ArticleBMC plant biology2022
Genomic prediction models for traits differing in heritability for soybean, rice, and maize.
Article in BMC plant biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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42 citing papers in PubMed.
- Improving multi-trait genomic prediction using synthetic traits from hyperspectral data based on co-heritability.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Improving hybrid breeding efficiency in winter oilseed rape under sparse multi-environment testing.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Insights from farming Macrocystis pyrifera offshore: phenotypic analysis, genome-wide association studies, genomic selection.Heredity · 2026Article
- Multi-environment evaluation and genomic prediction of agronomic traits in the southern US rice genepool.The plant genome · 2026Article
- G2P datasets: a hub for genomic datasets for predictive modeling in plants and animals.G3 (Bethesda, Md.) · 2026Article
- Large scale wheat data integration improves genomic prediction accuracy with the potential to facilitate international breeding collaborations.Communications biology · 2026Article
- A Quantitative Trait Nucleotide-Based Genomic Selection Strategy for Seed Oil and Protein Content in Soybean.Plants (Basel, Switzerland) · 2026Article
- Sharing approaches in predictive genomics across animals, plants and humans.Nature genetics · 2026Review
- Optimizing genomic predictions in maize using a diversity panel and a multiparental population.The plant genome · 2026Article
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- Dissecting fruit weight and quality traits in Australian passion fruit through genetic linkage mapping and QTL analysis.Frontiers in plant science · 2026Article
- Genetic parameters estimation and optimization of genomic selection in mud crab (Scylla paramamosain): a case study for growth-related traits.BMC genomics · 2025Article
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- Insights from farmingbioRxiv : the preprint server for biology · 2025Article
- Leveraging genetic resources and genomic prediction to enhance flavonol content in cranberry fruit.The plant genome · 2025Article
- Genomic prediction for heat and herbicide tolerance in faba bean.The plant genome · 2025Article
- Integrating multi-omics and machine learning for disease resistance prediction in legumes.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Genetic architecture of ideotype-related traits in middle American beans through single trait, multi-trait and epistatic genome-wide analyses.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Environment ensemble models for genomic prediction in common bean (Phaseolus vulgaris L.).The plant genome · 2025Article
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4 authors.
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Abstract
backgroundGenomic selection is a powerful tool in plant breeding. By building a prediction model using a training set with markers and phenotypes, genomic estimated breeding values (GEBVs) can be used as predictions of breeding values in a target set with only genotype data. There is, however, limited information on how prediction accuracy of genomic prediction can be optimized. The objective of this study was to evaluate the performance of 11 genomic prediction models across species in terms of prediction accuracy for two traits with different heritabilities using several subsets of markers and training population proportions. Species studied were maize (Zea mays, L.), soybean (Glycine max, L.), and rice (Oryza sativa, L.), which vary in linkage disequilibrium (LD) decay rates and have contrasting genetic architectures.
resultsCorrelations between observed and predicted GEBVs were determined via cross validation for three training-to-testing proportions (90:10, 70:30, and 50:50). Maize, which has the shortest extent of LD, showed the highest prediction accuracy. Amongst all the models tested, Bayes B performed better than or equal to all other models for each trait in all the three crops. Traits with higher broad-sense and narrow-sense heritabilities were associated with higher prediction accuracy. When subsets of markers were selected based on LD, the accuracy was similar to that observed from the complete set of markers. However, prediction accuracies were significantly improved when using a subset of total markers that were significant at P ≤ 0.05 or P ≤ 0.10. As expected, exclusion of QTL-associated markers in the model reduced prediction accuracy. Prediction accuracy varied among different training population proportions.
conclusionsWe conclude that prediction accuracy for genomic selection can be improved by using the Bayes B model with a subset of significant markers and by selecting the training population based on narrow sense heritability.
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