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BAYESIAN MODEL SEARCH AND MULTILEVEL INFERENCE FOR SNP ASSOCIATION STUDIES.

dc.contributor.author Clyde, Merlise
dc.contributor.author Iversen, Edwin S
dc.contributor.author Schildkraut, JM
dc.contributor.author Schmidler, Scott
dc.contributor.author Wilson, MA
dc.coverage.spatial United States
dc.date.accessioned 2014-03-24T16:43:11Z
dc.date.issued 2010-09-01
dc.identifier http://www.ncbi.nlm.nih.gov/pubmed/21179394
dc.identifier.issn 1932-6157
dc.identifier.uri http://hdl.handle.net/10161/8405
dc.description.abstract Technological advances in genotyping have given rise to hypothesis-based association studies of increasing scope. As a result, the scientific hypotheses addressed by these studies have become more complex and more difficult to address using existing analytic methodologies. Obstacles to analysis include inference in the face of multiple comparisons, complications arising from correlations among the SNPs (single nucleotide polymorphisms), choice of their genetic parametrization and missing data. In this paper we present an efficient Bayesian model search strategy that searches over the space of genetic markers and their genetic parametrization. The resulting method for Multilevel Inference of SNP Associations, MISA, allows computation of multilevel posterior probabilities and Bayes factors at the global, gene and SNP level, with the prior distribution on SNP inclusion in the model providing an intrinsic multiplicity correction. We use simulated data sets to characterize MISA's statistical power, and show that MISA has higher power to detect association than standard procedures. Using data from the North Carolina Ovarian Cancer Study (NCOCS), MISA identifies variants that were not identified by standard methods and have been externally "validated" in independent studies. We examine sensitivity of the NCOCS results to prior choice and method for imputing missing data. MISA is available in an R package on CRAN.
dc.language eng
dc.relation.ispartof Ann Appl Stat
dc.title BAYESIAN MODEL SEARCH AND MULTILEVEL INFERENCE FOR SNP ASSOCIATION STUDIES.
dc.type Journal article
pubs.author-url http://www.ncbi.nlm.nih.gov/pubmed/21179394
pubs.begin-page 1342
pubs.end-page 1364
pubs.issue 3
pubs.organisational-group Clinical Science Departments
pubs.organisational-group Community and Family Medicine
pubs.organisational-group Community and Family Medicine, Prevention Research
pubs.organisational-group Computer Science
pubs.organisational-group Duke
pubs.organisational-group Duke Cancer Institute
pubs.organisational-group Institutes and Centers
pubs.organisational-group School of Medicine
pubs.organisational-group Statistical Science
pubs.organisational-group Trinity College of Arts & Sciences
pubs.publication-status Published
pubs.volume 4


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