Thesis Portfolio: Characterization of Systemic Lupus Erythematosus Through Cell Death Pathway and Analysis; Examining Privacy and Consent in Contemporary Geonomics Data Collection and Usage

My technical project is a computational project studying major cell death pathways and their relationship to systemic lupus erythematosus (SLE). SLE is a lifelong autoimmune disease that disproportionately affects women and minority groups. SLE typically manifests as chronic inflammation. In this project, four major cell death pathways were examined in the context of SLE: apoptosis, pyroptosis, necroptosis, and ferroptosis. Each pathway has previously been characterized by the generation of a unique “gene signature”. Thus, the goal of this project is to understand how each cell death pathway contributes to SLE pathogenesis, and how the individual and combined effect of these pathways influences disease progression and/or the development of clinical manifestations. This project will further our current understanding of the pathogenesis of SLE, especially in how it relates to demographics and clinical features. This project accomplishes this goal by using gene set variation analysis (GSVA) to study gene signatures in biological samples and clinical metadata. Additionally, statistical and machine learning methods were employed to produce a model of the relationship between cell death and factors related to the pathophysiology of SLE. This will aid in the development of future therapeutic strategies and potentially identify “druggable” targets in SLE patients. Downstream, this project may lead to the development of therapeutics designed to target specific genes rather than simply minimize symptoms.
My STS project explores the ethics behind the issues of privacy and consent in genomic research and data collection. Genetic data collected by healthcare systems, academic institutions, and private corporations are all regulated differently on state, federal, and internal levels. However, as dictated by the social construction of technology theory (SCOT), genetic data collection systems change based on feedback from donors and consumers. In my thesis, I support the SCOT argument using several case studies. Instances such as using data from GEDMatch to catch the Golden State Killer and the Havasupai Tribe Lawsuits demonstrate how social feedback shapes how genetic technology is used.