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Multivariate Adaptive Shrinkage Improves Cross-Population Transcriptome Prediction and Association Studies in Underrepresented Populations
Human Genetics and Genomics Advances
  • Daniel Araujo, Loyola University Chicago
  • Chris Nguyen, Loyola University Chicago
  • Xiaowei Hu, University of Virginia
  • Anna V. Mikhaylova, University of Washington
  • Christopher R. Gignoux, University of Colorado Anschutz Medical Campus
  • Kristin Ardlie, Broad Institute
  • Kent D. Taylor, Harbor-UCLA Medical Center
  • Peter Durda, The University of Vermont
  • Yongmei Liu, Duke University School of Medicine
  • George Papanicolaou, National Heart, Lung, and Blood Institute (NHLBI)
  • Michael H. Cho, Brigham and Women's Hospital
  • Stephen S. Rich, University of Virginia School of Medicine
  • Jerome I. Rotter, Institute for Translational Genomics and Population Sciences
  • NHLBI TOPMed Consortium, NHLBI TOPMed Consortium
  • Hae Kyung Im, Loyola University Chicago
  • Ani Manichaikul, University of Virginia School of Medicine
  • Heather Wheeler, Loyola University Chicago
Document Type
Article
Publication Date
10-12-2023
Pages
1-12
Publisher Name
Elsevier
Disciplines
Abstract

Transcriptome prediction models built with data from European-descent individuals are less accurate when applied to different populations because of differences in linkage disequilibrium patterns and allele frequencies. We hypothesized that methods that leverage shared regulatory effects across different conditions, in this case, across different populations, may improve cross-population transcriptome prediction. To test this hypothesis, we made transcriptome prediction models for use in transcriptome-wide association studies (TWASs) using different methods (elastic net, joint-tissue imputation [JTI], matrix expression quantitative trait loci [Matrix eQTL], multivariate adaptive shrinkage in R [MASHR], and transcriptome-integrated genetic association resource [TIGAR]) and tested their out-of-sample transcriptome prediction accuracy in population-matched and cross-population scenarios. Additionally, to evaluate model applicability in TWASs, we integrated publicly available multiethnic genome-wide association study (GWAS) summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study and Pan-ancestry genetic analysis of the UK Biobank (PanUKBB) with our developed transcriptome prediction models. In regard to transcriptome prediction accuracy, MASHR models performed better or the same as other methods in both population-matched and cross-population transcriptome predictions. Furthermore, in multiethnic TWASs, MASHR models yielded more discoveries that replicate in both PAGE and PanUKBB across all methods analyzed, including loci previously mapped in GWASs and loci previously not found in GWASs. Overall, our study demonstrates the importance of using methods that benefit from different populations’ effect size estimates in order to improve TWASs for multiethnic or underrepresented populations.

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Author Posting © The Authors, 2023. This article was published open access in Human Genetics and Genomics Advances, Volume 4, Issue 4, October 2023. www.doi.org/10.1016/j.xhgg.2023.100216

Creative Commons License
Creative Commons Attribution 4.0 International
Citation Information
Daniel Araujo, Chris Nguyen, Xiaowei Hu, Anna V. Mikhaylova, et al.. "Multivariate Adaptive Shrinkage Improves Cross-Population Transcriptome Prediction and Association Studies in Underrepresented Populations" Human Genetics and Genomics Advances Vol. 4 Iss. 4 (2023)
Available at: http://works.bepress.com/heather-wheeler/70/