A Bayesian model for genomic prediction using metabolic networks

Abstract

Genomic prediction is now an indispensable technique in breeding and medical fields, and it is of interest how to use omics data to enhance prediction accuracy. A precedent work proposed a metabolic network-based method in biomass prediction of Arabidopsis, however, the method consists of multiple steps which possibly degrade prediction accuracy. We proposed a Bayesian model that integrates all steps and jointly infers all fluxes of reactions related to biomass production. The proposed model showed higher accuracies than methods compared both in simulated and real data. The results support the excellent idea of the precedent study that the metabolite network information is useful for phenotype prediction.