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Protein prediction for trait mapping in diverse populations
PLOS ONE (2022)
  • Heather Wheeler, Loyola University Chicago
  • Ryan Schubert
  • Elyse Geoffroy
  • Isabelle Gregga
  • Ashley J Mulford
  • Francois Aguet
  • Kristin Ardlie
  • Robert Gerszten
  • Clary B. Clish
  • David van den Berg
  • Kent D Taylor
  • Peter Durda
  • W. Craig Johnson, University of Washington
  • Elaine Cornell
  • Xiuqing Guo, Los Angeles Biomedical Research Institute
  • Yongmei Liu
  • Tracy Russell
  • Matthew Conomos
  • Tommy Dawain Blackwell
  • George J. Papanicolaou, National Institutes of Health
  • Tuuli Lappalainen, Columbia University
  • Anna Mikhaylova
  • Michael Cho
  • Christopher Gignoux,
  • Leslie Lange
  • Ethan Lange
  • Stephen S. Rich
  • Jerome I. Rotter, Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
  • Manichaikul A, University of Virginia
  • Hae Kyung Im, University of Chicago
Abstract
Genetically regulated gene expression has helped elucidate the biological mechanisms underlying complex traits. Improved high-throughput technology allows similar interrogation of the genetically regulated proteome for understanding complex trait mechanisms. Here, we used the Trans-omics for Precision Medicine (TOPMed) Multi-omics pilot study, which comprises data from Multi-Ethnic Study of Atherosclerosis (MESA), to optimize genetic predictors of the plasma proteome for genetically regulated proteome-wide association studies (PWAS) in diverse populations. We built predictive models for protein abundances using data collected in TOPMed MESA, for which we have measured 1,305 proteins by a SOMAscan assay. We compared predictive models built via elastic net regression to models integrating posterior inclusion probabilities estimated by fine-mapping SNPs prior to elastic net. In order to investigate the transferability of predictive models across ancestries, we built protein prediction models in all four of the TOPMed MESA populations, African American (n = 183), Chinese (n = 71), European (n = 416), and Hispanic/Latino (n = 301), as well as in all populations combined. As expected, fine-mapping produced more significant protein prediction models, especially in African ancestries populations, potentially increasing opportunity for discovery. When we tested our TOPMed MESA models in the independent European INTERVAL study, fine-mapping improved cross-ancestries prediction for some proteins. Using GWAS summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study, which comprises ∼50,000 Hispanic/Latinos, African Americans, Asians, Native Hawaiians, and Native Americans, we applied S-PrediXcan to perform PWAS for 28 complex traits. The most protein-trait associations were discovered, colocalized, and replicated in large independent GWAS using proteome prediction model training populations with similar ancestries to PAGE. At current training population sample sizes, performance between baseline and fine-mapped protein prediction models in PWAS was similar, highlighting the utility of elastic net
Disciplines
Publication Date
February 24, 2022
DOI
10.1371/journal.pone.0264341
Citation Information
Heather Wheeler, Ryan Schubert, Elyse Geoffroy, Isabelle Gregga, et al.. "Protein prediction for trait mapping in diverse populations" PLOS ONE (2022)
Available at: http://works.bepress.com/heather-wheeler/67/