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Random-Effects Model Aimed at Discovering Associations in Meta-Analysis of Genome-wide Association Studies

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Han, Buhm, Eskin, Eleazar·2011·The American Journal of Human Genetics

DOI 10.1016/j.ajhg.2011.04.014PMC PMC3146723

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Abstract

Meta-analysis is an increasingly popular tool for combining multiple different genome-wide association studies (GWASs) in a single aggregate analysis in order to identify associations with very small effect sizes. Because the data of a meta-analysis can be heterogeneous, referring to the differences in effect sizes between the collected studies, what is often done in the literature is to apply both the fixed-effects model (FE) under an assumption of the same effect size between studies and the random-effects model (RE) under an assumption of varying effect size between studies. However, surprisingly, RE gives less significant p values than FE at variants that actually show varying effect sizes between studies. This is ironic because RE is designed specifically for the case in which there is heterogeneity. As a result, usually, RE does not discover any associations that FE did not discover. In this paper, we show that the underlying reason for this phenomenon is that RE implicitly assumes a markedly conservative null-hypothesis model, and we present a new random-effects model that relaxes the conservative assumption. Unlike the traditional RE, the new method is shown to achieve higher statistical power than FE when there is heterogeneity, indicating that the new method has practical utility for discovering associations in the meta-analysis of GWASs.

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Structural variants are a major source of gene expression differences in humans and often affect multiple nearby genesPreprint
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Authors: Han, B., & Eskin, E.
Affiliations: University of California, Los Angeles (US)
Corresponding author: Eskin, Eleazar
ORCID: Han, Buhm
Eskin, Eleazar
Venue: The American Journal of Human Genetics
Publisher: Elsevier BV
Published: 2011-05
Volume: 88
Issue: 5
Pages: 586-598
ISSN: 0002-9297 (ISSN-L)
DOI: 10.1016/j.ajhg.2011.04.014
PMC: PMC3146723
OpenAlex: W2042421957
Cited by: 680
References: 55
Citations by year: 2026: 39 · 2025: 53 · 2024: 86 · 2023: 57 · 2022: 68 · 2021: 54 · 2020: 44 · 2019: 52 · 2018: 48 · 2017: 32 · 2016: 40 · 2015: 27 · 2014: 37 · 2013: 21 · 2012: 17
FWCI: 2.62
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 844 d)
Locations: The American Journal of Human Genetics (publishedVersion)
Europe PMC (PubMed Central) (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Seoul National University Open Repository (Seoul National University) (submittedVersion)
Funders: National Institutes of Health (5u01da024417-02)
National Heart, Lung, and Blood Institute (K25HL080079)
National Institute on Drug Abuse (U01-DA024417)
National Institute of Environmental Health Sciences (N01 ES045530)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords: random effects models, genome-wide association study, meta-analysis, heterogeneity, fixed effects model, statistical power, null hypothesis, association discovery
MeSH terms: Crohn Disease, Diabetes Mellitus, Type 2, Humans, Models, Genetic, Models, Statistical, Likelihood Functions, Quantitative Trait Loci, Genome-Wide Association Study

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Random-Effects Model Aimed at Discovering Associations in Meta-Analysis of Genome-wide Association Studies

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