Statistical power and utility of meta-analysis methods for cross-phenotype genome-wide association studies
Open accessAbstract
Advances in recent genome wide association studies (GWAS) suggest that pleiotropic effects on human complex traits are widespread. A number of classic and recent meta-analysis methods have been used to identify genetic loci with pleiotropic effects, but the overall performance of these methods is not well understood. In this work, we use extensive simulations and case studies of GWAS datasets to investigate the power and type-I error rates of ten meta-analysis methods. We specifically focus on three conditions commonly encountered in the studies of multiple traits: (1) extensive heterogeneity of genetic effects; (2) characterization of trait-specific association; and (3) inflated correlation of GWAS due to overlapping samples. Although the statistical power is highly variable under distinct study conditions, we found the superior power of several methods under diverse heterogeneity. In particular, classic fixed-effects model showed surprisingly good performance when a variant is associated with more than a half of study traits. As the number of traits with null effects increases, ASSET performed the best along with competitive specificity and sensitivity. With opposite directional effects, CPASSOC featured the first-rate power. However, caution is advised when using CPASSOC for studying genetically correlated traits with overlapping samples. We conclude with a discussion of unresolved issues and directions for future research.
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The study has previously shown that this method outperforms a range of alternatives for detecting pleiotropy under various settings.
“However, we have previously shown that this method outperforms a range of alternatives for detecting pleiotropy under various settings (Zhu et al., 2018).”
From Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders (2019) · cites this paper as Zhu et al., 2018
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- Authors
- Zhu, Z., Anttila, V., Smoller, J. W., & Lee, P. H.
- Affiliations
- Harvard University (US)
- Broad Institute (US)
- Corresponding author
- Lee, Phil H.
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-03-01
- Volume
- 13
- Issue
- 3
- Pages
- e0193256
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC5832233
- OpenAlex
- W2793560365
- Cited by
- 47
- References
- 44
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 3 · 2023: 3 · 2022: 4 · 2021: 10 · 2020: 3 · 2019: 9 · 2018: 2
- FWCI
- 4.59
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- National Institute of Mental Health (R00MH101367, R01MH085542, R01MH085545, K24MH094614)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- pleiotropy, meta-analysis methodology, statistical power, type I error rate, genetic heterogeneity, overlapping samples, genetic correlation, fixed effects model, assets, complex traits
- MeSH terms
- Humans, Phenotype, Meta-Analysis as Topic, Models, Statistical, Genetic Heterogeneity, Genome-Wide Association Study, Genetic Loci, Genetic Pleiotropy
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