Cross-cohort analysis of expression and splicing quantitative trait loci in TOPMed

Abstract Most genetic variants associated with complex traits and diseases occur in non-coding genomic regions and are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterize 14,324 ancestrally diverse RNA-sequencing samples from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program and integrate whole genome sequencing data to perform cis and trans expression and splicing quantitative trait locus ( cis -/trans-e/sQTL) analyses in six tissues and cell types, most notably whole blood (N=6,454) and lung (N=1,291). We show this dataset enables greater detection of secondary cis-e/sQTL signals than was achieved in previous studies, and that secondary cis-eQTL and primary trans-eQTL signal discovery is not saturated even though eGene discovery is. Most TOPMed trans-eQTL signals colocalize with cis-e/sQTL signals, suggesting many trans signals are mediated by cis signals. We fine-map European UK BioBank GWAS signals from 164 traits and colocalize the resulting 34,107 fine-mapped GWAS signals with TOPMed e/sQTL signals, finding that of 10,611 GWAS signals with a colocalization, 7,096 GWAS signals colocalize with at least one secondary e/sQTL signal. These results demonstrate that larger e/sQTL analyses will continue to uncover secondary e/sQTL signals, and that these new signals will benefit GWAS interpretation.

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Publication Details

Journal
Science
Published
2026-07-16
DOI
https://doi.org/10.1126/science.adx2989
Citations
13
Primary Topic
RNA modifications and cancer
Type
article
Field-Weighted Citation Impact
30.00

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article

Cross-cohort analysis of expression and splicing quantitative trait loci in TOPMed

Sharon Graw, Charles Kooperberg, Daniel L. Levy, Laura J. Scott et al.
13 citations
Science
RNA modifications and cancer
30.00
article

Cross-cohort analysis of expression and splicing quantitative trait loci in TOPMed

Sharon Graw, Charles Kooperberg, Daniel L. Levy, Laura J. Scott, Ramachandran Srini Vasan, Stephanie A. Christenson, David T. Paik, Weiniu Gan, Peter Durda, Roby Joehanes, Honghuang Lin, Russell Paul Bowler, Michael H. Cho, Kent D. Taylor, Ying Zhou, Craig Johnson, Ani Manichaikul, Edwin K. Silverman, Aabida Saferali, Jerome I. Rotter, Laura M. Raffield, Mari Johnson, Yinan Zheng, Joseph C. Wu, Peter Orchard, Russell P. Tracy, Stephen C.J. Parker, Linda Kachuri, Elad Ziv, Joshua S. Weinstock, Nancy L. Heard‐Costa, Peter J. Castaldi, Angel C. Y. Mak, Heather E. Wheeler, L. Adrienne Cupples, Craig P. Hersh, Xingnan Li, Deborah A. Meyers, Stacey Gabriel, S.-J. Hwang, Martin G. