A0788
Title: A Bayesian model for genomic prediction using metabolic networks
Authors: Akio Onogi - Ryukoku University (Japan) [presenting]
Abstract: Genomic prediction is now an essential technique in animal and plant breeding, and it is also used for predicting disease risks in medicine. One of current research interests in genomic prediction would be how omics data can be used to improve prediction accuracy. A precedent work proposed a metabolic network-based method in biomass prediction of Arabidopsis. The method is based on flux balance analysis where production and consumption of all metabolites are assumed to be balanced. Although the idea is unique, the method consists of multiple steps that possibly degrade prediction accuracy. Here, I 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 findings support the previous idea that metabolic network information can be used for prediction.
