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.
Authors
- Lucı́a Gutiérrez (ORCID: https://orcid.org/0000-0002-2957-3086)
- Julie C. Dawson (ORCID: https://orcid.org/0000-0002-9907-8611)
- Pablo Sandro (ORCID: https://orcid.org/0009-0009-8997-0161)
- Bettina Lado (ORCID: https://orcid.org/0000-0002-7054-1609)
- Rishap Dhakal
- Pablo González Barrios
- Martin Quincke
- Paula Silva (ORCID: https://orcid.org/0000-0003-2655-2949)
- Guillermo Sniadower
- Inés Rebollo (ORCID: https://orcid.org/0000-0002-9899-0801)
Institutions
- University of Minnesota (US)
- University of Wisconsin–Madison (US)
- Universidad de la República de Uruguay (UY)
- Swedish University of Agricultural Sciences (SE)
- Instituto Nacional de Investigación Agropecuaria (UY)
Publication Details
- Journal
- The Plant Genome
- Published
- 2026-05-08
- DOI
- https://doi.org/10.1002/tpg2.70247
- Primary Topic
- Genetic and phenotypic traits in livestock
- Type
- article
- Field-Weighted Citation Impact
- 0.00