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Multivariate adaptive shrinkage improves cross-population transcriptome prediction for transcriptome-wide association studies in underrepresented populations.
Araujo, Daniel S; Nguyen, Chris; Hu, Xiaowei; Mikhaylova, Anna V; Gignoux, Chris; Ardlie, Kristin; Taylor, Kent D; Durda, Peter; Liu, Yongmei; Papanicolaou, George; Cho, Michael H; Rich, Stephen S; Rotter, Jerome I; Im, Hae Kyung; Manichaikul, Ani; Wheeler, Heather E.
Afiliação
  • Araujo DS; Program in Bioinformatics, Loyola University Chicago, Chicago, IL, 60660, USA.
  • Nguyen C; Department of Biology, Loyola University Chicago, Chicago, IL, 60660, USA.
  • Hu X; Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, 22908, USA.
  • Mikhaylova AV; Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA.
  • Gignoux C; Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, UC Denver Anschutz Medical Campus, Aurora, CO, 80045, USA.
  • Ardlie K; Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
  • Taylor KD; 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.
  • Durda P; Laboratory for Clinical Biochemistry Research, University of Vermont, Colchester, VT, 05446, USA.
  • Liu Y; Department of Medicine, Duke University School of Medicine, Durham, NC, 27710, USA.
  • Papanicolaou G; Epidemiology Branch, Division of Cardiovascular Sciences, National Heart, Lung and Blood Institute, Bethesda, MD, 20892, USA.
  • Cho MH; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA.
  • Rich SS; Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, 22908, USA.
  • Rotter JI; 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.
  • Im HK; Section of Genetic Medicine, The University of Chicago, Chicago, IL, 60637, USA.
  • Manichaikul A; Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, 22908, USA.
  • Wheeler HE; Program in Bioinformatics, Loyola University Chicago, Chicago, IL, 60660, USA.
bioRxiv ; 2023 May 20.
Article em En | MEDLINE | ID: mdl-36798214
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 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 (TWAS) using different methods (Elastic Net, Joint-Tissue Imputation (JTI), 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 TWAS, we integrated publicly available multi-ethnic genome-wide association study (GWAS) summary statistics from the Population Architecture using Genomics and Epidemiology Study (PAGE) and Pan-UK Biobank 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 multi-ethnic TWAS, MASHR models yielded more discoveries that replicate in both PAGE and PanUKBB across all methods analyzed, including loci previously mapped in GWAS and new loci previously not found in GWAS. Overall, our study demonstrates the importance of using methods that benefit from different populations' effect size estimates in order to improve TWAS for multi-ethnic or underrepresented populations.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: BioRxiv Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: BioRxiv Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Estados Unidos