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September 2010 Bayesian model search and multilevel inference for SNP association studies
Melanie A. Wilson, Edwin S. Iversen, Merlise A. Clyde, Scott C. Schmidler, Joellen M. Schildkraut
Ann. Appl. Stat. 4(3): 1342-1364 (September 2010). DOI: 10.1214/09-AOAS322


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.


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Melanie A. Wilson. Edwin S. Iversen. Merlise A. Clyde. Scott C. Schmidler. Joellen M. Schildkraut. "Bayesian model search and multilevel inference for SNP association studies." Ann. Appl. Stat. 4 (3) 1342 - 1364, September 2010.


Published: September 2010
First available in Project Euclid: 18 October 2010

zbMATH: 1202.62166
MathSciNet: MR2758331
Digital Object Identifier: 10.1214/09-AOAS322

Keywords: AIC , Bayes factor , Bayesian model averaging , BIC , Evolutionary Monte Carlo , false discovery , Genetic models , Lasso , model uncertainty , single nucleotide polymorphism , Variable selection

Rights: Copyright © 2010 Institute of Mathematical Statistics

Vol.4 • No. 3 • September 2010
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