Bayesian Optimization Approaches for Identifying the Best Genotype from a Candidate Population

Abstract

AbstractBayesian optimization is incorporated into genomic prediction to identify the best genotype from a candidate population. Several expected improvement (EI) criteria are proposed for the Bayesian optimization. The iterative search process of the optimization consists of two main steps. First, a genomic BLUP (GBLUP) prediction model is constructed using the phenotype and genotype data of a training set. Second, an EI criterion, estimated from the resulting GBLUP model, is employed to select the individuals that are phenotyped and added to the current training set to update the GBLUP model until the sequential observed EI values are less than a stopping tolerance. Three real datasets are analyzed to illustrate the proposed approach. Furthermore, a detailed simulation study is conducted to compare the performance of the EI criteria. The simulation results show that one augmented version derived from the distribution of predicted genotypic values is able to identify the best genotype from a large candidate population with an economical training set, and it can therefore be recommended for practical use. Supplementary materials accompanying this paper appear on-line.