Inference Of Natural Selection In Human Populations And Cancers

Yale University

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Yale Medicine Thesis Digital Library School of Medicine

January 2020

Inference Of Natural Selection In Human Populations And

Cancers: Testing, Extending, And Complementing Dn/ds-Like

Approaches

William Meyerson

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Recommended Citation

Meyerson, William, "Inference Of Natural Selection In Human Populations And Cancers: Testing,

Extending, And Complementing Dn/ds-Like Approaches" (2020). Yale Medicine Thesis Digital Library.

3931.

https://elischolar.library.yale.edu/ymtdl/3931

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Inference of Natural Selection in Human Populations and Cancers: Testing, Extending, and

Complementing dN/dS-like Approaches

A Thesis Submitted to the Yale University School of Medicine in Partial Fulfillment of the

Requirements for the Degree of Doctor of Medicine

by

William Ulysses Meyerson

2020

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.