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Population-Matched Transcriptome Prediction Increases TWAS Discovery and Replication Rate.

Elyse Geoffroy1, Isabelle Gregga2, Heather E Wheeler1,2

  • 1Program in Bioinformatics, Loyola University Chicago, Chicago, IL 60660, USA.

Iscience
|December 14, 2020
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Summary
This summary is machine-generated.

Transcriptome-wide association studies (TWAS) using population-matched models, like the African American and Hispanic/Latino (AFHI) model, improve gene-trait discovery in diverse populations. This highlights the need for broader genetic studies beyond European ancestries.

Keywords:
GeneticsGenomicsHuman GeneticsPopulation

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Area of Science:

  • Genomics and Population Genetics
  • Genetic Epidemiology
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) and transcriptome-wide association studies (TWAS) predominantly utilize European population data.
  • Genetic architecture variations across diverse ancestries limit the direct applicability of European-centric findings to non-European populations.
  • The Population Architecture using Genomics and Epidemiology (PAGE) study offers a valuable resource for investigating genetic associations in underrepresented groups.

Purpose of the Study:

  • To conduct transcriptome-wide association studies (TWAS) to identify gene-trait associations in a diverse cohort.
  • To compare the efficacy of different transcriptome prediction models (African American and Hispanic/Latino (AFHI), European (EUR), and ALL) for TWAS.
  • To assess the impact of population-matched transcriptome models on gene discovery and replication power.

Main Methods:

  • Utilized GWAS summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study (∼50,000 participants).
  • Performed TWAS using three transcriptome prediction models derived from the Multi-Ethnic Study of Atherosclerosis (MESA) populations: AFHI, EUR, and ALL.
  • Compared the number of significant, colocalized genes identified and their replication success across different models.

Main Results:

  • Identified 240 unique genes significantly associated with various traits.
  • The AFHI model yielded a greater number of significant and colocalized genes that replicated in larger cohorts compared to the EUR and ALL models.
  • Demonstrated superior performance of population-matched transcriptome models in TWAS discovery and replication.

Conclusions:

  • Transcriptome-wide association studies (TWAS) benefit significantly from using transcriptome prediction models that match the ancestry of the study population.
  • The AFHI model showed enhanced power for identifying and replicating gene-trait associations in the studied diverse populations.
  • Emphasized the critical need for developing transcriptome resources from diverse ethnic groups to improve the accuracy and scope of genetic association studies.