In partial fulfillment of the requirements for the degree of
Doctor of Philosophy in Bioinformatics
in the School of School of Biological Sciences
Shivam Sharma
Defends his thesis:
Leveraging genetic ancestry to decipher complex health phenotypes
Wednesday, April 16th, 2025
12PM EST
Engineered Biosystems Building (EBB),
CHOA seminar room EBB 1005
Zoom Link = https://gatech.zoom.us/j/93123356644
Thesis Advisor:
Dr. I. King Jordan, School of Biological Sciences, Georgia Institute of Technology
Committee Members:
Dr. Greg Gibson, School of Biological Sciences, Georgia Institute of Technology
Dr. Joseph Lachance, School of Biological Sciences, Georgia Institute of Technology
Dr. Annalise Paaby, School of Biological Sciences, Georgia Institute of Technology
Dr. Leonardo Mariño Ramírez, Division of Intramural Research National Institute on Minority Health and Health Disparities
Abstract:
Understanding individual health risks is central to achieving long and healthy lives. These risks stem from both inherited genetic variation and environmental exposures—forces that are deeply intertwined. While environmental risks can often be modified, understanding genetic predispositions is essential for informing effective lifestyle and treatment strategies. Although numerous studies have uncovered genetic contributors to complex traits, most have focused on European populations, limiting their applicability to admixed or non-European groups. This thesis aims to identify ancestry-specific genetic factors that influence health phenotypes, using large-scale, diverse biomedical datasets to improve our understanding of complex phenotypes and to advance equitable precision medicine.
To provide a foundation for such efforts, the thesis begins by characterizing genetic ancestry within a large, diverse cohort using genomic clustering methods. Genetic ancestry is defined both as a continuous measure and through population-informed clusters at continental and subcontinental levels. Patterns of ancestry are analyzed across geography and time, offering a high-resolution, objective framework for designing ancestry-aware studies further in this thesis to interpret genetic associations in diverse populations.
Building on this foundation, ancestry-specific differences are investigated in a widely used clinical biomarker, serum creatinine (Scr), which is central to estimating kidney function. Scr levels were found to increase with African genetic ancestry in healthy individuals, independent of socioeconomic status, suggesting a biological rather than social basis for this disparity. Genome-wide analyses further revealed ancestry-specific loci—particularly at the GATM gene—associated with Scr variation, offering insights into creatine biosynthesis and its contribution to elevated Scr levels. These findings underscore the limitations of race-based clinical corrections and highlight the need for ancestry-informed approaches to improve accuracy in kidney function assessment.
In the context of pharmacogenomics, the thesis explores how variation in pharmacogenes—genes involved in drug metabolism, absorption, and excretion—can directly affect treatment outcomes. Known pharmacogenomic variants differ across ancestry groups and partially reflect underlying population structure. Beyond these established variants, exome sequencing reveals a landscape of potentially novel pharmacogenomic variants, some of which may carry functional consequences similar to known risk alleles. By integrating these data with electronic health records, the analysis quantifies the risk of contraindicated prescriptions and assesses the burden of pharmacogenomic risk across genetically diverse individuals.
Together, these studies demonstrate how genetic ancestry can be used as a powerful, objective tool to uncover population-specific genetic variation in complex health traits, including biomarkers and drug response. The findings highlight the critical need for greater diversity in genomic research and provide a path forward for building more equitable and precise healthcare systems.