A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms
Open accessAbstract
Multiple testing is a challenging issue in genetic association studies using large numbers of single nucleotide polymorphism (SNP) markers, many of which exhibit linkage disequilibrium (LD). Failure to adjust for multiple testing appropriately may produce excessive false positives or overlook true positive signals. The Bonferroni method of adjusting for multiple comparisons is easy to compute, but is well known to be conservative in the presence of LD. On the other hand, permutation‐based corrections can correctly account for LD among SNPs, but are computationally intensive. In this work, we propose a new multiple testing correction method for association studies using SNP markers. We show that it is simple, fast and more accurate than the recently developed methods and is comparable to permutation‐based corrections using both simulated and real data. We also demonstrate how it might be used in whole‐genome association studies to control type I error. The efficiency and accuracy of the proposed method make it an attractive choice for multiple testing adjustment when there is high intermarker LD in the SNP data set. Genet. Epidemiol . 2008. © 2008 Wiley‐Liss, Inc.
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To provide more conservative results accounting for the number of tested proteins, the study used a strategy described by Gao et al. and Kettunen et al. that accommodates the correlation between protein levels.
“To also provide the more conservative results accounting for the number of tested proteins, we used a strategy described by Gao et al.35 and Kettunen et al.36, which accommodates the correlation between protein levels.”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as Gao et al. [35]
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- Authors
- Gao, X., Starmer, J., & Martin, E. R.
- Affiliations
- University of Miami (US)
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Gao, Xiaoyi
- Venue
- Genetic Epidemiology
- Publisher
- Wiley
- Published
- 2008-02-12
- Volume
- 32
- Issue
- 4
- Pages
- 361-369
- ISSN
- 0741-0395 (ISSN-L)
- 1098-2272
- OpenAlex
- W2105673666
- Cited by
- 849
- References
- 4
- Citations by year
- 2026: 55 · 2025: 63 · 2024: 75 · 2023: 57 · 2022: 58 · 2021: 92 · 2020: 50 · 2019: 43 · 2018: 38 · 2017: 48 · 2016: 43 · 2015: 39 · 2014: 43 · 2013: 36 · 2012: 32
- FWCI
- 13.19
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Genetic Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (7r01ag019757-06)
- National Institute on Aging (AG20135, R01AG020135, AG019757)
- National Institute of Neurological Disorders and Stroke (NS3 9764, P50NS039764)
- National Institute of Environmental Health Sciences (T32ES007126, ES 007126)
- 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
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- multiple testing correction, genetic association studies, single nucleotide polymorphisms, linkage disequilibrium, Bonferroni correction, type I error control, whole-genome association studies, false positives
- MeSH terms
- Alzheimer Disease, Biometry, Data Interpretation, Statistical, Epidemiologic Methods, Genetic Techniques, Humans, Models, Genetic, Linkage Disequilibrium, Polymorphism, Single Nucleotide, Principal Component Analysis, Databases, Genetic
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