Abstract

BackgroundA computationally efficient tool is required for a genome-wide gene-gene interaction analysis that tests an extremely large number of single-nucleotide polymorphism (SNP) interaction pairs in genome-wide association studies (GWAS). Current tools for GWAS interaction analysis are mainly developed for unrelated case-control samples. Relatively fewer tools for interaction analysis are available for complex disease studies with family-based design, and these tools tend to be computationally expensive.ResultsWe developed a fast gene-gene interaction test, GCORE-sib, for discordant sib pairs and implemented the test into an efficient tool. We used simulations to demonstrate that the GCORE-sib has correct type I error rates and has comparable power to that of the regression-based interaction test. We also showed that the GCORE-sib can run more than 10 times faster than the regression-based test. Finally, the GCORE-sib was applied to a GWAS dataset with approximately 2,000 discordant sib pairs, and the GCORE-sib finished testing 19,368,078,382 pairs of SNPs within 6 days.ConclusionsAn efficient gene-gene interaction tool for discordant sib pairs was developed. It will be very useful for genome-wide gene-gene interaction analysis in GWAS using discordant sib pairs. The tool can be downloaded for free at http://gcore-sib.sourceforge.net.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1145-z) contains supplementary material, which is available to authorized users.

Highlights

  • A computationally efficient tool is required for a genome-wide gene-gene interaction analysis that tests an extremely large number of single-nucleotide polymorphism (SNP) interaction pairs in genome-wide association studies (GWAS)

  • Genome-wide association studies (GWAS) are a popular strategy to investigate the genetic structure of complex diseases by identifying the association between single nucleotide polymorphisms (SNPs) and complex disorders

  • GWAS analysis is mainly focused on testing the effects of individual SNPs on complex diseases; complex diseases are likely to result from the interactions among multiple genes

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Summary

Introduction

A computationally efficient tool is required for a genome-wide gene-gene interaction analysis that tests an extremely large number of single-nucleotide polymorphism (SNP) interaction pairs in genome-wide association studies (GWAS). Genome-wide association studies (GWAS) are a popular strategy to investigate the genetic structure of complex diseases by identifying the association between single nucleotide polymorphisms (SNPs) and complex disorders. Several approaches, which can finish genome-wide interaction tests in a reasonable time while still maintaining statistical power, have been developed for GWAS with unrelated case-control samples. Some examples for these approaches include SNPHarvester [6], SNPRuler [7], and BOOST [8].

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