Jackknife-based gene-gene interactiontests for untyped SNPs.

Minsun Song

doi:10.1186/s12863-015-0225-9

Abstract

BackgroundTesting gene-gene interaction in genome-wide association studies generally yields lower power than testing marginal association. Meta-analysis that combines different genotyping platforms is one method used to increase power when assessing gene-gene interactions, which requires a test for interaction on untyped SNPs. However, to date, formal statistical tests for gene-gene interaction on untyped SNPs have not been thoroughly addressed. The key concern for gene-gene interaction testing on untyped SNPs located on different chromosomes is that the pair of genes might not be independent and the current generation of imputation methods provides imputed genotypes at the marginal accuracy.ResultsIn this study we address this challenge and describe a novel method for testing gene-gene interaction on marginally imputed values of untyped SNPs. We show that our novel Wald-type test statistics for interactions with and without constraints in the interaction parameters follow the asymptotic distributions which are the same as those of the corresponding tests for typed SNPs. Through simulations, we show that the proposed tests properly control type I error and are more powerful than the extension of the classical dosage method to interaction tests. The increase in power results from a proper correction for the uncertainty in imputation through the variance estimator using the jackknife, one of resampling techniques. We apply the method to detect interactions between SNPs on chromosomes 5 and 15 on lung cancer data. The inclusion of the results at the untyped SNPs provides a much more detailed information at the regions of interest.ConclusionsAs demonstrated by the simulation studies and real data analysis, our approaches outperform the application of traditional dosage method to detection of gene-gene interaction in terms of power while providing control of the type I error.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-015-0225-9) contains supplementary material, which is available to authorized users.

Highlights

Testing gene-gene interaction in genome-wide association studies generally yields lower power than testing marginal association
We address how to perform interaction tests on imputed values given from the imputation approaches and issue of how the performance of proposed tests depends on linkage disequilibrium (LD) among single nucleotide polymorphisms (SNPs)
We propose statistical tests which could be applied on marginally imputed data from external imputation algorithms, a Wald-type test (WTT) and a Waldtype test with constraints (WTTc), that are constructed under the null

Summary

Introduction

Testing gene-gene interaction in genome-wide association studies generally yields lower power than testing marginal association. Meta-analysis that combines different genotyping platforms is one method used to increase power when assessing gene-gene interactions, which requires a test for interaction on untyped SNPs. to date, formal statistical tests for gene-gene interaction on untyped SNPs have not been thoroughly addressed. One way to increase power for detecting gene-gene interaction is by performing a meta-analysis for the gene-gene interaction that combines disparate datasets from studies where genotyping platforms differ in terms of SNP sets [5]. There is a need for better genegene interaction test, and for tests that are developed for interactions between untyped SNPs that are not included in the genotyping platform in a meta-analytic approach

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