Increased Predictive Accuracy of Multi-Environment Genomic Prediction Model for Yield and Related Traits in Spring Wheat (Triticum aestivum L.) - Tomar, Vipin; Singh, Daljit; Dhillon, Guriqbal Singh; Chung, Yong Suk; Poland, Jesse; Singh, Ravi Prakash; Joshi, Arun Kumar; Gautam, Yogesh; Tiwari, Budhi Sagar; Kumar, Uttam (Frontiers in Plant Science, 2021)
Genomic selection (GS) has the potential to improve the selection gain for complex traits in crop breeding programs from resource-poor countries. GS model performance in multi-environment (ME) trials was assessed for 141 advanced breeding lines under four field environments via cross-predictions. We compared prediction accuracy (PA) of two GS models with or without accounting environmental variation on four quantitative traits of significant importance, i.e. grain yield, thousand grain weight, days to heading, and days to maturity, under North and Central Indian conditions. For each trait, we generated PA using two different ME cross-validation (CV) schemes representing actual breeding scenarios: a) predicting untested lines in tested environments through ME model (ME_CV1), b) predicting tested lines in untested environments through ME model (ME_CV2). The ME predictions were compared with the baseline single-environment GS model (SE_CV1) representing a breeding scenario, where across environment relationships and interactions are not leveraged. Our results suggested that the ME models provide clear advantage over SE models in terms of robust trait predictions. Both ME models provided 2-3 times higher prediction accuracies for all four traits across the four tested environments, highlighting the importance of accounting for environmental variance in GS models. While the improvement in PA from SE to ME models was significant, the CV1 and CV2 schemes did not show any clear differences within ME, indicating the ME model was able to predict the untested environments and lines equally well. Overall, our results provide an important insight into the impact of environmental variation on GS in smaller breeding programs where these programs can potentially increase the rate of genetic gain by leveraging the multi-environment wheat breeding trials.