Evidence map›Paper›PMID 41932314›Full record

ArticleAmerican journal of human genetics2026

Multi-ancestry transcriptome prediction with functionally informed variants in TOPMed MESA improves performance of transcriptome-wide association studies.

Xiaowei Hu, Daniel S Araujo, Chachrit Khunsriraksakul, Lida Wang, Quan Sun, Jia Wen, Lingbo Zhou, Lynette Ekunwe, Leslie A Lange, Ethan M Lange and 26 more

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Article in American journal of human genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

36 authors.

Xiaowei HuDepartment of Genome Sciences, University of Virginia, Charlottesville, VA, USA. Electronic address: xh6dx@virginia.edu.
Daniel S AraujoDepartment of Biology, Loyola University Chicago, Chicago, IL, USA; Department of Human Genetics, University of Chicago, Chicago, IL, USA.
Chachrit KhunsriraksakulDepartment of Bioinformatics and Genomics, Pennsylvania State University, Hershey, PA, USA.
Lida WangDepartment of Public Health Sciences, Pennsylvania State University, Hershey, PA, USA.
Quan SunDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Center for Computational and Genomic Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Jia WenDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Lingbo ZhouDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Lynette EkunweDepartment of Medicine, University of Mississippi Medical Center, Jackson, MS, USA.
Leslie A LangeDepartment of Biomedical Informatics, University of Colorado-Anschutz, Aurora, CO, USA.
Ethan M LangeDepartment of Biomedical Informatics, University of Colorado-Anschutz, Aurora, CO, USA.
Stephen B MontgomeryDepartment of Genetics, Department of Pathology, Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Alexander P ReinerDepartment of Epidemiology, University of Washington, Seattle, WA, USA.
Francois AguetThe Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Kristin G ArdlieThe Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Tuuli LappalainenNew York Genome Center, New York, NY, USA.
Christopher R GignouxColorado Center for Personalized Medicine, Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Esteban G BurchardDepartment of Medicine, Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.
Kent D TaylorThe Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
Xiuqing GuoThe Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
Jerome I RotterThe Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
Stephen S RichDepartment of Genome Sciences, University of Virginia, Charlottesville, VA, USA.
Elaine CornellDepartment of Pathology Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA.
Peter DurdaDepartment of Pathology Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA.
Russell P TracyDepartment of Pathology Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA.
Yongmei LiuDivision of Cardiology, Department of Medicine, Duke University, Durham, NC, USA.
W Craig JohnsonDepartment of Biostatistics, University of Washington, Seattle, WA, USA.
George P PapanicolaouEpidemiology Branch, National Heart, Lung, and Blood Institute, Bethesda, MD, USA.
Minoli A PereraDepartment of Pharmacology, Northwestern University, Chicago, IL, USA.
Michael H ChoChanning Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Dajiang J LiuDepartment of Bioinformatics and Genomics, Pennsylvania State University, Hershey, PA, USA.
Laura M RaffieldDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Yun LiDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
TOPMed Multi-Omics Working Group
Heather E WheelerDepartment of Biology, Loyola University Chicago, Chicago, IL, USA.
Hae Kyung ImSection of Genetic Medicine, The University of Chicago, Chicago, IL, USA.
Ani ManichaikulDepartment of Genome Sciences, University of Virginia, Charlottesville, VA, USA. Electronic address: am3xa@virginia.edu.

Funding

Next generation functional genomics of hematology traitsR01HL146500 · NHLBI · UNIVERSITY OF WASHINGTON · PI ALEXANDER P REINER · 2020 to 2026
$5.7M
Polygenic risk scores for cardiometabolic disorders: the role of blood cells immune response and evolutionary adaptationU01HG011720 · NHGRI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Yun Li, ALEXANDER P REINER · 2021 to 2026
$5.4M
Immune Cells in Alzheimer’s Disease and Related Dementias in the Jackson Heart StudyR01AG075884 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Laura M Raffield · 2022 to 2026
$3.7M
Integrative genomic, transcriptomic and proteomic studies of pulmonary function and COPDR01HL153248 · NHLBI · UNIVERSITY OF VIRGINIA · PI CHO, MICHAEL H., MANICHAIKUL, ANI WANG · 2021 to 2024
$2.8M
Integrative genomic and geospatial analysis of insurance claim, biobank and GWAS summary statistics for complex traitsR01ES036042 · NIEHS · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · PI Bibo Jiang · 2023 to 2026
$2.1M
Predicting gene regulation across populations to understand mechanisms underlying complex traitsR15HG009569 · NHGRI · LOYOLA UNIVERSITY OF CHICAGO · PI WHEELER, HEATHER ELIZABETH · 2017 to 2023
$1.3M
NHGRI NIH HHS R15 HG009569NHGRI NIH HHS U01 HG011720NHLBI NIH HHS R01 HL146500NHLBI NIH HHS R01 HL153248NIA NIH HHS R01 AG075884NIEHS NIH HHS R01 ES036042
6 · The paper itself

Abstract

Reliable reference transcriptome prediction models are key to accurate multi-ancestry transcriptome-wide association studies (TWASs). We propose three methods leveraging functionally informed variants (FIVs) for transcriptome prediction models to improve multi-ancestry TWASs. We trained models on 1,287 multi-ancestry participants from the Trans-Omics for Precision Medicine (TOPMed) program Multi-Ethnic Study of Atherosclerosis (MESA) with RNA sequencing (RNA-seq) data from peripheral blood mononuclear cells (PBMCs). We validated models' prediction accuracy on two external independent datasets, Geuvadis and Jackson Heart Study. To test robustness of our methods for TWASs, we integrated models with three multi-ancestry GWASs from blood cell, lipid, and pulmonary function traits, respectively. Our methods presented similar prediction accuracy while using a smaller and functionally informed set of variants compared to the benchmark method, elastic net (EN). Overall, our methods achieved higher power and accuracy (with average improved accuracy of 24% over EN) for TWASs. However, no single proposed method outperformed all GWAS traits. To further improve TWAS performance, we propose an omnibus approach that aggregates TWAS summary statistics from our methods. The omnibus approach yielded the highest number of Bonferroni-significant TWAS genes for all GWAS traits, and it further improved TWAS power and accuracy for blood cell traits. Additionally, the omnibus approach detected some trait-relevant important genes that the EN missed. Our study demonstrates the value of including FIVs in multi-ancestry transcriptome prediction models for improving TWAS performance. Further, the observed TWAS improvement depends on the GWAS trait's relevance to the PBMCs used to build our transcriptome prediction models.

Indexed as

AtherosclerosisGenome-Wide Association StudyTranscriptomeFemaleGene Expression ProfilingHumansLeukocytes, MononuclearPolymorphism, Single Nucleotidefunctional annotationmulti-ancestryomnibus approachPBMCsperipheral blood mononuclear cellsRNA-seqtranscriptome prediction modelstranscriptome-wide association study

Identifiers

PMID41932314
PMCPMC13078710

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.