Date of Award
Master of Science (MS)
Bioinformatics & Computational Biology
Most genome-wide and transcriptome-wide association studies (GWAS, TWAS) focus on European populations; however, these results cannot always be accurately applied to non-European populations due to differences in genetic architecture. Using summary statistics from GWAS in the Population Architecture using Genomics and Epidemiology (PAGE) study, which comprises ~50,000 Hispanic/Latinos, African Americans, Asians, Native Hawaiians, and Native Americans, we perform transcriptome-wide association studies to determine gene-trait associations. Initially, we compared results using two transcriptome prediction models derived from the Multi-Ethnic Study of Atherosclerosis (MESA) populations: the African American (AFA) model and the Hispanic/Latino (HIS) model. We identified 141 unique genome-wide significant trait-associated genes. 11 of the 141 gene-trait pairs were found to have colocalized eQTL and GWAS signals and replicated in PhenomeXcan. Overall, we identified more significant, colocalized, replicated gene-trait pairs in the HIS MESA model than the AFA model. Since the largest population in PAGE is of Hispanic/Latino ancestries, TWAS with more population-matched transcriptome models, i.e. HIS rather than AFA, have more power for discovery and gene-trait replication. Following this analysis, we then compared results using three larger transcriptome prediction models derived from Multi-Ethnic Study of Atherosclerosis (MESA) populations: the African American and Hispanic/Latino (AFHI) model, the European (EUR) model, and the African American, Hispanic/Latino, and European (ALL) model. We identified 240 unique genome-wide significant trait-associated genes. We found more significant, colocalized genes that replicate in larger cohorts when applying the AFHI model to the PAGE summary statistics than the EUR or ALL model. We also found more significant gene-trait pairs using the AFHI, which identified 152 trait-associated genes, when compared to the number of associations made by both the AFA and HIS transcriptome models. Thus, TWAS with population-matched transcriptome models have more power for discovery and replication, demonstrating the need for more transcriptome studies in diverse populations.
Geoffroy, Elyse, "Population-Matched Transcriptome Prediction Increases Discovery and Replication Rate in TWAS" (2021). Master's Theses. 4359.
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Copyright © 2021 Elyse Geoffroy