@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={AbstractTremendous progress has been made in variety development and host plant resistance to mitigate the impact of Fusarium head blight (FHB) since the disease manifested in the southeastern United States in the early 2000s. Much of this improvement was made possible through the establishment of and recurring support from the US Wheat & Barley Scab Initiative (USWBSI). Since its inception in 1997, the USWBSI has enabled land‐grant institutions to make advances in reducing the annual threat of devastating FHB epidemics. A coordinated field phenotyping effort for annual germplasm screening has become a staple tool for selection in public and private soft red winter wheat (SRWW) breeding programmes. Dedicated efforts of many SRWW breeders to identify and utilize resistance genes from both native and exotic sources provided a strong foundation for improvement. In recent years, implementation of genomics‐enabled breeding has further accelerated genetic gains in FHB resistance. This article reflects on the improvement of FHB resistance in southern SRWW and contextualizes the monumental progress made by collaborative, persistent, and good old‐fashioned cultivar development.}, 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{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={AbstractWheat (Triticum aestivum L.) is crucial to global food security but is often threatened by diseases, pests, and environmental stresses. Wheat‐stem sawfly (Cephus cinctus Norton) poses a major threat to food security in the United States, and solid‐stem varieties, which carry the stem‐solidness locus (Sst1), are the main source of genetic resistance against sawfly. Marker‐assisted selection uses molecular markers to identify lines possessing beneficial haplotypes, like that of the Sst1 locus. In this study, an R package titled “HaploCatcher” was developed to predict specific haplotypes of interest in genome‐wide genotyped lines. A training population of 1056 lines genotyped for the Sst1 locus, known to confer stem solidness, and genome‐wide markers was curated to make predictions of the Sst1 haplotypes for 292 lines from the Colorado State University wheat breeding program. Predicted Sst1 haplotypes were compared to marker‐derived haplotypes. Our results indicated that the training set was substantially predictive, with kappa scores of 0.83 for k‐nearest neighbors and 0.88 for random forest models. Forward validation on newly developed breeding lines demonstrated that a random forest model, trained on the total available training data, had comparable accuracy between forward and cross‐validation. Estimated group means of lines classified by haplotypes from PCR‐derived markers and predictive modeling did not significantly differ. The HaploCatcher package is freely available and may be utilized by breeding programs, using their own training populations, to predict haplotypes for whole‐genome sequenced early generation material.}, 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}, abstractNote={At the time of publication, it appears that there was scientific literature which was contradictory to a statement made in the abstract.The contradictory statement is that "This locus was identified on a chromosome where no other Hessian fly resistance/tolerance QTL has been previously identified."}, 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_larkin_lozada_dewitt_brown-guedira_mason_2023, title={Multivariate genomic selection models improve prediction accuracy of agronomic traits in soft red winter wheat}, volume={5}, ISSN={["1435-0653"]}, DOI={10.1002/csc2.20994}, abstractNote={AbstractUnivariate genomic selection (UVGS) is an important tool for increasing genetic gain and multivariate GS (MVGS), where correlated traits are included in genomic selection, which can improve genomic prediction accuracy. The objectives for this study were to evaluate MVGS approaches to improve prediction accuracy for four agronomic traits using a training population of 351 soft red winter wheat (Triticum aestivum L.) genotypes, evaluated over six site‐years in Arkansas from 2014 to 2017. Genotypes were phenotyped for grain yield, heading date, plant height, and test weight in both the training and test populations. In cross‐validations, various combinations of traits in MVGS models significantly improved prediction accuracy for test weight in comparison to a UVGS model. Marginal increases in predictive accuracy were also observed for grain yield, plant height, and heading date. Multivariate models which were identified as superior to the univariate case in cross‐validations were forward validated by predicting the advanced breeding nurseries of 2018 and 2020. In forward validation, consistent increases in accuracy were observed for test weight, plant height, and heading date using MVGS instead of UVGS. Overall, MVGS models improved prediction accuracies when correlated traits were included with the predicted response. The methods outlined in this study may be used to achieve higher prediction accuracies in unbalanced datasets over multiple environments.}, journal={CROP SCIENCE}, author={Winn, Zachary J. and Larkin, Dylan L. and Lozada, Dennis N. and DeWitt, Noah and Brown-Guedira, Gina and Mason, Richard Esten}, year={2023}, month={May} }
