Document Detail

Detecting sample misidentifications in genetic association studies.
MedLine Citation:
PMID:  22611595     Owner:  NLM     Status:  In-Process    
Genetic association studies require that the genotype data from a given person can be correctly linked to the phenotype data from the same person. However, sample misidentification errors sometimes happen, whereby the link becomes invalid for some of the subjects in a study. This can have substantial consequences in terms of power to detect truly associated variants. In family-based studies, Mendelian inconsistencies can be used to detect sample misidentification. Genome-wide association studies (GWAS), however, typically use unrelated individuals, making error detection more problematic. Here we present a method for identifying potential sample misidentifications in GWAS and other genetic association studies building on ideas from forensic sciences. A widely used ad-hoc method for error detection is to check if the sex of an individual matches its X-linked genotype. We generalize this idea to less stringent associations between known genotypes and phenotypes, and show that if several known associations are combined, the power to detect misidentifications increases substantially. Individuals with an unlikely set of phenotypes given their genotypes are flagged as potential errors. We provide analytical and simulation results comparing the odds that the genotype and phenotype are both from the same individual for different numbers of available genotype-p henotype associations and for different information content of the associations. Our method has good sensitivity and specificity with as few as ten moderately informative genotype-phenotype associations. We apply the method to GWAS data from the Danish National Birth Cohort.
Claus T Ekstrøm; Bjarke Feenstra
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Statistical applications in genetics and molecular biology     Volume:  11     ISSN:  1544-6115     ISO Abbreviation:  Stat Appl Genet Mol Biol     Publication Date:  2012  
Date Detail:
Created Date:  2012-05-21     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101176023     Medline TA:  Stat Appl Genet Mol Biol     Country:  United States    
Other Details:
Languages:  eng     Pagination:  Article 13     Citation Subset:  IM    
University of Southern Denmark, Biostatistics, Faculty of Health Sciences, Denmark.
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Grant Support
U01HG004423/HG/NHGRI NIH HHS; U01HG004438/HG/NHGRI NIH HHS; U01HG004446/HG/NHGRI NIH HHS; WT 084762MA//Wellcome Trust

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