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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