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Cited by 1,113Genetic Associations and EpidemiologyGenomics and Rare DiseasesGenetic Mapping and Diversity in Plants and Animals

The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.

A Groupwise Association Test for Rare Mutations Using a Weighted Sum Statistic

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Madsen, Bo Eskerod, Browning, Sharon R.·2009·PLoS Genetics
DOI 10.1371/journal.pgen.1000384PMC PMC2633048
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Abstract

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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Figures

Figure 1 Genetic models. Model descriptions and examples of predisposing genotypes are shown for the genetic models used. Lines symbolise haplotypes and dots symbolise disease-risk mutations.
Figure 1 Genetic models. Model descriptions and examples of predisposing genotypes are shown for the genetic models used. Lines symbolise haplotypes and dots symbolise disease-risk mutations.CC BY · PubMed Central
Figure 2 Power versus PAR of group. The power of the investigated methods is shown for different levels of group-PAR. The power simulations were performed using nA = nU = 1000 individuals, 50 D-variants, 50 N-variants and pM = 10%.
Figure 2 Power versus PAR of group. The power of the investigated methods is shown for different levels of group-PAR. The power simulations were performed using nA = nU = 1000 individuals, 50 D-variants, 50 N-variants and pM = 10%.CC BY · PubMed Central
Figure 3 Power versus number of D-variants. The power of the investigated methods is shown for different number of D-variants (disease-risk contributing variants). The power simulations were performed using nA = nU = 1000 individuals, 50% D-variants, group PAR of 10% and pM = 10%. Note that the jump in the power for the CMC method under the Recessive-set model occurs because a low number of variants yields a high allele-frequency of each variant, and the variants are hence not grouped by the CMC method.
Figure 3 Power versus number of D-variants. The power of the investigated methods is shown for different number of D-variants (disease-risk contributing variants). The power simulations were performed using nA = nU = 1000 individuals, 50% D-variants, group PAR of 10% and pM = 10%. Note that the jump in the power for the CMC method under the Recessive-set model occurs because a low number of variants yields a high allele-frequency of each variant, and the variants are hence not grouped by the CMC method.CC BY · PubMed Central
Figure 4 Power versus proportion of D-variants. The power of the investigated methods is shown for different proportions of D-variants (disease-risk contributing variants). The power simulations were performed using nA = nU = 1000 individuals, 50 D-variants, group PAR of 10% and pM = 10%.
Figure 4 Power versus proportion of D-variants. The power of the investigated methods is shown for different proportions of D-variants (disease-risk contributing variants). The power simulations were performed using nA = nU = 1000 individuals, 50 D-variants, group PAR of 10% and pM = 10%.CC BY · PubMed Central

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Metadata

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
DOI
10.1371/journal.pgen.1000384
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
Licence
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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