ArticlePloS one2019
Training set optimization of genomic prediction by means of EthAcc.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- Portability of genomic predictions trained on sparse factorial designs across two maize silage breeding cycles.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Optimization of Multi-Generation Multi-location Genomic Prediction Models for Recurrent Genomic Selection in an Upland Rice Population.Rice (New York, N.Y.) · 2023Article
- Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Combining genetic resources and elite material populations to improve the accuracy of genomic prediction in apple.G3 (Bethesda, Md.) · 2022Article
- Building a Calibration Set for Genomic Prediction, Characteristics to Be Considered, and Optimization Approaches.Methods in molecular biology (Clifton, N.J.) · 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
- Genomic prediction and training set optimization in a structured Mediterranean oat population.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Article
- Training Set Optimization for Sparse Phenotyping in Genomic Selection: A Conceptual Overview.Frontiers in plant science · 2021Article
- TrainSel: An R Package for Selection of Training Populations.Frontiers in genetics · 2021Article
- Factors Influencing Genomic Prediction Accuracies of Tropical Maize Resistance to Fall Armyworm and Weevils.Plants (Basel, Switzerland) · 2020Article
- Optimized breeding strategies to harness genetic resources with different performance levels.BMC genomics · 2020Article
- Improving Genomic Prediction of Crossbred and Purebred Dairy Cattle.Frontiers in genetics · 2020Article
- Discovery of interesting new polymorphisms in a sugar beet (eliteTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2019Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Genomic prediction is a useful tool for plant and animal breeding programs and is starting to be used to predict human diseases as well. A shortcoming that slows down the genomic selection deployment is that the accuracy of the prediction is not known a priori. We propose EthAcc (Estimated THeoretical ACCuracy) as a method for estimating the accuracy given a training set that is genotyped and phenotyped. EthAcc is based on a causal quantitative trait loci model estimated by a genome-wide association study. This estimated causal model is crucial; therefore, we compared different methods to find the one yielding the best EthAcc. The multilocus mixed model was found to perform the best. We compared EthAcc to accuracy estimators that can be derived via a mixed marker model. We showed that EthAcc is the only approach to correctly estimate the accuracy. Moreover, in case of a structured population, in accordance with the achieved accuracy, EthAcc showed that the biggest training set is not always better than a smaller and closer training set. We then performed training set optimization with EthAcc and compared it to CDmean. EthAcc outperformed CDmean on real datasets from sugar beet, maize, and wheat. Nonetheless, its performance was mainly due to the use of an optimal but inaccessible set as a start of the optimization algorithm. EthAcc's precision and algorithm issues prevent it from reaching a good training set with a random start. Despite this drawback, we demonstrated that a substantial gain in accuracy can be obtained by performing training set optimization.
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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.