New Model Predicts Wheat Traits With 50-100% Higher Accuracy
Researchers have developed a genomic prediction method that dramatically improves wheat breeding by accounting for how plants respond differently to varying weather conditions. The technique could accelerate crop development cycles and help breeders select better varieties faster, reducing time-to-market for improved wheat varieties.
Originaltitel: Improving genomic prediction in wheat with random regression models with genotype‐specific phenology‐driven environmental covariates
) modeled as a factor analytic (FA) model, and RRM were compared for their predictive ability performance. RRM with three ECs outperformed GBLUP achieving 50%-100% higher accuracy in CV1 and CV2. The FA exhibited the highest accuracy overall for CV2 but not for CV1. At least one RRM model improved predictions in >89% of environments when predicting new, un-phenotyped environments. Integrating ECs into the RRM enhances genomic prediction by effectively capturing the GEI with a limited number of covariates.