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• W4386780211 abstract "Genome-wide association studies (GWASes) aim to identify single nucleotide polymorphisms (SNPs) associated with a given phenotype. A common approach for the analysis of GWAS is single marker analysis (SMA) based on linear mixed models (LMMs). However, LMM-based SMA usually yields a large number of false discoveries and cannot be directly applied to non-Gaussian phenotypes such as count data.We present a novel Bayesian method to find SNPs associated with non-Gaussian phenotypes. To that end, we use generalized linear mixed models (GLMMs) and, thus, call our method Bayesian GLMMs for GWAS (BG2). To deal with the high dimensionality of GWAS analysis, we propose novel nonlocal priors specifically tailored for GLMMs. In addition, we develop related fast approximate Bayesian computations. BG2 uses a two-step procedure: first, BG2 screens for candidate SNPs; second, BG2 performs model selection that considers all screened candidate SNPs as possible regressors. A simulation study shows favorable performance of BG2 when compared to GLMM-based SMA. We illustrate the usefulness and flexibility of BG2 with three case studies on cocaine dependence (binary data), alcohol consumption (count data), and number of root-like structures in a model plant (count data)." @default.
• W4386780211 created "2023-09-16" @default.
• W4386780211 creator A5035026708 @default.
• W4386780211 creator A5059626476 @default.
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• W4386780211 date "2023-09-15" @default.
• W4386780211 modified "2023-10-14" @default.
• W4386780211 title "BG2: Bayesian variable selection in generalized linear mixed models with nonlocal priors for non-Gaussian GWAS data" @default.
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• W4386780211 doi "https://doi.org/10.1186/s12859-023-05468-w" @default.
• W4386780211 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37715138" @default.
• W4386780211 hasPublicationYear "2023" @default.
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