A Novel Statistic for Genome-Wide Interaction Analysis

Abstract

Although great progress in genome-wide association studies (GWAS) has been made, the significant SNP associations identified by GWAS account for only a few percent of the genetic variance, leading many to question where and how we can find the missing heritability. There is increasing interest in genome-wide interaction analysis as a possible source of finding heritability unexplained by current GWAS. However, the existing statistics for testing interaction have low power for genome-wide interaction analysis. To meet challenges raised by genome-wide interactional analysis, we have developed a novel statistic for testing interaction between two loci (either linked or unlinked). The null distribution and the type I error rates of the new statistic for testing interaction are validated using simulations. Extensive power studies show that the developed statistic has much higher power to detect interaction than classical logistic regression. The results identified 44 and 211 pairs of SNPs showing significant evidence of interactions with FDR<0.001 and 0.001<FDR<0.003, respectively, which were seen in two independent studies of psoriasis. These included five interacting pairs of SNPs in genes LST1/NCR3, CXCR5/BCL9L, and GLS2, some of which were located in the target sites of miR-324-3p, miR-433, and miR-382, as well as 15 pairs of interacting SNPs that had nonsynonymous substitutions. Our results demonstrated that genome-wide interaction analysis is a valuable tool for finding remaining missing heritability unexplained by the current GWAS, and the developed novel statistic is able to search significant interaction between SNPs across the genome. Real data analysis showed that the results of genome-wide interaction analysis can be replicated in two independent studies.

Conducting Genome-Wide Association Studies: Epistasis Scenarios

“…For the scenarios we tested, our results also support the assessment by Wu et al [9] and Ueki et al [10] that, analytical methods that assume statistical interactions between loci are more powerful than single-loci models.…”

“…Consequently, our experiments specifically investigated scenarios involving low-risk genetic variants, and assessed whether multi-gene scenarios could be a source of the "missing heritability" observed using single-gene models [8]. We also examined the impact of two recent studies that collaborated in the development of novel tests, for measuring interaction between two linked (in epistasis) or unlinked loci [9,10]. These studies purport to have higher power to detect interaction than classical logistic regression models.…”

A Novel Test for Detecting SNP–SNP Interactions in Case-Only Trio Studies

“…The TC test is an extension of the SC test from case-only data to caseonly trio data. However, many other case-only interaction tests have been proposed (Piegorsch et al 1994;Yang et al 1999;Bhattacharjee et al 2010;Wu et al 2010;Ueki and Cordell 2012), and the extension described here may also be applicable to other case-only interaction tests. Ackermann and Beyer (2012) showed that in the absence of main effects the significance of the ImAP test statistic for each marker pair can be properly assessed via a permutation approach.…”

“…Similar to the SC test, the case-only interaction test proposed by Piegorsch et al (1994) compares the distribution of the product of genotypes at a pair of SNPs to the expectation of that product under the assumption of linkage equilibrium between the markers. Alternatively, the case-only interaction test proposed by Yang et al (1999) tests for departures from additivity under a logit model and the case-only interaction tests proposed by Wu et al (2010) and improved in Ueki and Cordell (2012) also consider a logit model. While easily performed, these case-only tests fail to leverage the information available from the parents in the trios and are susceptible to inflation from population structure.…”

“…Like other case-only tests (Piegorsch et al 1994;Yang et al 1999;Cordell 2002;Cordell et al 2004;Wellek and Ziegler 2009;Wu et al 2010;Ueki and Cordell 2012) and some caseonly trio tests (Ackermann and Beyer 2012), the TC test assumes linkage equilibrium between SNPs being tested for interaction. Proximal SNPs in linkage disequilibrium (LD) and SNPs with different population minor allele frequencies tested in structured cohorts will produce biased test statistics leading to false positives.…”