Constructing training sets for genomic selection to identify superior genotypes in candidate populations.

ABSTRACT:

Key message

Approaches for constructing training sets in genomic selection are proposed to efficiently identify top-performing genotypes from a breeding population. Identifying superior genotypes from a candidate population is a key objective in plant breeding programs. This study evaluates various methods for the training set optimization in genomic selection, with the goal of enhancing efficiency in discovering top-performing genotypes from a breeding population. Additionally, two approaches, inspired by classical optimal design criteria, are proposed to expand the search space for the best genotypes and compared with methods focusing on maximizing accuracy in breeding value prediction. Evaluation metrics such as normalized discounted cumulative gain, Spearman's rank correlation,

SUBMITTER: Chen SP 

PROVIDER: S-EPMC11570567 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature