Multivariate genomic prediction model with weighted kernel.

The developed Bayesian algorithm is a tool to model GxE for genomic prediction without the requirement of clonal genetic material or inbred lines (i.e., genetically heterogeneous populations). The proposed weighted kernel (WK) that incorporates both allele frequency and GWAS summary statistics was designed to reflect more reliable genetic relationship for a specific phenotypic trait. Furthermore, the WK framework can be extended to other omics data to capture more accurate genetic relationship and then to improve genomic prediction.