Peer review

Open Access

Disease resistance traits are complex and quantitative in nature. Breeders regularly evaluate multiple important traits across diverse environments to employ them in genomics-assisted breeding. In this study, we evaluated the prospects of genomic prediction models by incorporating genome-wide association study (GWAS) results into single-trait and multitrait genomic prediction scenarios, using two distinct panels: the NMBU panel and the GRAMINOR panel. A standard genomic prediction model (Base) and the Base model with the addition of significant GWAS markers as fixed covariates (Base + GWAS) were tested on both panels. The predictive ability of models was measured in terms of prediction ability by using Pearson's correlation method. An improvement of 0.05% to as high as a two-fold improvement was observed in both the panels for single-trait and multitrait scenarios. In general, multitrait models outperformed single-trait models regardless of whether the GWAS markers were included. This study further concludes that multitrait-based genomic predictions are superior to single trait-based ones when the associated traits are used and are well correlated.

Text in English