ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2024
Constructing training sets for genomic selection to identify superior genotypes in candidate populations.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Improving grain yield prediction in Southern US oat germplasm using genomics information and environmental covariates.The plant genome · 2026Article
- Optimizing training sets for genomic selection to identify superior genotypes across multiple environments.G3 (Bethesda, Md.) · 2026Article
- Normalized cumulative gain as an alternative evaluation measure for genomic selection models.Genetics, selection, evolution : GSE · 2025Article
- Optimizing training sets to identify superior genotypes in hybrid populations.Frontiers in plant science · 2025Article
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3 authors.
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Abstract
key messageApproaches 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, 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
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