Bivariate Genomic Prediction of Phenotypes by Selecting Epistatic Interactions Across Years Based on Haplotype Blocks and Pruned Sets of SNPs

Abstract The importance of accurate genomic prediction of phenotypes in plant breeding is undeniable, as higher prediction accuracy can increase selection responses. In this study, we investigated the ability of three models to improve prediction accuracy by including phenotypic information from the last growing season. This was done by considering a single biological trait in two growing seasons (2017 and 2018) as separate traits in a multi-trait model. Thus, bivariate variants of the Genomic Best Linear Unbiased Prediction (GBLUP) as an additive model, Epistatic Random Regression BLUP (ERRBLUP) and selective Epistatic Random Regression BLUP (sERRBLUP) as epistasis models were compared with respect to their prediction accuracies for the second year. The results indicate that bivariate ERRBLUP is almost identical to bivariate GBLUP in prediction accuracy, while bivariate sERRBLUP has the highest prediction accuracy in most cases. The obtained prediction accuracies were similar when utilizing pruned sets of SNPs and haplotype blocks, while utilizing haplotype blocks reduces the computational load significantly compared to utilizing pruned sets of SNPs. The prediction accuracies of bivariate GBLUP, ERRBLUP and sERRBLUP have been assessed across eight phenotypic traits and studied datasets from 471/402 doubled haploid lines in the European maize landrace Kemater Landmais Gelb/Petkuser Ferdinand Rot. We further investigated the genomic correlation, phenotypic correlation and trait heritability as factors affecting the bivariate models’ prediction accuracy, with genetic correlation between growing seasons being the most important one. For all three considered model architectures results were far worse when using a univariate version of the model.