ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2018
Bayesian optimization for genomic selection: a method for discovering the best genotype among a large number of candidates.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Optimizing training sets for genomic selection to identify superior genotypes across multiple environments.G3 (Bethesda, Md.) · 2026Article
- Optimizing training sets to identify superior genotypes in hybrid populations.Frontiers in plant science · 2025Article
- EcoBOT: an AI/ML enabled automated phenotyping capability for model plants.Frontiers in plant science · 2025Article
- Constructing training sets for genomic selection to identify superior genotypes in candidate populations.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Training set optimization is a feasible alternative for perennial orphan crop domestication and germplasm management: anFrontiers in plant science · 2024Article
- IP4GS: Bringing genomic selection analysis to breeders.Frontiers in plant science · 2023Article
- Genomic Prediction: Progress and Perspectives for Rice Improvement.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Bayesian optimisation for breeding schemes.Frontiers in plant science · 2022Article
- From gene banks to farmer's fields: using genomic selection to identify donors for a breeding program in rice to close the yield gap on smallholder farms.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Article
- Review
- ASAS-NANP SYMPOSIUM: prospects for interactive and dynamic graphics in the era of data-rich animal science1.Journal of animal science · 2021Article
- Whole-genome sequence diversity and association analysis of 198 soybean accessions in mini-core collections.DNA research : an international journal for rapid publication of reports on genes and genomes · 2021Article
- Training Set Optimization for Sparse Phenotyping in Genomic Selection: A Conceptual Overview.Frontiers in plant science · 2021Article
- Enhancing Genetic Gain through Genomic Selection: From Livestock to Plants.Plant communications · 2020Review
- Towards a fully automated algorithm driven platform for biosystems design.Nature communications · 2019Article
- Training set determination for genomic selection.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2019Article
- Accelerating crop genetic gains with genomic selection.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2019Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
key messageA new pre-breeding strategy based on an optimization algorithm is proposed and evaluated via simulations. This strategy can find superior genotypes with less phenotyping effort. Genomic prediction is a promising approach to search for superior genotypes among a large number of accessions in germplasm collections preserved in gene banks. When some accessions are phenotyped and genotyped, a prediction model can be built, and the genotypic values of the remaining accessions can be predicted from their marker genotypes. In this study, we focused on the application of genomic prediction to pre-breeding, and propose a novel strategy that would reduce the cost of phenotyping needed to discover better accessions. We regarded the exploration of superior genotypes with genomic prediction as an optimization problem, and introduced Bayesian optimization to solve it. Bayesian optimization, that samples unobserved inputs according to the expected improvement (EI) as a selection criterion, seemed to be beneficial in pre-breeding. The EI depends on the predicted distribution of genotypic values, whereas usual selection depends only on the point estimate. We simulated a search for the best genotype among candidate genotypes and showed that the EI-based strategy required fewer genotypes to identify the best genotype than the usual and random selection strategy. Therefore, Bayesian optimization can be useful for applying genomic prediction to pre-breeding and would reduce the number of phenotyped accessions needed to find the best accession among a large number of candidates.
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Registered trials
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.