Document Detail


Estimating causal effects of genetic risk variants for breast cancer using marker data from bilateral and familial cases.
MedLine Citation:
PMID:  22028405     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
BACKGROUND: Cases with a family history are enriched for genetic risk variants, and the power of association studies can be improved by selecting cases with a family history of disease. However, in recent genome-wide association scans utilizing familial sampling, the excess relative risk for familial cases is less than predicted when compared with unselected cases. This can be explained by incomplete linkage disequilibrium between the tested marker and the underlying causal variant.
METHODS: We show that the allele frequency and effect size of the underlying causal variant can be estimated by combining marker data from studies that ascertain cases based on different family histories. This allows us to learn about the genetic architecture of a complex trait, without having identified any causal variants. We consider several validated common marker alleles for breast cancer, using our own study of high risk, predominantly bilateral cases, cases preferentially selected to have at least two affected first- or second-degree relatives, and published estimates of relative risk from standard case-control studies.
RESULTS: To obtain realistic estimates and to accommodate some prior beliefs, we use Bayesian estimation to infer that the causal variants are probably common, with minor allele frequency >5%, and have small effects, with relative risk around 1.2.
CONCLUSION: These results strongly support the common disease common variant hypothesis for these specific loci associated with breast cancer. Impact: Our results agree with recent assertions that synthetic associations of rare variants are unlikely to account for most associations seen in genome-wide studies.
Authors:
Frank Dudbridge; Olivia Fletcher; Kate Walker; Nichola Johnson; Nick Orr; Isabel Dos Santos Silva; Julian Peto
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2011-10-25
Journal Detail:
Title:  Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology     Volume:  21     ISSN:  1538-7755     ISO Abbreviation:  Cancer Epidemiol. Biomarkers Prev.     Publication Date:  2012 Feb 
Date Detail:
Created Date:  2012-02-08     Completed Date:  2012-08-23     Revised Date:  2013-06-27    
Medline Journal Info:
Nlm Unique ID:  9200608     Medline TA:  Cancer Epidemiol Biomarkers Prev     Country:  United States    
Other Details:
Languages:  eng     Pagination:  262-72     Citation Subset:  IM    
Copyright Information:
©2011 AACR.
Affiliation:
Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, United Kingdom. frank.dudbridge@lshtm.ac.uk
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MeSH Terms
Descriptor/Qualifier:
Bayes Theorem
Breast Neoplasms / epidemiology*,  genetics*
Case-Control Studies
Female
Gene Frequency
Genetic Predisposition to Disease
Genotype
Great Britain / epidemiology
Humans
Likelihood Functions
Polymorphism, Single Nucleotide
Risk Factors
Tumor Markers, Biological / genetics*
Grant Support
ID/Acronym/Agency:
A5660//Cancer Research UK; C1178/A3947//Cancer Research UK; C150/A5660//Cancer Research UK; G1000718//Medical Research Council; G1000718//Medical Research Council; G1000718(94927)//Medical Research Council
Chemical
Reg. No./Substance:
0/Tumor Markers, Biological
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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