BAYESIAN MODEL SEARCH AND MULTILEVEL INFERENCE FOR SNP ASSOCIATION STUDIES.
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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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Professor of Statistical Science
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.
Research Professor of Statistical Science
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.
Professor Emeritus in Family Medicine and Community Health
Dr. Schildkraut is an epidemiologist whose research includes the molecular epidemiology of ovarian, breast and brain cancers. Dr. Schildkraut's research interests include the study of the interaction between genetic and environmental factors. She is currently involved in a large study of genome wide association and ovarian cancer risk and survival. Some of her work is also focused on particular genetic pathways including the DNA repair and apoptosis pathways. She currently leads a study of
Associate Professor of Statistical Science
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