Accounting for epistasis improves genomic prediction of phenotypes with univariate and bivariate models across environments.

ABSTRACT:

Key message

The accuracy of genomic prediction of phenotypes can be increased by including the top-ranked pairwise SNP interactions into the prediction model. We compared the predictive ability of various prediction models for a maize dataset derived from 910 doubled haploid lines from two European landraces (Kemater Landmais Gelb and Petkuser Ferdinand Rot), which were tested at six locations in Germany and Spain. The compared models were Genomic Best Linear Unbiased Prediction (GBLUP) as an additive model, Epistatic Random Regression BLUP (ERRBLUP) accounting for all pairwise SNP interactions, and selective Epistatic Random Regression BLUP (sERRBLUP) accounting for a selected subset of pairwise SNP interactions. These models have been compared in both univariate and bivariate sta

SUBMITTER: Vojgani E 

PROVIDER: S-EPMC8354961 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature