Bayesian Models for Integrative Genomics
Novel methodological questions are being generated in the biological sciences, requiring the integration of different concepts, methods, tools and data types. Bayesian methods that employ variable selection have been particularly successful for genomic applications, as they allow to handle situations where the amount of measured variables can be much greater than the number of observations. In this talk I will focus on models that integrate experimental data from different platforms together with prior knowledge. I will look in particular at hierarchical models that relate genotype data to mRNAs, for the selection of the markers that affect the gene expression. Specific sequence/structure information will be incorporated into the prior probability models. I will also present a Bayesian hierarchical modeling approach for imaging genetics, where the interest lies in linking brain connectivity across multiple individuals to their genetic information. All modeling settings employ variable selection techniques and prior constructions that cleverly incorporate biological knowledge about structural dependencies among the variables. Applications will be to data from cancer studies and from an imaging genetics study on schizophrenia.
Brief bio: Dr. Vannucci is currently Professor and Chair of Statistics at Rice University, Houston, TX. She is also an adjunct faculty member of the UT M.D. Anderson Cancer Center, TX, and the Rice Director of the Interinstitutional Graduate Program in Biostatistics. Dr. Vannucci received the Laurea (B.S.) degree in Mathematics in 1992 and the Ph.D. degree in Statistics in 1996, both from the University of Florence, Italy. Prior to joining Rice in 2007, she held positions at the University of Kent at Canterbury, UK, and at Texas A&M University, TX. Dr. Vannucci's research focuses on the theory and practice of Bayesian variable selection techniques and on the development of statistical models for the analysis of high-throughput genomic data. She has written over 98 technical papers and has co-edited the books "Bayesian Inference for Gene Expression and Proteomics" and "Advances in Statistical Bioinformatics: Models and Integrative Inference for High-Throughput Data". Dr. Vannucci currently serves as the Editor-in-Chief for the journal Bayesian Analysis.