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Protein prediction for trait mapping in diverse populations.

Ryan Schubert1,2,3, Elyse Geoffroy3, Isabelle Gregga2

  • 1Department of Mathematics and Statistics, Loyola University Chicago, Chicago, IL, United States of America.

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|February 24, 2022
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Summary

We developed genetic predictors for plasma proteome-wide association studies (PWAS) in diverse populations. Fine-mapping improved protein prediction models, enhancing discovery potential for complex traits.

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Area of Science:

  • Genomics and Proteomics
  • Precision Medicine
  • Complex Trait Genetics

Background:

  • Genetically regulated gene expression aids in understanding complex traits.
  • High-throughput technology enables proteome interrogation for similar insights.
  • The Trans-omics for Precision Medicine (TOPMed) Multi-omics pilot study provides valuable data.

Purpose of the Study:

  • To optimize genetic predictors of the plasma proteome for proteome-wide association studies (PWAS) in diverse populations.
  • To assess the transferability of predictive models across different ancestries.
  • To identify protein-trait associations for complex traits using PWAS.

Main Methods:

  • Utilized TOPMed Multi-Ethnic Study of Atherosclerosis (MESA) data to build predictive models for 1,305 proteins.
  • Compared elastic net regression models with and without fine-mapping using posterior inclusion probabilities.
  • Applied S-PrediXcan to GWAS summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study for PWAS.

Main Results:

  • Fine-mapping generated more significant protein prediction models, particularly in African ancestries, increasing discovery potential.
  • Models trained in TOPMed MESA showed improved cross-ancestry prediction in the INTERVAL study when fine-mapping was used.
  • PWAS identified protein-trait associations that colocalized and replicated in independent GWAS, especially when training populations matched PAGE ancestries.

Conclusions:

  • Optimized genetic predictors for plasma proteome enable PWAS in diverse populations.
  • Fine-mapping enhances protein prediction models, aiding in the discovery of genetic associations with complex traits.
  • Publicly available predictive models facilitate proteome mapping research.