Exploring the Consequences of Single Nucleotide Polymorphisms in Systemic Lupus Erythematosus; Exploring Racism in Genetic Research as a Socially Constructed Technology

Clemens, Rheyanna, School of Engineering and Applied Science, University of Virginia
Allen, Timothy, EN-Biomed Engr Dept, University of Virginia
Barker, Shannon, EN-Biomed Engr Dept, University of Virginia
Baritaud, Catherine, EN-Engineering and Society, University of Virginia
Berne, Rosalyn, EN-Engineering and Society, University of Virginia

Many genetic diseases, including Systemic Lupus Erythematosus (SLE), affect patients of different ethnicities in a variety of ways, and studying diverse populations in the research of these diseases will not only give scientists a better understanding of the disease pathways, but also help create more efficacious treatments. The technical portion of this project focuses on analyzing gene-variants of SLE patients to determine the implications of genetic pathways in the disease, which can contribute to understanding the etiology of SLE. The Science, Technology, and Society (STS) portion of this project seeks to understand racial discrepancies in genetic research using the Social Construction of Technology (SCOT) theory to extrapolate why diverse populations are excluded from this life-saving research, which can then provide insights into how scientific communities can alleviate this kind of health inequality. Coupling the two projects provides new insights into SLE pathways in ancestral populations that are more likely to be impacted by the disease, as well as assures accuracy and inclusivity in research for diseases like SLE that disproportionately impact minority populations.
The technical report outlines the creation of a gene signature that reflects the structural and functional genetic pathways of SLE. The disease predominantly affects African, Asian, Hispanic, and Native American populations more so than European populations, thus the etiology of SLE remains unknown as very few genomic studies have researched these different ancestral populations. This project specifically focuses on creating a gene-signature for Asian gene-variants of SLE, which determines how genetic risk-variants are expressed to eventually understand the etiology of the disease for treatment targeting.
To begin the creation of the gene signature, Single Nucleotide Polymorphisms (SNPs) and their associated dysregulated genes that correlate with Asian SLE through Genome Wide Association Studies were prioritized based on novelty and hypothesized importance. First, to create the gene signature, coexpression matrices analyzed the enrichment of individual prioritized genes pathways in specific tissue samples from SLE patients. Next, Gene Set Variation Analysis determined if the genes and their cognate pathways are present in patient datasets such as SLE versus control, inactive versus active symptomatic SLE patients, and between different ancestral populations of SLE patients. Finally, linear regression was used to examine the relationships between the genes and different cell types involved in pathways of interest. Ultimately, the results led to new hypotheses about the pathways through which SLE operates, such as overexpression of the genes leading to increased pro-inflammatory pathways and autoreactive immune cells. Future in vitro testing can validate these findings, and may lead to a new awareness of the etiology of SLE.
The STS report addresses genetic research as a socially constructed technological artifact influenced by groups that are included or excluded from feeding back into the development of research. Genetic research breaks barriers in medicine, but social groups and socio-economic factors play a role in shaping the technology in unintentional ways, especially since there are differing interpretations of the uses of genetic research dependent upon which group is involved. Thus, through this framework, the report seeks to address steps that researchers can take to alleviate implicit racism in genetic research.
The SCOT framework establishes that genetic research has not been able to extend beyond the population it currently serves. European ancestry patients make up the vast majority of all Genome Wide Association Studies, which are the primary informants of genetic analysis of disease. While the research does provide for those European ancestry patients with life saving innovations, without inclusion of all populations the research is inaccurate and a cycle of mistreatment and health inequalities continues. Patients, researchers, and socio-economic factors are the groups that play a role in the progression of this technology. Using specific examples of inequalities in medical science, this paper delivers insights as to why diverse ethnicities are excluded from these groups. Lesser healthcare rates combined with a distrust of medical science by minority populations, lack of diversity in scientific communities, and continued discrimination of people of color all contribute to preventing these marginalized groups from feeding back into the technology as researchers or patients. Fostering diversity and transparency in research can lead to more informative results in genetic research.
This research hopes to understand discrepancies in researching genetic diseases like SLE to better understand the role implicit racism has played in the research of these diseases. The goal of the STS deliverable is to eventually understand how and why inaccurate research has led to mistreatment of non-white patients with genetic diseases. In turn, by using more diverse genetic material and creating a gene signature for SLE on the technical side, the research will provide an accurate analysis of new pathways that contribute to disease development.

BS (Bachelor of Science)
Social Construction of Technoogy, Single Nucleotide Polymorphisms, Racism in Genetic Research, Single Nucleotide Polymorphisms, Gene Signature

School of Engineering and Applied Science
Bachelor of Science in Biomedical Engineering
Technical Advisor: Shannon Barker
STS Advisor: Catherine Baritaud
Technical Team Members: Rheyanna Clemens

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