Bayesian optimization of multivariate genomic prediction models based on secondary traits for improved accuracy gains and phenotyping costs.

Abstract excerpt

KEY MESSAGE: We propose a novel approach to the Bayesian optimization of multivariate genomic prediction models based on secondary traits to improve accuracy gains and phenotyping costs via efficient Pareto frontier estimation. Multivariate genomic prediction based on secondary traits, such as data from various omics technologies including high-throughput phenotyping (e.g., unmanned aerial vehicle-based remote...

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