BG2: Bayesian variable selection in generalized linear mixed models with nonlocal priors for non-Gaussian GWAS data.

Abstract excerpt

BACKGROUND: 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. RESULTS:...

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