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- Title
BayesR3 enables fast MCMC blocked processing for largescale multi-trait genomic prediction and QTN mapping analysis.
- Authors
Breen, Edmond J.; MacLeod, Iona M.; Ho, Phuong N.; Haile-Mariam, Mekonnen; Pryce, Jennie E.; Thomas, Carl D.; Daetwyler, Hans D.; Goddard, Michael E.
- 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. BayesR3 samples the polymorphisms affecting complex traits at reduced computational cost to predict the genetic value, breeding value, or individual risk of genotypes.
- Subjects
MILKFAT; MARKOV chain Monte Carlo; GIBBS sampling; SINGLE nucleotide polymorphisms; MARKOV processes; MULTITRAIT multimethod techniques; GENETIC models
- Publication
Communications Biology, 2022, Vol 5, Issue 1, p1
- ISSN
2399-3642
- Publication type
Article
- DOI
10.1038/s42003-022-03624-1