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Quantifying the underestimation of relative risks from genome-wide association studies

Abstract:
Genome-wide association studies (GWAS) have identified hundreds of associated loci across many common diseases. Most risk variants identified by GWAS will merely be tags for as-yet-unknown causal variants. It is therefore possible that identification of the causal variant, by fine mapping, will identify alleles with larger effects on genetic risk than those currently estimated from GWAS replication studies. We show that under plausible assumptions, whilst the majority of the per-allele relative risks (RR) estimated from GWAS data will be close to the true risk at the causal variant, some could be considerable underestimates. For example, for an estimated RR in the range 1.2-1.3, there is approximately a 38% chance that it exceeds 1.4 and a 10% chance that it is over 2. We show how these probabilities can vary depending on the true efects associated with low-frequency variants and on the minor allele frequency (MAF) of the most associated SNP. We investigate the consequences of the underestimation of effect sizes for predictions of an individual's disease risk and interpret our results for the design of fine mapping experiments. Although these effects mean that the amount of heritability explained by known GWAS loci is expected to be larger than current projections, this increase is likely to explain a relatively small amout of the so-called "missing" heritability.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1371/journal.pgen.1001337

Authors

More by this author
Institution:
University of Oxford
Division:
MSD
Department:
NDM
Sub department:
Human Genetics Wt Centre
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
NDM
Sub department:
Human Genetics Wt Centre
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author


Publisher:
Public Library of Science
Journal:
PLoS Genetics More from this journal
Volume:
7
Issue:
3
Article number:
e1001337
Publication date:
2011-03-01
Edition:
Publisher's version
DOI:
EISSN:
1553-7404
ISSN:
1553-7390


Language:
English
Keywords:
Subjects:
UUID:
uuid:ed793007-7def-4de3-b8e4-90916fb21eb8
Local pid:
ora:5177
Deposit date:
2011-03-25
ARK identifier:

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