Bayesian optimization for genomic selection: a method for discovering the best genotype among a large number of candidates.

A new pre-breeding strategy based on an optimization algorithm is proposed and evaluated via simulations. This strategy can find superior genotypes with less phenotyping effort. Genomic prediction is a promising approach to search for superior genotypes among a large number of accessions in germplasm collections preserved in gene banks. When some accessions are phenotyped and genotyped, a prediction model can be built, and the genotypic values of the remaining accessions can be predicted from their marker genotypes. In this study, we focused on the application of genomic prediction to pre-breeding, and propose a novel strategy that would reduce the cost of phenotyping needed to discover better accessions. We regarded the exploration of superior genotypes with genomic prediction as an optimization problem, and introduced Bayesian optimization to solve it. Bayesian optimization, that samples unobserved inputs according to the expected improvement (EI) as a selection criterion, seemed to be beneficial in pre-breeding. The EI depends on the predicted distribution of genotypic values, whereas usual selection depends only on the point estimate. We simulated a search for the best genotype among candidate genotypes and showed that the EI-based strategy required fewer genotypes to identify the best genotype than the usual and random selection strategy. Therefore, Bayesian optimization can be useful for applying genomic prediction to pre-breeding and would reduce the number of phenotyped accessions needed to find the best accession among a large number of candidates.