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Practical aspects of imputation-driven meta-analysis of genome-wide association studies
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Motivated by the overwhelming success of genome-wide association studies, droves of researchers are working vigorously to exchange and to combine genetic data to expediently discover genetic risk factors for common human traits. The primary tools that fuel these new efforts are imputation, allowing researchers who have collected data on a diversity of genotype platforms to share data in a uniformly exchangeable format, and meta-analysis for pooling statistical support for a genotype-phenotype association. As many groups are forming collaborations to engage in these efforts, this review collects a series of guidelines, practical detail and learned experiences from a variety of individuals who have contributed to the subject.
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“This score is commonly applied as a quality filter for imputed genotypes and is equivalent to the RSQR_HAT value by MACH and the information content (INFO) measure by PLINK (44).”
From Common variants near FRK/COL10A1 and VEGFA are associated with advanced age-related macular degeneration (2011) · cites this paper as [44]
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“The genomic inflation factor l is defined as the ratio of the median of the empirically observed distribution of the test statistic to the expected median, thus quantifying the extent of the bulk inflation and the excess false positive rate (32).”
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Metadata
- Authors
- de Bakker, P. I. W., Ferreira, M. A. R., Jia, X., Neale, B. M., Raychaudhuri, S., & Voight, B. F.
- Affiliations
- Broad Institute (US)
- Harvard–MIT Division of Health Sciences and Technology (US)
- Venue
- Human Molecular Genetics
- Publisher
- Oxford University Press (OUP)
- Published
- 2008-10-15
- Volume
- 17
- Issue
- R2
- Pages
- R122-R128
- ISSN
- 0964-6906 (ISSN-L)
- 1460-2083
- PMC
- PMC2782358
- OpenAlex
- W2119067354
- Cited by
- 531
- References
- 44
- Citations by year
- 2026: 7 · 2025: 7 · 2024: 10 · 2023: 16 · 2022: 14 · 2021: 28 · 2020: 6 · 2019: 19 · 2018: 27 · 2017: 26 · 2016: 32 · 2015: 39 · 2014: 42 · 2013: 50 · 2012: 52
- FWCI
- 4.84
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Human Molecular Genetics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion)
- Funders
- National Institutes of Health
- U.S. Public Health Service (K08-KAR055688A)
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (K08AR055688, T32AR007530)
- 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
- imputation, genome-wide association study, meta-analysis, genotype-phenotype correlation, data exchange, genetic risk factors
- MeSH terms
- Data Interpretation, Statistical, Humans, Phenotype, Meta-Analysis as Topic, Genome, Human, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Genome-Wide Association Study
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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