Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat - Sandhu, Karansher S.; Patil, Shruti Sunil; Aoun, Meriem; Carter, Arron H. (Frontiers in Genetics, 2022)
Soft white wheat is a wheat class used in foreign and domestic markets to make various end products requiring specific quality attributes. Due to associated cost, time, and amount of seed needed, phenotyping for the end-use quality trait is delayed until later generations. Previously, we explored the potential of using genomic selection (GS) for selecting superior genotypes earlier in the breeding program, permitting time and resources for more extensive selection for grain yield and other agronomic traits. Breeders typically measure the multiple traits across various locations, and it opens up the avenue for exploring multi-trait-based GS models. This study’s main objective was to explore the potential of using multi-trait GS models for predicting seven different end-use quality traits using cross-validation, independent prediction, and across-location predictions in a wheat breeding program. The population used consisted of 666 soft white wheat genotypes planted for five years at two locations in Washington, USA. We optimized and compared the performances of four uni- and multi-trait-based GS models, namely, Bayes B, genomic best linear unbiased prediction (GBLUP), multilayer perceptron (MLP), and random forests. The prediction accuracies for multi-trait GS models were 5.5% and 7.9% superior to uni-trait models for the within environment and across location predictions. Multi-trait machine and deep learning models performed superior to GBLUP and Bayes B for across location predictions, but their advantages diminished when the genotype by environment component was included in the models. The highest improvement in prediction accuracy, i.e., 35%, was obtained for flour protein content with the multi-trait MLP model. This study showed the potential of using multi-trait-based GS models to enhance prediction accuracy by using information from previously phenotyped traits. It would assist in speeding up the breeding cycle time in a cost-friendly manner.