ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2015
Exploring the areas of applicability of whole-genome prediction methods for Asian rice (Oryza sativa L.).
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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45 citing papers in PubMed.
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- Genome-wide association study and genomic prediction for resistance to brown planthopper in rice.Frontiers in plant science · 2024Article
- Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice (Molecular breeding : new strategies in plant improvement · 2023Article
- Optimization of Multi-Generation Multi-location Genomic Prediction Models for Recurrent Genomic Selection in an Upland Rice Population.Rice (New York, N.Y.) · 2023Article
- Gene based markers improve precision of genome-wide association studies and accuracy of genomic predictions in rice breeding.Heredity · 2023Article
- A Bayesian model for genomic prediction using metabolic networks.Bioinformatics advances · 2023Article
- Enhancing genetic gain through the application of genomic selection in developing irrigated rice for the favorable ecosystem in Bangladesh.Frontiers in genetics · 2023Article
- Utilization of Genotyping-by-Sequencing (GBS) for Rice Pre-Breeding and Improvement: A Review.Life (Basel, Switzerland) · 2022Review
- Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review.Planta · 2022Review
- An R package VIGoR for joint estimation of multiple linear learners with variational Bayesian inference.Bioinformatics (Oxford, England) · 2022Article
- Genome-wide association study and genomic prediction for yield and grain quality traits of hybrid rice.Molecular breeding : new strategies in plant improvement · 2022Article
- Genomic Selection: A Tool for Accelerating the Efficiency of Molecular Breeding for Development of Climate-Resilient Crops.Frontiers in genetics · 2022Review
- Development of an inclusive 580K SNP array and its application for genomic selection and genome-wide association studies in rice.Frontiers in plant science · 2022Article
- Genomic Prediction: Progress and Perspectives for Rice Improvement.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Impact of early genomic prediction for recurrent selection in an upland rice synthetic population.G3 (Bethesda, Md.) · 2021Article
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Authors and funding
7 authors.
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Abstract
key messageOur simulation results clarify the areas of applicability of nine prediction methods and suggest the factors that affect their accuracy at predicting empirical traits. Whole-genome prediction is used to predict genetic value from genome-wide markers. The choice of method is important for successful prediction. We compared nine methods using empirical data for eight phenological and morphological traits of Asian rice cultivars (Oryza sativa L.) and data simulated from real marker genotype data. The methods were genomic BLUP (GBLUP), reproducing kernel Hilbert spaces regression (RKHS), Lasso, elastic net, random forest (RForest), Bayesian lasso (Blasso), extended Bayesian lasso (EBlasso), weighted Bayesian shrinkage regression (wBSR), and the average of all methods (Ave). The objectives were to evaluate the predictive ability of these methods in a cultivar population, to characterize them by exploring the area of applicability of each method using simulation, and to investigate the causes of their different accuracies for empirical traits. GBLUP was the most accurate for one trait, RKHS and Ave for two, and RForest for three traits. In the simulation, Blasso, EBlasso, and Ave showed stable performance across the simulated scenarios, whereas the other methods, except wBSR, had specific areas of applicability; wBSR performed poorly in most scenarios. For each method, the accuracy ranking for the empirical traits was largely consistent with that in one of the simulated scenarios, suggesting that the simulation conditions reflected the factors that affected the method accuracy for the empirical results. This study will be useful for genomic prediction not only in Asian rice, but also in populations from other crops with relatively small training sets and strong linkage disequilibrium structures.
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