Title: Leveraging Genomic Signatures to Understand Human Disease
Description: Genomics has been transformative to the study of human evolution and disease. With the dropping cost and increased availability of genome sequencing, it is now possible to probe the genetic mediators of human disease at an unprecedented level. My own research grew out of earlier work on the genomic signatures of natural selection in humans. As an undergraduate, I investigated the evidence for recent positive selection in large-scale genomic data, identifying pathways that appear to be targeted by evolution and prioritizing promising candidate variants for functional follow-up. In medical school, I turned my attention to applying tools in genomics and evolution to the study of human disease. In this thesis, I present the results of that work applied to two major contributors to human morbidity and mortality: infectious disease and malignancy.

Motivated by results from earlier work on genomic signals of recent adaptation in a West African population, I investigate genetic resistance to Lassa fever, a viral hemorrhagic disease endemic to West Africa. Focusing on a gene that is critical to Lassa infection and carries a signature of positive selection in the Yoruba population in Nigeria, I demonstrate that the same putative selected haplotype is present in other West African populations, but at different frequencies. Furthermore, I show evidence that the observed differences in frequency show correlation with the geographic distribution of Lassa virus and historical spread of the virus based on viral sequencing data. I test this haplotype for association with Lassa fever and demonstrate evidence of a protective effect. In a genome-wide association study for resistance to Lassa fever, I also identify preliminary genome-wide significant associations and promising variants for replication and follow-up.

In the second part of this thesis, I focus on genomic study of human malignancy. I collaborate with a team to investigate the signatures of mutational forces in the cancer genome. We develop a novel computational framework to extract signatures from large-scale tumor sequencing data. Through this approach, we provide unbiased new estimates for the number and characteristics of the mutational processes that shape the cancer genome. We also investigate these signatures at an unprecedented level of resolution and show how they have the potential to reveal new mechanistic insights into the process of DNA damage repair and mutagenesis in cancer. Finally, we show how these signatures can reveal important clinical insights and identify subsets of tumors within the same tumor type that are dominated by different mutational processes. Our results are in stark contrast to the currently accepted model of mutational signatures in cancer, and have broad implications on our fundamental understanding of cancer biology and the future direction of the field.

Although the diseases investigated here are diverse, the common theme underpinning my approach is to leverage tools in evolution and genomics to shed new light on the most devastating human diseases. Through this approach, I hope to extend our understanding of the biology of these disease processes, with implications on new therapeutic and public health interventions.