Fusarium head blight resistance in southern soft red winter wheat (Triticum aestivum L.)}, volume={8}, ISSN={["1439-0523"]}, DOI={10.1111/pbr.13137}, abstractNote={Abstract}, journal={PLANT BREEDING}, author={Boyles, Richard E. and Ballen-Taborda, Carolina and Brown-Guedira, Gina and Costa, Jose and Cowger, Christina and DeWitt, Noah and Griffey, Carl A. and Harrison, Stephen A. and Ibrahim, Amir and Johnson, Jerry and et al.}, year={2023}, month={Aug} }
 @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_hudson-arns_hammers_dewitt_lyerly_bai_st. amand_nachappa_haley_mason_2023, title={HaploCatcher: An R package for prediction of haplotypes}, ISSN={["1940-3372"]}, DOI={10.1002/tpg2.20412}, abstractNote={Abstract}, journal={PLANT GENOME}, author={Winn, Zachary James and Hudson-Arns, Emily and Hammers, Mikayla and DeWitt, Noah and Lyerly, Jeanette and Bai, Guihua and St. Amand, Paul and Nachappa, Punya and Haley, Scott and Mason, Richard Esten}, year={2023}, month={Nov} }
 @article{winn_acharya_merrill_lyerly_brown-guedira_cambron_harrison_reisig_murphy_2023, title={Mapping of a novel major effect Hessian fly field partial-resistance locus in southern soft red winter wheat line LA03136E71 (vol 134, pg 3911, 2021)}, volume={136}, ISSN={["1432-2242"]}, DOI={10.1007/s00122-023-04304-2}, number={4}, 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={2023}, month={Apr} }
 @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={Abstract}, 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}, 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{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-enabled1 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}, abstractNote={Hessian fly resistance has centralized around resistance loci that are biotype specific. We show that field resistance is evident and controlled by a single locus on chromosome 7D. Hessian flies (Mayetiola destructor Say) infest and feed upon wheat (Triticum aestivum L) resulting in significant yield loss. Genetically resistant cultivars are the most effective method of Hessian fly management. Wheat breeders in the southern USA have observed cultivars exhibiting a "field resistance" to Hessian fly that is not detectable by greenhouse assay. The resistant breeding line "LA03136E71" and susceptible cultivar "Shirley" were crossed to develop a population of 200 random F 4:5  lines using single seed descent. The population was evaluated in a total of five locations in North Carolina during the 2019, 2020, and 2021 seasons. A subsample of each plot was evaluated for the total number of tillers, number of infested tillers, and total number of larvae/pupae. From these data, the percent infested tillers, number of larvae/pupae per tiller, and the number of larvae/pupae per infested tiller were estimated. In all within and across environment combinations for all traits recorded, the genotype effect was significant (p < 0.05). Interval mapping identified a single large effect QTL distally on the short arm of chromosome 7D for all environment-trait combinations. This locus was identified on a chromosome where no other Hessian fly resistance/tolerance QTL has been previously identified. This novel Hessian fly partial-resistance QTL is termed QHft.nc-7D. Fine mapping must be conducted in this region to narrow down the causal agents responsible for this trait, and investigation into the mode of action is highly suggested.}, 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}, abstractNote={Abstract}, 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} }