What question did this study set out to answer?

This research aims to improve genomic prediction accuracy in wheat by using random regression models (RRM) with genotype-specific environmental covariates.

May 10, 2026Open Access

Improving genomic prediction in wheat with random regression models with genotype‐specific phenology‐driven environmental covariates

Puntos clave

This research aims to improve genomic prediction accuracy in wheat by using random regression models (RRM) with genotype-specific environmental covariates.
Compared RRM and GBLUP models for genomic prediction accuracy.
Evaluated performance across different environments (CV1 and CV2).
Integrated environmental covariates into RRM to capture genotype-environment interaction.
RRM outperformed GBLUP with 50%-100% higher accuracy in CV1 and CV2.
FA model showed the highest overall accuracy for CV2, but not for CV1.
At least one RRM model improved predictions in over 89% of un-phenotyped environments.

Resumen

) 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.

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