Major
Physics
Anticipated Graduation Year
2022
Access Type
Restricted Access
Abstract
Genome-wide association studies (GWAS) have been utilized to find associations between single nucleotide polymorphisms (SNPs) and traits. However, most GWAS are conducted on populations of European ancestries. Further analysis involving the transcriptome has to lead to new biological discoveries. Transcriptome-wide association studies (TWAS) have utilized prediction models to estimate transcriptome data and then find gene-trait associations. However, most of these prediction models are based on populations of European ancestries with different genetic structures, making cross-population predictions unreliable. Therefore, this study focuses on building and testing transcriptome prediction models with better cross-population performance.
Faculty Mentors & Instructors
Dr. Heather Wheeler;Daniel Araujo
Supported By
TOPMed MESA;NIH
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
Cross-population Transcriptome Prediction Models for Transcriptome-Wide Association Studies in Diverse Populations
Genome-wide association studies (GWAS) have been utilized to find associations between single nucleotide polymorphisms (SNPs) and traits. However, most GWAS are conducted on populations of European ancestries. Further analysis involving the transcriptome has to lead to new biological discoveries. Transcriptome-wide association studies (TWAS) have utilized prediction models to estimate transcriptome data and then find gene-trait associations. However, most of these prediction models are based on populations of European ancestries with different genetic structures, making cross-population predictions unreliable. Therefore, this study focuses on building and testing transcriptome prediction models with better cross-population performance.