Leveraging local ancestry to detect gene-gene interactions in genome-wide data

Abstract

BackgroundAlthough genome-wide association studies have successfully identified thousands of variants associated to complex traits, these variants only explain a small amount of the entire heritability of the trait. Gene-gene interactions have been proposed as a source to explain a significant percentage of the missing heritability. However, detecting gene-gene interactions has proven to be very difficult due to computational and statistical challenges. The vast number of possible interactions that can be tested induces very stringent multiple hypotheses corrections that limit the power of detection. These issues have been mostly highlighted for the identification of pairwise effects and are even more challenging when addressing higher order interaction effects. In this work we explore the use of local ancestry in recently admixed individuals to find signals of gene-gene interaction on human traits and diseases.ResultsWe introduce statistical methods that leverage the correlation between local ancestry and the hidden unknown causal variants to find distant gene-gene interactions. We show that the power of this test increases with the number of causal variants per locus and the degree of differentiation of these variants between the ancestral populations. Overall, our simulations confirm that local ancestry can be used to detect gene-gene interactions, solving the computational bottleneck. When compared to a single nucleotide polymorphism (SNP)-based interaction screening of the same sample size, the power of our test was lower on all settings we considered. However, accounting for the dramatic increase in sample size that can be achieve when genotyping only a set of ancestry informative markers instead of the whole genome, we observe substantial gain in power in several scenarios.ConclusionLocal ancestry-based interaction tests offer a new path to the detection of gene-gene interaction effects. It would be particularly useful in scenarios where multiple differentiated variants at the interacting loci act in a synergistic manner.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-015-0283-z) contains supplementary material, which is available to authorized users.

An ancestry‐based approach for detecting interactions

“…In contrast to the molecular phenotype data, searches for interactions in these phenotypes did not yield any findings passing genome‐wide significance. This is consistent with previous disease studies that have failed to find many replicable interactions in disease studies (Aschard et al., ). In the data here, the lack of any findings may be due to the relatively small sample size or because the effects of the interactions are extremely small (if they exist for covariates correlated with ).…”

“…a). This is in agreement with recent work showing the limited utility of local ancestry by local ancestry interaction test to identify underlying SNP by SNP interaction when genotype data are available (Aschard et al., ). For the larger effect sizes we simulated, we do see power increasing as the difference between ancestral frequencies increases.…”

“…For example, some cis-eQTL SNPs explain up to 50% of the variance of gene expression (Grundberg et al, 2012). However for most phenotypes, known interactions will explain a very small proportion of the phenotypic variance, mainly due to the fact that so few interactions have been identified and replicated (Aschard, Gusev, Brown, & Pasaniuc, 2015).…”

Mixed-model admixture mapping identifies smoking-dependent loci of lung function in African Americans

“…As discussed in our previous work 18 , we expected SNPs in regions of local ancestry-smoking interactions to show multiple-SNP effects on the trait as well as high allelic frequency differentiation at SNPs between ancestral populations. Hence, we performed comparative study of allelic frequencies between the two ancestral populations and fine-mapping analysis to assess the potential presence of multiple causal variants.…”

“…Recently, we found that the correlation between local ancestry and untyped causal variants can be leveraged to detect distant gene-gene interactions in admixed populations through local ancestry-local ancestry screening 18 . That work also demonstrated that the power of such admixture mapping increases with the number of causal variants within local ancestry tested and with the degree of differentiation of variants between the ancestral populations.…”