Detecting genetic interactions in pathway-based genome-wide association studies

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Pathway-based genome-wide association studies (GWAS) can exploit collective effects of causal variants in a pathway to increase power of detection. However, current methods for pathway-based GWAS do not consider epistatic effects of genetic variants, although interactions between genetic variants may play an important role in influencing complex traits. In this paper, we employed a Bayesian Lasso logistic regression model for pathway-based GWAS to include all possible main effects and a large number of pairwise interactions of single nucleotide polymorphisms (SNPs) in a pathway, and then inferred the model with an efficient group empirical Bayesian Lasso (EBLasso) method. Using the inferred model, the statistical significance of a pathway was tested with the Wald statistics. Reliable effects in a significant pathway were selected using the stability selection technique. Extensive computer simulations demonstrated that our group EBlasso method significantly outperformed two competitive methods in most simulation setups and offered similar performance in other simulation setups. When applying to a GWAS dataset for Parkinson disease, EBLasso identified three significant pathways including the primary bile acid biosynthesis pathway, the neuroactive ligand-receptor interaction, and the MAPK signaling pathway. All effects identified in the primary bile acid biosynthesis pathway and many of effects in the other two pathways were epistatic effects. The group EBLasso method provides a valuable tool for pathway-based GWAS to identify main and epistatic effects of genetic variants.

Original languageEnglish
Pages (from-to)300-309
Number of pages10
JournalGenetic Epidemiology
Volume38
Issue number4
DOIs
StatePublished - Jan 1 2014

Fingerprint

Genome-Wide Association Study
Bayes Theorem
Bile Acids and Salts
Logistic Models
Statistical Models
Computer Simulation
Single Nucleotide Polymorphism
Parkinson Disease
Ligands
alachlor

Keywords

  • Epistasis
  • Group EBlasso
  • GWAS
  • Parkinson disease
  • Pathway

ASJC Scopus subject areas

  • Genetics(clinical)
  • Epidemiology

Cite this

Detecting genetic interactions in pathway-based genome-wide association studies. / Huang, Anhui; Martin, Eden R; Vance, Jeffery M; Cai, Xiaodong.

In: Genetic Epidemiology, Vol. 38, No. 4, 01.01.2014, p. 300-309.

Research output: Contribution to journalArticle

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