A Groupwise Association Test for Rare Mutations Using a Weighted Sum Statistic
Open accessAbstract
Resequencing is an emerging tool for identification of rare disease-associated mutations. Rare mutations are difficult to tag with SNP genotyping, as genotyping studies are designed to detect common variants. However, studies have shown that genetic heterogeneity is a probable scenario for common diseases, in which multiple rare mutations together explain a large proportion of the genetic basis for the disease. Thus, we propose a weighted-sum method to jointly analyse a group of mutations in order to test for groupwise association with disease status. For example, such a group of mutations may result from resequencing a gene. We compare the proposed weighted-sum method to alternative methods and show that it is powerful for identifying disease-associated genes, both on simulated and Encode data. Using the weighted-sum method, a resequencing study can identify a disease-associated gene with an overall population attributable risk (PAR) of 2%, even when each individual mutation has much lower PAR, using 1,000 to 7,000 affected and unaffected individuals, depending on the underlying genetic model. This study thus demonstrates that resequencing studies can identify important genetic associations, provided that specialised analysis methods, such as the weighted-sum method, are used.
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Madsen and Browning showed that their proposed frequency-weighted statistic is more powerful than either the CAST or CMC methods in a number of settings.
“Madsen and Browning showed that their proposed statistic is more powerful than either the CAST or CMC methods in a number of settings, but more work in this area is needed to clarify the advantages, if any, of each.34”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [34]
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- Authors
- Madsen, B. E., & Browning, S. R.
- Affiliations
- Aarhus University (DK)
- University of Auckland (NZ)
- Corresponding author
- Browning, Sharon R.
- ORCID
- Browning, Sharon R.
- Venue
- PLoS Genetics
- Publisher
- Public Library of Science (PLoS)
- Published
- 2009-02-13
- Volume
- 5
- Issue
- 2
- Pages
- e1000384
- ISSN
- 1553-7390 (ISSN-L)
- 1553-7404
- PMC
- PMC2633048
- OpenAlex
- W2029387902
- Cited by
- 1,113
- References
- 43
- Citations by year
- 2026: 15 · 2025: 29 · 2024: 23 · 2023: 43 · 2022: 45 · 2021: 46 · 2020: 47 · 2019: 70 · 2018: 68 · 2017: 69 · 2016: 91 · 2015: 87 · 2014: 115 · 2013: 114 · 2012: 96
- FWCI
- 48.75
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Genetics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- University of Auckland
- National Institutes of Health
- National Institute of General Medical Sciences (R01GM075091)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- rare mutations, genetic heterogeneity, resequencing, disease-associated genes, population attributable risk, genetic association
- MeSH terms
- DNA Mutational Analysis, Genetic Testing, Genetics, Population, Humans, Mutation, Genetic Variation, Models, Statistical, Genetic Predisposition to Disease
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