ReviewGenetics, selection, evolution : GSE2022
Application of Bayesian genomic prediction methods to genome-wide association analyses.
Review in Genetics, selection, evolution : GSE, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Bayesian Genome-Wide Association Study of Feed Efficiency Traits in Pigs.Animals : an open access journal from MDPI · 2026Article
- Potential Applications of Genome-Wide Association Studies in Establishing Climate Resilience in Livestock: A Comprehensive Review.International journal of molecular sciences · 2026Review
- Genomic prediction accuracy for low hydrogen cyanide selection in fresh cassava roots. Comparative model analysis.BMC plant biology · 2026Article
- Sharing approaches in predictive genomics across animals, plants and humans.Nature genetics · 2026Review
- Genetic analysis of High Immune Response technology and relationships with disease resilience in pigs.Journal of animal science · 2026Article
- Longitudinal genomic prediction of cacao disease resilience identifies robust witches' broom disease targets.Frontiers in plant science · 2026Article
- Sequence-based chromatin activity modeling and regulatory impact prediction of genetic variants in farmed animals using deep learning.NAR genomics and bioinformatics · 2025Article
- Advancements in Animal Breeding: From Mendelian Genetics to Machine Learning.International journal of molecular sciences · 2025Review
- Single-variant genome-wide association study and regional heritability mapping of protein efficiency and performance traits in Large White pigs.Genetics, selection, evolution : GSE · 2025Article
- Bivariate GWAS performed on rabbits divergently selected for intramuscular fat content reveals pleiotropic genomic regions and genes related to meat and carcass quality traits.Genetics, selection, evolution : GSE · 2025Article
- Genetic Strategies for Enhancing Rooster Fertility in Tropical and Humid Climates: Challenges and Opportunities.Animals : an open access journal from MDPI · 2025Review
- Genomic and phenomic prediction for soybean seed yield, protein, and oil.The plant genome · 2025Article
- Optimizing fully-efficient two-stage models for genomic selection using open-source software.Plant methods · 2025Article
- Mutual information-based best linear unbiased prediction for enhanced genomic prediction accuracy.Journal of animal science · 2025Article
- Deep learning for genomic selection of aquatic animals.Marine life science & technology · 2024Review
- Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non-additive variation for key traits.The plant genome · 2023Article
- Bayesian Approaches in Exploring Gene-environment and Gene-gene Interactions: A Comprehensive Review.Cancer genomics & proteomics · 2023Review
- Article
- Genome-wide family prediction unveils molecular mechanisms underlying the regulation of agronomic traits inFrontiers in plant science · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBayesian genomic prediction methods were developed to simultaneously fit all genotyped markers to a set of available phenotypes for prediction of breeding values for quantitative traits, allowing for differences in the genetic architecture (distribution of marker effects) of traits. These methods also provide a flexible and reliable framework for genome-wide association (GWA) studies. The objective here was to review developments in Bayesian hierarchical and variable selection models for GWA analyses.
resultsBy fitting all genotyped markers simultaneously, Bayesian GWA methods implicitly account for population structure and the multiple-testing problem of classical single-marker GWA. Implemented using Markov chain Monte Carlo methods, Bayesian GWA methods allow for control of error rates using probabilities obtained from posterior distributions. Power of GWA studies using Bayesian methods can be enhanced by using informative priors based on previous association studies, gene expression analyses, or functional annotation information. Applied to multiple traits, Bayesian GWA analyses can give insight into pleiotropic effects by multi-trait, structural equation, or graphical models. Bayesian methods can also be used to combine genomic, transcriptomic, proteomic, and other -omics data to infer causal genotype to phenotype relationships and to suggest external interventions that can improve performance.
conclusionsBayesian hierarchical and variable selection methods provide a unified and powerful framework for genomic prediction, GWA, integration of prior information, and integration of information from other -omics platforms to identify causal mutations for complex quantitative traits.
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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 Socratic graph.