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Genet Epidemiol. 2020 Jan;44(1):52-66. doi: 10.1002/gepi.22262. Epub 2019 Oct 4.

Embracing study heterogeneity for finding genetic interactions in large-scale research consortia.

Author information

1
Department of Population and Data Sciences, The University of Texas Southwestern Medical Center, Dallas, Texas.
2
Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania.
3
Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania.
4
Department of Statistics, University of Connecticut, Storrs, Connecticut.
5
Department of Pharmacology, University of Pennsylvania, Philadelphia, Pennsylvania.
6
Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas.

Abstract

Genetic interactions have been recognized as a potentially important contributor to the heritability of complex diseases. Nevertheless, due to small effect sizes and stringent multiple-testing correction, identifying genetic interactions in complex diseases is particularly challenging. To address the above challenges, many genomic research initiatives collaborate to form large-scale consortia and develop open access to enable sharing of genome-wide association study (GWAS) data. Despite the perceived benefits of data sharing from large consortia, a number of practical issues have arisen, such as privacy concerns on individual genomic information and heterogeneous data sources from distributed GWAS databases. In the context of large consortia, we demonstrate that the heterogeneously appearing marginal effects over distributed GWAS databases can offer new insights into genetic interactions for which conventional methods have had limited success. In this paper, we develop a novel two-stage testing procedure, named phylogenY-based effect-size tests for interactions using first 2 moments (YETI2), to detect genetic interactions through both pooled marginal effects, in terms of averaging site-specific marginal effects, and heterogeneity in marginal effects across sites, using a meta-analytic framework. YETI2 can not only be applied to large consortia without shared personal information but also can be used to leverage underlying heterogeneity in marginal effects to prioritize potential genetic interactions. We investigate the performance of YETI2 through simulation studies and apply YETI2 to bladder cancer data from dbGaP.

KEYWORDS:

genetic interaction; heterogeneity in marginal effects; meta-analysis; privacy-preserving algorithm; two-stage testing procedure

PMID:
31583758
PMCID:
PMC6980207
[Available on 2021-01-01]
DOI:
10.1002/gepi.22262

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