The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Practical aspects of imputation-driven meta-analysis of genome-wide association studies
Open accessAbstract
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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PLINK: a tool set for whole-genome association and population-based linkage analyses.
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Self-citationPrincipal components analysis corrects for stratification in genome-wide association studies
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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).”
BackgroundGenome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels
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METAL: fast and efficient meta-analysis of genomewide association scans
Willer, C. J., Li, Y., & Abecasis, G. R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq340 · Cited by 5,205
(2013) · The Lancet · doi:10.1016/s0140-6736(12)62129-1 · Cited by 3,010
Jansen, I. E., Savage, J. E., Watanabe, K., Bryois, J., Williams, D. M., Steinberg, S., Sealock, J., Karlsson, I. K., Hägg, S., Athanasiu, L., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0311-9 · Cited by 2,277
International Parkinson's Disease Genomics Consortium (IPDGC), Nalls, M. A., Parkinson's Study Group (PSG) Parkinson's Research: The Organized GENetics Initiative (PROGENI), 23andMe, GenePD, NeuroGenetics Research Consortium (NGRC), Hussman Institute of Human Genomics (HIHG), Investigator, T. A. J. D., (CHARGE), C. F. H. A. A. R. I. G. E., North American Brain Expression Consortium (NABEC), et al. (2014) · Nature Genetics · doi:10.1038/ng.3043 · Cited by 1,895
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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
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