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Genet Epidemiol. 2016 Nov;40(7):534-543. doi: 10.1002/gepi.21996. Epub 2016 Aug 15.

A meta-analytic framework for detection of genetic interactions.

Liu Y1,2, Chen Y3, Scheet P4,5,6.

Author information

1
Division of Biostatistics, School of Public Health, The University of Texas, Houston, TX 77030, USA.
2
Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
3
Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
4
Division of Biostatistics, School of Public Health, The University of Texas, Houston, TX 77030, USA. pscheet@alum.wustl.edu.
5
Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. pscheet@alum.wustl.edu.
6
Graduate School of Biomedical Sciences, The University of Texas, Houston, TX 77030, USA. pscheet@alum.wustl.edu.

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.

KEYWORDS:

case-control design; gene-gene interaction; heterogeneity; mega-analysis; meta-analysis

PMID:
27528046
PMCID:
PMC5081230
DOI:
10.1002/gepi.21996
[Indexed for MEDLINE]
Free PMC Article

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