An examination of single nucleotide polymorphism selection prioritization strategies for tests of gene-gene interaction

Abstract

Background: Given that genome-wide association studies (GWAS) of psychiatric disorders have identified only a small number of convincingly associated variants (single nucleotide polymorphism [SNP]), there is interest in seeking additional evidence for associated variants with tests of gene-gene interaction. Comprehensive pair-wise single SNP-SNP interaction analysis is computationally intensive, and the penalty for multiple testing is severe, given the number of interactions possible. Aiming to minimize these statistical and computational burdens, we have explored approaches to prioritize SNPs for interaction analyses.

Methods: Primary interaction analyses were performed with the Wellcome Trust Case-Control Consortium bipolar disorder GWAS (1868 cases, 2938 control subjects). Replication analyses were performed with the Genetic Association Information Network bipolar disorder dataset (1001 cases, 1033 control subjects). The SNPs were prioritized for interaction analysis that showed evidence for association that surpassed a number of nominally significant thresholds, are within genome-wide significant genes, or are within genes that are functionally related.

Results: For no set of prioritized SNPs did we obtain evidence to support the hypothesis that the selection strategy identified pairs of variants that were enriched for true (statistical) interactions.

Conclusions: The SNPs prioritized according to a number of criteria do not have a raised prior probability for significant interaction that is detectable in samples of this size. We argue that the use of significance levels reflecting only the number of tests performed, as is now widely accepted for single SNP analysis, does not offer an appropriate degree of protection against the potential for GWAS studies to generate an enormous number of false positive interactions.