ArticleCommunications biology2022
BayesR3 enables fast MCMC blocked processing for largescale multi-trait genomic prediction and QTN mapping analysis.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
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- Genetic parameters estimation and optimization of genomic selection in mud crab (Scylla paramamosain): a case study for growth-related traits.BMC genomics · 2025Article
- Effect of using preselected markers from imputed whole-genome sequence for genomic prediction in Angus cattle.Genetics, selection, evolution : GSE · 2025Article
- Identification of MAPK10 as a Candidate Gene for High Milk Production in Water Buffaloes Through a Genome-Wide Association Study.Animals : an open access journal from MDPI · 2025Article
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- Assessing the utility of genomic selection to breed for durable Ascochyta blight resistance in chickpea.The plant genome · 2025Article
- An integrative approach to prioritize candidate causal genes for complex traits in cattle.PLoS genetics · 2025Article
- Enhancing Genomic Prediction Accuracy with a Single-Step Genomic Best Linear Unbiased Prediction Model Integrating Genome-Wide Association Study Results.Animals : an open access journal from MDPI · 2025Article
- Integrative genomics sheds light on the immunogenetics of tuberculosis in cattle.Communications biology · 2025Article
- Evaluation of genomic selection models using whole genome sequence data and functional annotation in Belgian Blue cattle.Genetics, selection, evolution : GSE · 2025Article
- DeepAnnotation: A novel interpretable deep learning-based genomic selection model that integrates comprehensive functional annotations.GigaScience · 2025Article
- Advancements and limitations in polygenic risk score methods for genomic prediction: a scoping review.Human genetics · 2024Article
- Optimising desired gain indices to maximise selection response.Frontiers in plant science · 2024Article
- Gene expression and RNA splicing explain large proportions of the heritability for complex traits in cattle.Cell genomics · 2023Article
- Interpreting single-step genomic evaluation as a neural network of three layers: pedigree, genotypes, and phenotypes.Genetics, selection, evolution : GSE · 2023Article
- Genomic selection for target traits in the Australian lentil breeding program.Frontiers in plant science · 2023Article
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Abstract
Bayesian methods, such as BayesR, for predicting the genetic value or risk of individuals from their genotypes, such as Single Nucleotide Polymorphisms (SNP), are often implemented using a Markov Chain Monte Carlo (MCMC) process. However, the generation of Markov chains is computationally slow. We introduce a form of blocked Gibbs sampling for estimating SNP effects from Markov chains that greatly reduces computational time by sampling each SNP effect iteratively n-times from conditional block posteriors. Subsequent iteration over all blocks m-times produces chains of length m × n. We use this strategy to solve large-scale genomic prediction and fine-mapping problems using the Bayesian MCMC mixed-effects genetic model, BayesR3. We validate the method using simulated data, followed by analysis of empirical dairy cattle data using high dimension milk mid infra-red spectra data as an example of "omics" data and show its use to increase the precision of mapping variants affecting milk, fat, and protein yields relative to a univariate analysis of milk, fat, and protein.
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