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Publication Date
2023-5
First Advisor
Andrew Dahl
Second Advisor
Rob Dorit
Document Type
Honors Project
Degree Name
Bachelor of Arts
Keywords
Polygenic Scores, Cross Population, Population Structure
Abstract
A polygenic score (PGS) quantifies an individual’s estimated risk of developing a disorder or complex trait based on their genome-wide data. Because PGS are based on Genome-Wide Association Study (GWAS) summary statistics, they are particularly vulnerable to the effects of population stratification and biased sampling. To date, the genetic data used in most GWAS is derived primarily from the White, European population. When PGS scores derived from this non-representative sample of human variation are extrapolated to other populations, their utility decreases markedly. Given the growing relevance of PGS in genetics research—and soon in health settings—this bias must be explored and mitigated. Here, we propose the “Hairpin” method which utilizes a measure of population structure (even-odd chromosome PGS correlation) to identify at which point the PGS model fails due to population stratification and becomes overfit to a population. This failure point, in turn, identifies the cutoff value that we recommend should be used when building PGS in order to increase their portability and facilitate their extrapolation to other populations.
Rights
©2023 Shevaughn Holness. Access limited to the Smith College community and other researchers while on campus. Smith College community members also may access from off-campus using a Smith College log-in. Other off-campus researchers may request a copy through Interlibrary Loan for personal use.
Language
English
Recommended Citation
Holness, Shevaughn, "The Hairpin: A Novel Method to Correct for the Effects of Population Stratification on Polygenic Risk Scores" (2023). Honors Project, Smith College, Northampton, MA.
https://scholarworks.smith.edu/theses/2537
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