Major

Bioinformatics

Anticipated Graduation Year

2020

Access Type

Open Access

Abstract

Recent genome-wide association studies (GWAS) primarily focus on European individuals; however, these results cannot be accurately applied to non-European populations due to differences in genetic architecture. Using previously published Multi-Ethnic Study of Atherosclerosis (MESA) gene-expression prediction models, we perform S-PrediXcan, a transcriptome wide association method, with previously published GWAS summary statistics to identify gene-trait associations. Wojcik et al. 2019 includes single nucleotide polymorphism-level associations for twenty-seven phenotypes in approximately 50,000 non-European individuals. From this S-PrediXcan output we can identify genes associated with various complex traits for non-European cohorts.

Faculty Mentors & Instructors

Dr. Heather Wheeler, Assistant Professor, Departments of Biology and Computer Science

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

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Inferring Gene-Trait Associations with Diverse Population Gene Expression Prediction Models

Recent genome-wide association studies (GWAS) primarily focus on European individuals; however, these results cannot be accurately applied to non-European populations due to differences in genetic architecture. Using previously published Multi-Ethnic Study of Atherosclerosis (MESA) gene-expression prediction models, we perform S-PrediXcan, a transcriptome wide association method, with previously published GWAS summary statistics to identify gene-trait associations. Wojcik et al. 2019 includes single nucleotide polymorphism-level associations for twenty-seven phenotypes in approximately 50,000 non-European individuals. From this S-PrediXcan output we can identify genes associated with various complex traits for non-European cohorts.