Constructing training sets for genomic selection to identify superior genotypes in candidate populations
Journal
Theoretical and Applied Genetics
Journal Volume
137
Journal Issue
12
ISSN
0040-5752
1432-2242
Date Issued
2024-11-17
Author(s)
Abstract
Key message: Approaches for constructing training sets in genomic selection are proposed to efficiently identify top-performing genotypes from a breeding population. Abstract: 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, and Pearson’s correlation are employed to assess performance in both simulation studies and real trait analyses. Overall, for candidate populations lacking a strong subpopulation structure, a ridge regression-based method, referred to as MSPERidge, is recommended. For candidate populations with a strong subpopulation structure, a heuristic-based version of generalized coefficient of determination CDmean(v2) and a D-optimality-like method that maximizes overall genomic variation (GVoverall) are preferred approaches for the primary objective of plant breeding. For populations with a large number of candidates, a proposed ranking method (GVaverage) can first be used to down-scale the candidate population, after which a heuristic-based method is employed to identify the best genotypes. Notably, the proposed CDmean(v2) has been verified to be equivalent to the original version, known as CDmean, but its implementation is much more computationally efficient.
SDGs
Publisher
Springer Science and Business Media LLC
Description
Article number: 270
Type
journal article