The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Estimation of the multiple testing burden for genomewide association studies of nearly all common variants
Abstract
Genomewide association studies are an exciting strategy in genetics, recently becoming feasible and harvesting many novel genes linked to multiple phenotypes. Determining the significance of results in the face of testing a genomewide set of multiple hypotheses, most of which are producing noisy, null‐distributed association signals, presents a challenge to the wide community of association researchers. Rather than each study engaging in independent evaluation of significance standards, we have undertaken the task of developing such standards for genomewide significance, based on data collected by the International Haplotype Map Consortium. We report an estimated testing burden of a million independent tests genomewide in Europeans, and twice that number in Africans. We further identify the sensitivity of the testing burden to the required significance level, with implications to staged design of association studies. Genet. Epidemiol. 2008. © 2008 Wiley‐Liss, Inc.
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In the citing paper
“These validated the HLA association but showed no significant linkage to loci outside the MHC.”
From Genome‐wide association studies of multiple sclerosis (2018) · cites this paper as [28]
In the citing paper
“The currently acceptable standard is a significance level of P < 5 × 10−8, which is equivalent to P < 0.05 after Bonferroni correction for the number of independent tests in the genome given linkage disequilibrium between common variants.28”
From Genome‐wide association studies of multiple sclerosis (2018) · cites this paper as [28]
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“For the non-trio cohorts, each regression included derived principal components as covariates [29].”
In the citing paper
“Simulations indicated that a dense genome-wide scan of common variants involves the equivalent of ~1 million independent hypotheses (64). A significance level of P = 5 × 10−8 thus represents a finding expected by chance once in 20 GWASs.”
From Genetic Mapping in Human Disease (2008) · cites this paper as [64]
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- Pe'er I, 2006, Biases and reconciliation in estimations of linkage disequilibriumin the human genome, Am J Hum Genet, 73
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Genome-wide association studies
Uffelmann, E., Huang, Q. Q., Munung, N. S., de Vries, J., Okada, Y., Martin, A. R., Martin, H. C., Lappalainen, T., & Posthuma, D. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00056-9 · Cited by 1,849
An atlas of genetic influences on human blood metabolites
The Multiple Tissue Human Expression Resource (MuTHER) Consortium, Shin, S.-Y., Fauman, E. B., Petersen, A.-K., Krumsiek, J., Santos, R., Huang, J., Arnold, M., Erte, I., Forgetta, V., et al. (2014) · Nature Genetics · doi:10.1038/ng.2982 · Cited by 1,515
Metadata
- Authors
- Pe'er, I., Yelensky, R., Altshuler, D., & Daly, M. J.
- Affiliations
- Massachusetts General Hospital (US)
- Broad Institute (US)
- Harvard University (US)
- Corresponding author
- Daly, Mark J.
- Venue
- Genetic Epidemiology
- Publisher
- Wiley
- Published
- 2008-03-17
- Volume
- 32
- Issue
- 4
- Pages
- 381-385
- ISSN
- 0741-0395 (ISSN-L)
- 1098-2272
- OpenAlex
- W2163184920
- Cited by
- 783
- References
- 24
- Citations by year
- 2026: 22 · 2025: 26 · 2024: 31 · 2023: 38 · 2022: 30 · 2021: 50 · 2020: 47 · 2019: 47 · 2018: 53 · 2017: 52 · 2016: 39 · 2015: 30 · 2014: 44 · 2013: 38 · 2012: 56
- FWCI
- 34.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Genetic Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust
- National Institutes of Health
- National Cancer Institute (U54CA121852)
- National Institute of Diabetes and Digestive and Kidney Diseases (F32DK070527)
- 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 diversity and population structure
- Keywords
- genome-wide association study, common genetic variants, Europeans, Africans
- MeSH terms
- Biometry, Epidemiologic Methods, Genetic Techniques, Humans, Models, Genetic, Genetic Variation, Polymorphism, Single Nucleotide, Databases, Genetic, Black People, White People
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