The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Random-Effects Model Aimed at Discovering Associations in Meta-Analysis of Genome-wide Association Studies
Open accessAbstract
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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“Compared to the conventional random-effects model, the RE2 model offers greater power under heterogeneity.”
From Meta-analysis identifies five novel loci associated with endometriosis highlighting key genes involved in hormone metabolism (2017) · cites this paper as [17]
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Cites
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Cited by
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Genetic effects on gene expression across human tissues
GTEx Consortium (2017) · Nature · doi:10.1038/nature24277 · Cited by 4,036
Meta-analysis in clinical trials revisited
DerSimonian, R., & Laird, N. (2015) · Contemporary Clinical Trials · doi:10.1016/j.cct.2015.09.002 · Cited by 2,677
Tutorial: a guide to performing polygenic risk score analyses
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Metadata
- Authors
- Han, B., & Eskin, E.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- 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)
- 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
- 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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