Genomic prediction and association mapping of maize grain yield in multi-environment trials based on reaction norm models.

ABSTRACT: Genotype-by-environment interaction (GEI) is among the greatest challenges for maize breeding programs. Strong GEI limits both the prediction of genotype performance across variable environmental conditions and the identification of genomic regions associated with grain yield. Incorporating GEI into yield prediction models has been shown to improve prediction accuracy of yield; nevertheless, more work is needed to further understand this complex interaction across populations and environments. The main objectives of this study were to: 1) assess GEI in maize grain yield based on reaction norm models and predict hybrid performance across a gradient of environmental (EG) conditions and 2) perform a genome-wide association study (GWAS) and post-GWAS analyses for maize grain yield using data f

SUBMITTER: Tolley SA 

PROVIDER: S-EPMC10501150 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature