Improving genomic prediction in wheat with random regression models with environmental covariates.

Wheat (<i>Triticum aestivum</i> L.), a crucial cereal crop for global food security, faces growing challenges from climate change. Future production requires varieties that are resilient to environmental extremes and fluctuations. The goal of this study was to assess strategies to increase selection response through genomic selection (GS) in wheat by integrating environmental covariates (ECs) and random regression models (RRM) in multi-environment trials. We analyzed phenotypic and genomic data from 1683 genotypes from 2010 to 2020 across 71 environments using 45 ECs derived from vegetative, reproductive, and grain-filling phenological phases. Seven key ECs were selected via partial least squares (PLS) regression to model genotype by environment interaction (GEI) and evaluate their integration in three different genomic prediction scenarios (CV0, CV1, and CV2). Genomic best linear unbiased prediction models (GBLUP), GBLUP models with GEI (GBLUP_GEI) Factor Analytic models (FA), and RRM were compared for their predictive ability performance. RRM with four ECs outperformed GBLUP, achieving 52–124% higher accuracy in CV1 and CV2, however FA exhibited the highest accuracy overall. At least one RRM model improved predictions in more than 90% of environments when predicting new, un-phenotyped environments. We conclude that integrating ECs into the RRM enhances genomic prediction by effectively capturing the GEI with few covariates.