BAYESIAN MODEL SEARCH AND MULTILEVEL INFERENCE FOR SNP ASSOCIATION STUDIES.
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.
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Scholars@Duke

Edwin Severin Iversen
Bayesian statistical modeling with application to problems in genetic
epidemiology and cancer research; models for epidemiological risk
assessment, including hierarchical methods for combining related
epidemiological studies; ascertainment corrections for high risk
family data; analysis of high-throughput genomic data sets.

Merlise Clyde
Model uncertainty and choice in prediction and variable selection problems for linear, generalized linear models and multivariate models. Bayesian Model Averaging. Prior distributions for model selection and model averaging. Wavelets and adaptive kernel non-parametric function estimation. Spatial statistics. Experimental design for nonlinear models. Applications in proteomics, bioinformatics, astro-statistics, air pollution and health effects, and environmental sciences.

Scott C. Schmidler
Research Interests:
- Monte Carlo methods; high-dimensional sampling algorithms; Mixing times of Markov chains; MCMC; Sequential Monte Carlo; probabilistic graphical models; Bayesian computation; probabilistic Machine Learning; Computational complexity of statistical inference.
- Computational biology; Protein structure and folding; computational immunology; computational biophysics; statistical physics; computational statistical mechanics; molecular evolution.
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