ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2009
Accuracy of genotypic value predictions for marker-based selection in biparental plant populations.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 188 papers, 1 of them a synthesis that pooled it.
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Who cites it
188 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Genomic Prediction for Germplasm Improvement Through Inter-Heterotic-Group Line Crossing in Maize.International journal of molecular sciences · 2025Article
- Optimization of high-throughput marker systems for genomic prediction in alfalfa family bulks.The plant genome · 2025Article
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- Comparative genomic prediction of resistance to Fusarium wilt (Fusarium oxysporum f. sp. niveum race 2) in watermelon: parametric and nonparametric approaches.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Genome-wide association study and genomic prediction of leaf spot (Frontiers in plant science · 2025Article
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128 more citing papers are in PubMed but not listed here.
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Abstract
The availability of cheap and abundant molecular markers has led to plant-breeding methods that rely on the prediction of genotypic value from marker data, but published information is lacking on the accuracy of genotypic value predictions with empirical data in plants. Our objectives were to (1) determine the accuracy of genotypic value predictions from multiple linear regression (MLR) and genomewide selection via best linear unbiased prediction (BLUP) in biparental plant populations; (2) assess the accuracy of predictions for different numbers of markers (N(M)) and progenies (N(P)) used in estimation; and (3) determine if an empirical Bayes approach for modeling of the variances of individual markers and of epistatic effects leads to more accurate predictions in empirical data. We divided each of four maize (Zea mays L.) datasets, one Arabidopsis dataset, and two barley (Hordeum vulgare L.) datasets into an estimation set, where marker effects were calculated, and a test set, where genotypic values were predicted based on markers. Predictions were more accurate with BLUP than with MLR. Predictions became more accurate as N(P) and N(M) increased, until sufficient genome coverage was reached. Modeling marker variances with the empirical Bayes method sometimes led to slightly better predictions, but the accuracy with different variants of the empirical Bayes method was often inconsistent. In nearly all cases, the accuracy with BLUP was not significantly different from the highest accuracy across all methods. Accounting for epistasis in the empirical Bayes procedure led to poorer predictions. We concluded that among the methods considered, the quick and simple BLUP approach is the method of choice for predicting genotypic value in biparental plant populations.
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