GenomeNet

Database: PubMed
Entry: 20206333
LinkDB: 20206333
Original site: 20206333 
PMID:
     20206333
Authors:
     Bhattacharjee S, Wang Z, Ciampa J, Kraft P, Chanock S, Yu K,
     Chatterjee N.
Title:
     Using principal components of genetic variation for robust and powerful detection 
     of gene-gene interactions in case-control and case-only studies.
Journal:
     Am J Hum Genet. 2010 Mar 12;86(3):331-42. doi: 10.1016/j.ajhg.2010.01.026. Epub 
Abstract:
     Many popular methods for exploring gene-gene interactions, including the 
     case-only approach, rely on the key assumption that physically distant loci are 
     in linkage equilibrium in the underlying population. These methods utilize the 
     presence of correlation between unlinked loci in a disease-enriched sample as 
     evidence of interactions among the loci in the etiology of the disease. We use 
     data from the CGEMS case-control genome-wide association study of breast cancer 
     to demonstrate empirically that the case-only and related methods have the 
     potential to create large-scale false positives because of the presence of 
     population stratification (PS) that creates long-range linkage disequilibrium in 
     the genome. We show that the bias can be removed by considering parametric and 
     nonparametric methods that assume gene-gene independence between unlinked loci, 
     not in the entire population, but only conditional on population substructure 
     that can be uncovered based on the principal components of a suitably large panel 
     of PS markers. Applications in the CGEMS study as well as simulated data show 
     that the proposed methods are robust to the presence of population stratification 
     and are yet much more powerful, relative to standard logistic regression methods 
     that are also commonly used as robust alternatives to the case-only type methods.

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