Major
Bioinformatics
Anticipated Graduation Year
2024
Access Type
Restricted Access
Abstract
We all have 99.9% of the same DNA with 0.1% genetic difference that contributes to many phenotypic differences, including disease risk. Using polygenic risk scores, we can combine the SNP effect sizes to show how likely an individual is at risk for a disease/trait compared to others. PRS-CSx is a python-based command line tool that integrates data from multiple populations to improve cross-population polygenic predictions. The results that I obtain from this project can help create better polygenic risk score models that can be applicable to all populations and not just populations that have more genotypic and phenotypic data.
Faculty Mentors & Instructors
Dr. Heather Wheeler, Department of Biology
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
Refining cross-population polygenic risk scores to optimize trait prediction in diverse populations
We all have 99.9% of the same DNA with 0.1% genetic difference that contributes to many phenotypic differences, including disease risk. Using polygenic risk scores, we can combine the SNP effect sizes to show how likely an individual is at risk for a disease/trait compared to others. PRS-CSx is a python-based command line tool that integrates data from multiple populations to improve cross-population polygenic predictions. The results that I obtain from this project can help create better polygenic risk score models that can be applicable to all populations and not just populations that have more genotypic and phenotypic data.