Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations
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 ac...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-10-01
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| Series: | HGG Advances |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666247723000489 |
| _version_ | 1797778409334505472 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Daniel S. Araujo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T23:17:52Z |
| format | Article |
| id | doaj.art-e5378247f4d847d8b11fd367f8966c80 |
| institution | Directory Open Access Journal |
| issn | 2666-2477 |
| language | English |
| last_indexed | 2024-03-12T23:17:52Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | HGG Advances |
| spelling | doaj.art-e5378247f4d847d8b11fd367f8966c802023-07-17T04:07:57ZengElsevierHGG Advances2666-24772023-10-0144100216Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populationsDaniel S. Araujo0Chris Nguyen1Xiaowei Hu2Anna V. Mikhaylova3Chris Gignoux4Kristin Ardlie5Kent D. Taylor6Peter Durda7Yongmei Liu8George Papanicolaou9Michael H. Cho10Stephen S. Rich11Jerome I. Rotter12Hae Kyung Im13Ani Manichaikul14Heather E. Wheeler15Program in Bioinformatics, Loyola University Chicago, Chicago, IL 60660, USADepartment of Biology, Loyola University Chicago, Chicago, IL 60660, USACenter for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USADepartment of Biostatistics, University of Washington, Seattle, WA 98195, USADivision of Biomedical Informatics and Personalized Medicine, Department of Medicine, UC Denver Anschutz Medical Campus, Aurora, CO 80045, USABroad Institute of MIT and Harvard, Cambridge, MA 02142, USAThe Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USALaboratory for Clinical Biochemistry Research, University of Vermont, Colchester, VT 05446, USADepartment of Medicine, Duke University School of Medicine, Durham, NC 27710, USAEpidemiology Branch, Division of Cardiovascular Sciences, National Heart, Lung and Blood Institute, Bethesda, MD 20892, USAChanning Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USACenter for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USAThe Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USASection of Genetic Medicine, University of Chicago, Chicago, IL 60637, USACenter for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USAProgram in Bioinformatics, Loyola University Chicago, Chicago, IL 60660, USA; Department of Biology, Loyola University Chicago, Chicago, IL 60660, USA; Corresponding authorSummary: 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.http://www.sciencedirect.com/science/article/pii/S2666247723000489geneticsgenomicshuman geneticstranscriptome-wide association studiestranscriptome predictionmultivarite adaptive shrinkage |
| spellingShingle | 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 Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations HGG Advances genetics genomics human genetics transcriptome-wide association studies transcriptome prediction multivarite adaptive shrinkage |
| title | Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations |
| title_full | Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations |
| title_fullStr | Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations |
| title_full_unstemmed | Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations |
| title_short | Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations |
| title_sort | multivariate adaptive shrinkage improves cross population transcriptome prediction and association studies in underrepresented populations |
| topic | genetics genomics human genetics transcriptome-wide association studies transcriptome prediction multivarite adaptive shrinkage |
| url | http://www.sciencedirect.com/science/article/pii/S2666247723000489 |
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