Department of Statistics, UCLA

Novel technologies, such as the 10k Affymetrix genotyping array, allow scoring of genetic polymorphisms at a very high density across the genome. This allows researchers to conduct traditional inquiries at an unprecedented resolution, while simutaneously motivates novel types of analysis, aimed at exploiting the increased information contained in these datasets. We consider how genotypes of cancer cell lines can be used to reconstruct genomic loss events and map putative tumor suppressor genes (TSG). Using a hidden Markov model framework, we adapt a previously described model for genomic instability in cancers to the current data structure. Simulations indicate that our procedure can be powerful and accurate and initial application to real data leads to encouraging results.