Sini Nagpal, Bioinformatics Thesis Defense

In partial fulfillment of the requirements for the degree of
Doctor of Philosophy in Bioinformatics
in the School of Biological Sciences

Sini Nagpal

Defends her thesis:
Genetic and Environmental Factors Influencing the Genomic Prediction of Complex Traits

Monday, April 4, 2022 
10:00am Eastern Time 
In person = EBB 1005 (CHOA Seminar Room)
BlueJeans = https://bluejeans.com/643268096/4445

Thesis advisor: 
Dr. Gregory Gibson, School of Biology, Georgia Institute of Technology. 

Committee Members: 
Dr. I. King Jordan, School of Biology, Georgia Institute of Technology.
Dr. Joseph Lachance, School of Biology, Georgia Institute of Technology.
Dr. Annalise Paaby, School of Biology, Georgia Institute of Technology.
Dr. Jingjing Yang, Department of Human Genetics, Emory University. 


Abstract
Precision medicine is an emerging field in health care which aims to synthesize therapeutics precisely targeted towards a subgroup of individuals identified to be at a higher risk of a disease.  The subgroups of individuals to be targeted are identified using predictive health genomics approaches which utilize genetic, environment, lifestyle, and ancestry information for disease risk stratification. While this field has shown some success in providing personalized treatment advice for Mendelian diseases, the challenge for the future is to extend it to common diseases and complex traits, which are polygenic and influenced by multiple environmental factors. Collectively these represent the largest health and economic burden for society. 

Using polygenic models and integrative genomic approaches, my thesis aims to understand the genetic and environmental factors influencing the genomic prediction of complex traits, with the goal of disease risk assessment and its implication in precision medicine. Specifically, I assessed disease risk using genomic data to study three overarching questions: (1) The role of polygenic risk score-by-environment interactions and how the rapid cultural changes of the modern environment interact with genetic variation and impact disease susceptibility; (2) The role of ancestry-specific genetic effects in the estimation of disease risk; (3) The ability of integrative genomic strategies, namely integrating gene expression profiling with GWAS, to move from prediction of disease onset to prediction of disease progression within cases. 

By analyzing the relationship between the prevalence of disease as a function of polygenic score (PGS) for 10 complex traits and 151 environmental exposures of the UK Biobank, I sought evidence of the process of (de)canalization i.e. the modern environment plays a major role in increasing the genetic variance leading to current surge in the prevalence of common diseases. Secondly, I argued that subtle differences in effect sizes and allele frequencies at the inflammatory bowel disease risk loci between Europeans and Africans, when combined into a PGS, can have a significant impact on the estimation of disease risk. Thus, as the focus moves towards precision medicine and clinical translation, ancestry-matched genetic effects must be used for accurate polygenic risk prediction and to avoid the exacerbation of health disparities. Thirdly, I introduced a transcriptome-wide association study (TWAS) method and its further utility by developing a genetic predictor based on predicted gene expression, called predicted polygenic transcriptional risk score (PPTRS). PPTRS offered advantage over both traditional PGS and transcriptional risk score (TRS), by not only predicting disease onset but also progression to colectomy in ulcerative colitis patients, from the genotypes alone, when gene expression profiling of the relevant tissue is impractical. Although certain challenges remain to addressed, integrating genomic, environment and ancestry information enhances the resolution of disease risk stratification and can help in clinical decision making, therapeutic interventions or lifestyle modifications as we move towards precision medicine.