ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2019
Training set determination for genomic selection.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Optimizing training sets for genomic selection to identify superior genotypes across multiple environments.G3 (Bethesda, Md.) · 2026Article
- Rapid selection of early-maturing and low-ear position maize hybrids suitable for China using genomic prediction.Plant molecular biology · 2026Article
- 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
- Optimizing core collections for genetic studies: a worldwide flax germplasm case study.Frontiers in plant science · 2025Article
- Optimizing training sets to identify superior genotypes in hybrid populations.Frontiers in plant science · 2025Article
- Multi-trait ridge regression BLUP withFrontiers in plant science · 2025Article
- Impact of different genomic relationship matrix construction methods on the accuracy of genomic prediction in different species.Frontiers in genetics · 2025Article
- Constructing training sets for genomic selection to identify superior genotypes in candidate populations.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Genomic estimated selection criteria and parental contributions in parent selection increase genetic gain of maternal haploid inducers in maize.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Maximizing efficiency in sunflower breeding through historical data optimization.Plant methods · 2024Article
- Training set optimization is a feasible alternative for perennial orphan crop domestication and germplasm management: anFrontiers in plant science · 2024Article
- A statistical package for evaluation of hybrid performance in plant breeding via genomic selection.Scientific reports · 2023Article
- Sample size determination for training set optimization in genomic prediction.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- A comparison of methods for training population optimization in genomic selection.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Combining genetic resources and elite material populations to improve the accuracy of genomic prediction in apple.G3 (Bethesda, Md.) · 2022Article
- Selection of parental lines for plant breedingFrontiers in plant science · 2022Article
- Genomic Prediction: Progress and Perspectives for Rice Improvement.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Building a Calibration Set for Genomic Prediction, Characteristics to Be Considered, and Optimization Approaches.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Genomic prediction and training set optimization in a structured Mediterranean oat population.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Article
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Authors and funding
2 authors.
Funding
Abstract
key messageA new optimality criterion is proposed to determine a training set for genomic selection, which is derived from Pearson's correlation between GEBVs and phenotypic values of a test set. R functions are provided to generate the optimal training set. For a specified test set, we develop a highly efficient algorithm to determine an optimal subset from a large candidate set in which the individuals have been genotyped but not phenotyped yet. The chosen subset serves as a training set to be phenotyped, and then a genomic selection (GS) model is built based on its phenotype and genotype data. In this study, we consider the additive effects whole-genome regression model and adopt ridge regression estimation for marker effects in the GS model. The resulting GS model is then employed to predict genomic estimated breeding values (GEBVs) for the individuals of the test set, which have been genotyped only. We propose a new optimality criterion to determine the required training set, which is derived directly from Pearson's correlation between GEBVs and phenotypic values of the test set. Pearson's correlation is the standard measure for prediction accuracy of a GS model. Our proposed methods can be applied to data with the varying degree of population structure. All the R functions for implementing our training set determination algorithms are available from the R package TSDFGS. The algorithms are illustrated with two datasets which have strong (rice genome dataset) and mild (wheat genome dataset) population structures. Our methods are shown to be advantageous over existing ones, mainly because they fully use the genomic relationship between the test set and the training set by taking into account both the variance and bias for predicting GEBVs.
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