Integrative Bayesian variable selection with gene-based informative priors for genome-wide association studies

Xiaoshuai Zhang, Fuzhong Xue, Hong Liu, Dianwen Zhu, Bin Peng, Joseph L. Wiemels, Xiaowei Yang

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Background: Genome-wide Association Studies (GWAS) are typically designed to identify phenotype-associated single nucleotide polymorphisms (SNPs) individually using univariate analysis methods. Though providing valuable insights into genetic risks of common diseases, the genetic variants identified by GWAS generally account for only a small proportion of the total heritability for complex diseases. To solve this "missing heritability" problem, we implemented a strategy called integrative Bayesian Variable Selection (iBVS), which is based on a hierarchical model that incorporates an informative prior by considering the gene interrelationship as a network. It was applied here to both simulated and real data sets. Results: Simulation studies indicated that the iBVS method was advantageous in its performance with highest AUC in both variable selection and outcome prediction, when compared to Stepwise and LASSO based strategies. In an analysis of a leprosy case-control study, iBVS selected 94 SNPs as predictors, while LASSO selected 100 SNPs. The Stepwise regression yielded a more parsimonious model with only 3 SNPs. The prediction results demonstrated that the iBVS method had comparable performance with that of LASSO, but better than Stepwise strategies. Conclusions: The proposed iBVS strategy is a novel and valid method for Genome-wide Association Studies, with the additional advantage in that it produces more interpretable posterior probabilities for each variable unlike LASSO and other penalized regression methods.

Original languageEnglish (US)
Article number130
JournalBMC Genetics
Volume15
Issue number1
DOIs
StatePublished - 2014
Externally publishedYes

Keywords

  • Bayesian hierarchical modeling
  • Bayesian variable selection
  • Biomarker discovery
  • Gene-based biomarkers
  • Integrative biomarker identification

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

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