Multivariate genomic selection models improve prediction accuracy of agronomic traits in soft red winter wheat

Abstract

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.