Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations
Daniel S. Araujo,
Chris Nguyen,
Xiaowei Hu,
Anna V. Mikhaylova,
Chris Gignoux,
Kristin Ardlie,
Kent D. Taylor,
Peter Durda,
Yongmei Liu,
George Papanicolaou,
Michael H. Cho,
Stephen S. Rich,
Jerome I. Rotter,
Hae Kyung Im,
Ani Manichaikul,
Heather E. Wheeler
Affiliations
Daniel S. Araujo
Program in Bioinformatics, Loyola University Chicago, Chicago, IL 60660, USA
Chris Nguyen
Department of Biology, Loyola University Chicago, Chicago, IL 60660, USA
Xiaowei Hu
Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
Anna V. Mikhaylova
Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
Chris Gignoux
Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, UC Denver Anschutz Medical Campus, Aurora, CO 80045, USA
Kristin Ardlie
Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
Kent D. Taylor
The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
Peter Durda
Laboratory for Clinical Biochemistry Research, University of Vermont, Colchester, VT 05446, USA
Yongmei Liu
Department of Medicine, Duke University School of Medicine, Durham, NC 27710, USA
George Papanicolaou
Epidemiology Branch, Division of Cardiovascular Sciences, National Heart, Lung and Blood Institute, Bethesda, MD 20892, USA
Michael H. Cho
Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
Stephen S. Rich
Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
Jerome I. Rotter
The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
Hae Kyung Im
Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA
Ani Manichaikul
Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
Heather E. Wheeler
Program in Bioinformatics, Loyola University Chicago, Chicago, IL 60660, USA; Department of Biology, Loyola University Chicago, Chicago, IL 60660, USA; Corresponding author
Summary: 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.
