BGWAS: Bayesian variable selection in linear mixed models with nonlocal priors for genome-wide association studies

BGWAS: Bayesian variable selection in linear mixed models with nonlocal priors for genome-wide association studies

BACKGROUND: Genome-wide association studies (GWAS) seek to identify single nucleotide polymorphisms (SNPs) that cause observed phenotypes. However, with highly correlated SNPs, correlated observations, and the number of SNPs being two orders of magnitude larger than the number of observations, GWAS...

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Bibliographic Details
Main Authors: Ferreira, M.A.R (Author), Williams, J. (Author), Xu, S. (Author)
Format: Article
Language:English
Published: NLM (Medline) 2023
Subjects:
Bayes theorem
Bayes Theorem
Bayesian
computer simulation
Computer Simulation
genome-wide association study
Genome-Wide Association Study
GWAS
Linear Models
Model selection
phenotype
Phenotype
Polymorphism, Single Nucleotide
procedures
single nucleotide polymorphism
statistical model
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