Biology subjects

Winn, Z. J.

Publications and source records attributed to Winn, Z. J..

3 recordsLinked to original sources

bioRxiv2023

HaploCatcher: An R Package for Prediction of Haplotypes

Wheat (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) poeses 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 1,056 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. CORE IDEASO_LIIdentification, introgression, and frequency increase of large effect loci are important for cultivar development. C_LIO_LIThe Sst1 locus has a significant effect on cutting score in fields exposed to sawfly infestation. C_LIO_LIHistorical genetic information can be utilized to predict haplotypes for lines which have genome-wide genetic data. C_LIO_LIAn R package, HaploCatcher, has been developed to facilitate this analysis in other programs. C_LI

plant biology↗

bioRxiv2023

Phenomic vs Genomic Prediction - A Comparison of Prediction Accuracies for Grain Yield in Hard Winter Wheat Lines

Common bread wheat (Triticum aestivum L.) is a key component of global diets, but the genetic improvement of wheat is not keeping pace with the growing demands of the worlds population. To increase efficiency and reduce costs, breeding programs are rapidly adopting the use of unoccupied aerial vehicles (UAVs) to conduct high-throughput spectral analyses. This study examined the effectiveness of multispectral indices in predicting grain yield compared to genomic prediction. Multispectral data were collected on advanced generation yield nursery trials during the 2019-2021 growing seasons in the Colorado State University Wheat Breeding Program. Genome-wide genotyping was performed on these advanced generations and all plots were harvested to measure grain yield. Two methods were used to predict grain yield: genomic estimated breeding values (GEBVs) generated by a genomic best linear unbiased prediction (gBLUP) model and phenomic phenotypic estimates (PPEs) using only spectral indices via multiple linear regression (MLR), k-nearest neighbors (KNN), and random forest (RF) models. In cross-validation, PPEs produced by MLR, KNN, and RF models had higher prediction accuracy ([r]: 0.41 [≤][r] [≤] 0.48) than GEBVs produced by gBLUP ([r] = 0.35). In leave-one-year-out forward validation using only multispectral data for 2020 and 2021, PPEs from MLR and KNN models had higher prediction accuracy of grain yield than GEBVs of those same lines. These findings suggest that a limited number of spectra may produce PPEs that are more accurate than or equivalently accurate as GEBVs derived from gBLUP, and this method should be evaluated in earlier development material where sequencing is not feasible.

plant biology↗

bioRxiv2023

UTILIZATION OF A PUBLICLY AVAILABLE DIVERSITY PANEL IN GENOMIC PREDICTION OF FUSARIUM HEAD BLIGHT RESISTANCE TRAITS IN WHEAT

Fusarium head blight (FHB) is an economically and environmentally concerning disease of wheat (Triticum aestivum L). A two-pronged approach of marker assisted selection (MAS) coupled with genomic selection (GS) has been suggested when breeding for FHB resistance. An historical dataset comprised of entries in the Southern Uniform Winter Wheat Scab Nursery (SUWWSN) from 2011-2021 was partitioned and used in genomic prediction. Two traits were curated from 2011-2021 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 for FDK and three for DON. Cross-validation on SUWWSN data from 2011-2019 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 {approx} 0.58 and r {approx} 0.53, respectively. Forward validation for DON indicated a predictive accuracy of r {approx} 0.57 and r {approx} 0.45, respectively. Forward validation using environments in cluster one for FDK indicated a predictive accuracy of r {approx} 0.65 and r {approx} 0.60, respectively. Forward validation using environments in cluster one for DON indicated a predictive accuracy of r {approx} 0.67 and 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 to create a public resource for genomic prediction of FHB resistance traits across public wheat breeding programs. CORE IDEASO_LIThe data from the Southern Uniform Winter Wheat Nursery may be used for genomic prediction. C_LIO_LICreating training populations based on like-check performance improves forward genomic predictive accuracies. C_LIO_LIFiltering out locations with low genomic, per-plot, narrow-sense heritability may improve predictive accuracies. C_LI

plant biology↗