ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2023
Sample size determination for training set optimization in genomic prediction.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Optimizing training sets for genomic selection to identify superior genotypes across multiple environments.G3 (Bethesda, Md.) · 2026Article
- Improving multibreed genomic prediction for breeds with small populations by modeling heterogeneous genetic (co)variance blockwise accounting for linkage disequilibrium.Journal of animal science and biotechnology · 2025Article
- Optimizing genomic prediction for complex traits via investigating multiple factors in switchgrass.Plant physiology · 2025Article
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- Genomic selection in a kiwiberry breeding programme: integrating intra- and inter-specific crossing.Molecular breeding : new strategies in plant improvement · 2025Article
- Sparse testing designs for optimizing resource allocation in multi-environment cassava breeding trials.The plant genome · 2025Article
- Multi-trait ridge regression BLUP withFrontiers in plant science · 2025Article
- Optimizing training sets to identify superior genotypes in hybrid populations.Frontiers in plant science · 2025Article
- Constructing training sets for genomic selection to identify superior genotypes in candidate populations.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Selective Genotyping and Phenotyping for Optimization of Genomic Prediction Models for Populations with Different Diversity.Plants (Basel, Switzerland) · 2024Article
- Maximizing efficiency in sunflower breeding through historical data optimization.Plant methods · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
key messageA practical approach is developed to determine a cost-effective optimal training set for selective phenotyping in a genomic prediction study. An R function is provided to facilitate the application of the approach. Genomic prediction (GP) is a statistical method used to select quantitative traits in animal or plant breeding. For this purpose, a statistical prediction model is first built that uses phenotypic and genotypic data in a training set. The trained model is then used to predict genomic estimated breeding values (GEBVs) for individuals within a breeding population. Setting the sample size of the training set usually takes into account time and space constraints that are inevitable in an agricultural experiment. However, the determination of the sample size remains an unresolved issue for a GP study. By applying the logistic growth curve to identify prediction accuracy for the GEBVs and the training set size, a practical approach was developed to determine a cost-effective optimal training set for a given genome dataset with known genotypic data. Three real genome datasets were used to illustrate the proposed approach. An R function is provided to facilitate widespread application of this approach to sample size determination, which can help breeders to identify a set of genotypes with an economical sample size for selective phenotyping.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.