TY - JOUR
T1 - A meta-analytic framework for detection of genetic interactions
AU - Liu, Yulun
AU - Chen, Yong
AU - Scheet, Paul
N1 - Funding Information:
We wish to thank Chad Huff, Peng Wei, and two anonymous reviewers for helpful comments. This work was partially supported by NIH grants R01 HG 005859 (for P.S., Y.L.), R21 LM 012197 (for Y.C.), and AHRQ grant R03 HS 022900 (for Y.C.). The authors have no conflict of interest to declare.
Publisher Copyright:
© 2016 WILEY PERIODICALS, INC.
PY - 2016/11/1
Y1 - 2016/11/1
N2 - 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.
AB - 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.
KW - case-control design
KW - gene-gene interaction
KW - heterogeneity
KW - mega-analysis
KW - meta-analysis
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U2 - 10.1002/gepi.21996
DO - 10.1002/gepi.21996
M3 - Article
C2 - 27528046
AN - SCOPUS:84991737274
SN - 0741-0395
VL - 40
SP - 534
EP - 543
JO - Genetic Epidemiology
JF - Genetic Epidemiology
IS - 7
ER -