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

Shuangshuang Xu1, Jacob Williams1, Marco A R Ferreira2

  • 1Department of Statistics, Virginia Tech, Blacksburg, VA, 24061, USA.

BMC Bioinformatics
|September 15, 2023
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Summary

This study introduces Bayesian Generalized Linear Mixed Models for Genome-Wide Association Studies (BG2), a novel method for identifying genetic variants linked to non-Gaussian traits. BG2 improves accuracy and handles complex data types, outperforming traditional single marker analysis.

Keywords:
Bayesian statisticsGLMMGWASNonlocal priorVariable selection

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Area of Science:

  • Genetics
  • Statistical Genomics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) identify single nucleotide polymorphisms (SNPs) associated with phenotypes.
  • Linear mixed models (LMMs) are common for GWAS but produce false discoveries and struggle with non-Gaussian data.
  • Non-Gaussian phenotypes, like count data, require advanced analytical methods for accurate genetic association analysis.

Purpose of the Study:

  • To develop a novel Bayesian method for identifying SNPs associated with non-Gaussian phenotypes in GWAS.
  • To address the limitations of existing methods, particularly the high false discovery rate and inability to handle non-Gaussian data.
  • To provide a flexible and accurate tool for analyzing complex genetic data.

Main Methods:

  • Introduced Bayesian Generalized Linear Mixed Models for GWAS (BG2), a novel Bayesian approach.
  • Utilized generalized linear mixed models (GLMMs) with novel nonlocal priors tailored for high-dimensional GWAS.
  • Developed fast approximate Bayesian computations and a two-step procedure involving SNP screening and model selection.

Main Results:

  • BG2 demonstrated favorable performance compared to GLMM-based single marker analysis (SMA) in simulation studies.
  • The method effectively handles non-Gaussian phenotypes, including binary and count data.
  • Case studies on cocaine dependence, alcohol consumption, and plant root development illustrated BG2's utility and flexibility.

Conclusions:

  • BG2 offers a powerful and accurate Bayesian alternative for GWAS, especially for non-Gaussian phenotypes.
  • The novel priors and computational methods enhance the analysis of complex genetic association data.
  • BG2 provides a valuable tool for genetic research across various biological and medical applications.