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G3 (Bethesda). 2016 Jul 7;6(7):2043-50. doi: 10.1534/g3.116.028563.

Exhaustive Genome-Wide Search for SNP-SNP Interactions Across 10 Human Diseases.

Author information

1
Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut 06510.
2
Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut 06510 andrew.dewan@yale.edu.

Abstract

The identification of statistical SNP-SNP interactions may help explain the genetic etiology of many human diseases, but exhaustive genome-wide searches for these interactions have been difficult, due to a lack of power in most datasets. We aimed to use data from the Resource for Genetic Epidemiology Research on Adult Health and Aging (GERA) study to search for SNP-SNP interactions associated with 10 common diseases. FastEpistasis and BOOST were used to evaluate all pairwise interactions among approximately N = 300,000 single nucleotide polymorphisms (SNPs) with minor allele frequency (MAF) ≥ 0.15, for the dichotomous outcomes of allergic rhinitis, asthma, cardiac disease, depression, dermatophytosis, type 2 diabetes, dyslipidemia, hemorrhoids, hypertensive disease, and osteoarthritis. A total of N = 45,171 subjects were included after quality control steps were applied. These data were divided into discovery and replication subsets; the discovery subset had > 80% power, under selected models, to detect genome-wide significant interactions (P < 10(-12)). Interactions were also evaluated for enrichment in particular SNP features, including functionality, prior disease relevancy, and marginal effects. No interaction in any disease was significant in both the discovery and replication subsets. Enrichment analysis suggested that, for some outcomes, interactions involving SNPs with marginal effects were more likely to be nominally replicated, compared to interactions without marginal effects. If SNP-SNP interactions play a role in the etiology of the studied conditions, they likely have weak effect sizes, involve lower-frequency variants, and/or involve complex models of interaction that are not captured well by the methods that were utilized.

KEYWORDS:

GERA; epistasis; gene-gene interaction; statistical interaction

PMID:
27185397
PMCID:
PMC4938657
DOI:
10.1534/g3.116.028563
[Indexed for MEDLINE]
Free PMC Article

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