Inference Of Natural Selection In Human Populations And Cancers: Testing, Extending, And Complementing Dn/ds-Like Approaches

Abstract

Heritable traits tend to rise or fall in prevalence over time in accordance with their effect on survival and reproduction; this is the law of natural selection, the driving force behind speciation. Natural selection is both a consequence and (in cancer) a cause of disease. The new abundance of sequencing data has spurred the development of computational techniques to infer the strength of selection across a genome. One technique, dN/dS, compares mutation rates at mutation-tolerant synonymous sites with those at nonsynonymous sites to infer selection. This dissertation tests, extends, and complements dN/dS for inferring selection from sequencing data. First, I test whether the genomic community’s understanding of mutational processes is sufficient to use synonymous mutations to set expectations for nonsynonymous mutations. Second, I extend a dN/dS-like approach to the noncoding genome, where dN/dS is otherwise undefined, using conservation data among mammals. Third, I use evolutionary theory to co-develop a new technique for inferring selection within an individual patient’s tumor. Overall, this work advances our ability to infer selection pressure, prioritize disease-related genomic elements, and ultimately identify new therapeutic targets for patients suffering from a broad range of genetically-influenced diseases