 @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={AbstractFusarium 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  and , respectively. Forward validation for DON indicated a predictive accuracy of  and , respectively. Forward validation using environments in cluster one for FDK indicated a predictive accuracy of  and , respectively. Forward validation using environments in cluster one for DON indicated a predictive accuracy of  and , 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_reisig_murphy_2023, title={Yield protection afforded by imidacloprid during Hessian fly infestation in six genotypes}, volume={3}, ISSN={["1435-0645"]}, DOI={10.1002/agj2.21308}, abstractNote={AbstractThe Hessian fly (Mayetiola destructor Say) is a gall midge that infests and feeds upon wheat (Triticum aestivum L.). Recently, a new form of tolerance (QHft.nc‐7D) was identified in the breeding line LA03136E71 (PI 700336). Partial resistance allows immature Hessian fly to thrive in small numbers and does not function like antibiosis. Little is known about the potential yield drag of using partial resistance. In this study, we evaluated six genotypes: one containing QHft.nc‐7D (LA03136E71), one containing H13, and four potentially susceptible genotypes. All genotypes were evaluated with two different seed treatment regiments of imidacloprid: no treatment and a two times rate of imidacloprid. All tested genotypes were planted in six‐to‐eight replications of a full factorial design in four environments. Subsamples of yield trial plots were taken to measure percent infested tillers and a number of larvae/pupae per tiller. Plots were harvested and grain yield was recorded. There was a significant (p[>F] < 0.05) reduction of percent infested tillers and a number of larvae/pupae per tiller related to the imidacloprid treatment. Imidacloprid treatment significantly (p[>T] < 0.05) reduced the number of larvae/pupae per tiller for LA03136E71. There was no significant (p[>T] > 0.05) grain yield increase associated with treatment for LA03136E71. This indicates that a two times application of imidacloprid on LA03136E71 (QHft.nc‐7D) did not improve yield yet reduced infestation. Therefore, QHft.nc‐7D, while allowing Hessian fly to thrive on the plant, does not significantly compromise yield.}, journal={AGRONOMY JOURNAL}, author={Winn, Zachary J. J. and Reisig, Dominic and Murphy, Joseph P. P.}, year={2023}, month={Mar} }
 @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{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{winn_larkin_murry_moon_mason_2021, title={Phenotyping Anther Extrusion of Wheat Using Image Analysis}, volume={11}, ISSN={["2073-4395"]}, DOI={10.3390/agronomy11061244}, abstractNote={Phenotyping wheat (Triticum aestivum L.) is time-consuming and new methods are necessary to decrease labor. To develop a heterotic pool of male wheat lines for hybrid breeding, there must be an efficient way to measure both anther extrusion and the size of anthers. Five hundred and ninety-four soft red winter wheat lines in two replications of randomized complete block design were phenotyped for anther extrusion, a key trait for hybrid wheat production. A device was constructed to capture images using a mobile device. Four heads were sampled per line when anthesis was evident for half the heads in the plot. The extruded anthers were scraped onto a surface, their image was captured, and the area of the anthers was taken via ImageJ. The number of anthers extruded was estimated by counting the number of anthers per image and dividing by the number of heads sampled. The area per anther was taken by dividing the area of anthers per spike by the number of anthers per spike. A significant correlation (R=0.9, p<0.0001) was observed between the area of anthers per spike and the number of anthers per spike. This method is proposed as a substitute for field ratings of anther extrusion and to quantitatively measure the size of anthers.}, number={6}, journal={AGRONOMY-BASEL}, author={Winn, Zachary James and Larkin, Dylan Lee and Murry, Jamison Trey and Moon, David Earl and Mason, Richard Esten}, year={2021}, month={Jun} }