A meta‐analytic framework for detection of genetic interactions. Issue 7 (15th August 2016)

• Record Type: Journal Article
• Authors: Liu, Yulun; Chen, Yong; Scheet, Paul
• Is Part Of: Genetic epidemiology. Volume 40:Issue 7(2016)
• Page Start: 534
• Page End: 543
• Publication Date: 2016-08-15
• Subjects: case‐control design -- gene‐gene interaction -- heterogeneity -- mega‐analysis -- meta‐analysis; Genetic epidemiology -- Periodicals; Heredity -- Periodicals; Medical geography -- Periodicals; 614
• DOI: 10.1002/gepi.21996
• Languages: English
• ISSNs: 0741-0395

Abstract

ABSTRACT: With varying, but substantial, proportions of heritability remaining unexplained by summaries of single‐SNP genetic variation, there is a demand for methods that extract maximal information from genetic association studies. One source of variation that is difficult to assess is genetic interactions. A major challenge for naive detection methods is the large number of possible combinations, with a requisite need to correct for multiple testing. Assumptions of large marginal effects, to reduce the search space, may be restrictive and miss higher order interactions with modest marginal effects. In this paper, we propose a new procedure for detecting gene‐by‐gene interactions through heterogeneity in estimated low‐order (e.g., marginal) effect sizes by leveraging population structure, or ancestral differences, among studies in which the same phenotypes were measured. We implement this approach in a meta‐analytic framework, which offers numerous advantages, such as robustness and computational efficiency, and is necessary when data‐sharing limitations restrict joint analysis. We effectively apply a dimension reduction procedure that scales to allow searches for higher order interactions. For comparison to our method, which we term phylogenY‐aware Effect‐size Tests for Interactions (YETI), we adapt an existing method that assumes interacting loci will exhibit strong marginal effects to our meta‐analytic framework. As expected, YETI excels when multiple studies are from highly differentiated populations and maintains its superiority in these conditions even when marginal effects are small. When these conditions are less extreme, the advantage of our method wanes. We assess the Type‐I error and power characteristics of complementary approaches to evaluate their strengths and limitations.