An Effective Strategy for Trait Combinations in Multiple-Trait Genomic Selection

Abstract

Multiple-trait genomic selection (MTGS) is a recently developed method of genomic selection for satisfying the requirements of actual breeding, which usually aims to improve multiple traits simultaneously. Although many efforts have been made to develop MTGS prediction models, how to set the trait combination for the best performance of MTGS prediction models is still under exploration. In this study, we first classified the traits into two groups according to the single-trait genomic selection predictions: traits with a relatively high and low prediction performance. Then, we constructed three trait combinations (High & High, Low & Low, and High & Low) and evaluated their effects on the performance of a state-of-the-art MTGS prediction model using phenotypic and genotypic data from a maize diversity panel. Cross-validation experimental results indicate that single trait predictions could be used as reference for trait combinations in multi-trait genomic selection.

Type
Publication
ICIC 2017: Intelligent Computing Theories and Application
Date