In partial fulfillment of the requirements for the degree of

Doctor of Philosophy in Bioinformatics

in the School of Biological Sciences

Rohini Janivara

Defends her thesis:

Decoding Genetic Heterogeneity and Risk of Prostate Cancer in Africa through Evolutionary Genetics

Monday, October 27, 2025, at 12:00pm EST

Engineered Biosystems Building (EBB), CHOA seminar room EBB 1005

Meeting Link: https://gatech.zoom.us/j/98302052746 

Thesis Advisor:
Dr. Joseph Lachance, School of Biological Sciences, Georgia Institute of Technology

Committee Members:

Dr. Timothy Rebbeck, Vincent L. Gregory, Jr. Professor of Cancer Prevention; Harvard T.H. Chan School of Public Health & Professor of Medical Oncology, Dana-Farber Cancer Institute

Dr. Greg Gibson, School of Biological Sciences, Georgia Institute of Technology

Dr. I. King Jordan, School of Biological Sciences, Georgia Institute of Technology

Dr. Burcu Darst, Public Health Sciences Division, Fred Hutchinson Cancer Center and Department of Epidemiology, University of Washington

Abstract

Prostate cancer remains one of the most common and lethal malignancies among men worldwide, with the highest incidence and mortality observed in men of African ancestry. Despite this disproportionate burden, African populations have been severely underrepresented in genetic studies of disease risk. This thesis addresses that gap by investigating the ancestry-specific genetic architecture of prostate cancer across diverse African populations, integrating approaches from statistical and population genetics within an evolutionary framework.

Analysis of genome-wide and targeted genetic data from individuals across sub-Saharan Africa reveals that prostate cancer susceptibility is shaped by population-specific alleles and heterogeneous genetic architectures that differ markedly from those observed in non-African populations. These findings demonstrate that our understanding of disease risk can be substantially deepened by incorporating ancestry-specific variation. By integrating evolutionary and population-genetic analyses, this work further shows how evolutionary processes such as genetic drift and recent mutation as well as shared ancestry have influenced the distribution of risk alleles. Collectively, the results highlight how genetic background and population structure shape prostate cancer risk and underscore the value of diverse datasets for improving predictive models such as polygenic risk scores.

This thesis contributes to a more inclusive understanding of cancer genetics and affirms the scientific and ethical imperative of studying populations most affected by disease. By coupling genomic discovery with evolutionary insight, it charts a path toward ancestry-informed precision medicine and more equitable approaches to early detection and prevention in prostate cancer.