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

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,

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