A Hierarchical Bayesian Modeling Approach To Genetical Genomics

The proposed method re ects my continuing interest in the development of novel Bayesian methodologies for the analysis of data that arise in genomics. Novel methodological questions are now being generated in Bioinformatics and require the integration of dierent concepts, methods, tools and data types. The proposed modeling approach is general and can be readily applied to high-throughput data of dierent types, and to data from dierent cancers and diseases. A single mutation is not enough to trigger cancer, as this is the result of a number of complex biological events. Thus, discovering amplication of oncogenes or deletion of tumor suppressors are important steps in elucidating tumor genesis. Delineating the association between gene expression and CGH data is particularly useful in cancer studies, where copy number aberrations are widespread, due to genomic instability. This project focuses on the development of an innovative statistical model that integrates gene expression and genetics data. Our approach explicit models the relationship between these two types of data, allowing for the quantication of the eect of the genetic aberrations on the gene expression levels. The proposed model assumes that gene expression levels are aected by copy number aberrations in corresponding and adjacent segments and also allows for the possibility that changes in gene expression may be due to extraneous causes other than copy number aberrations. It allows, at the same time, to model array CGH data to learn about genome-wide changes in copy number considering information taken from all the samples simultaneously.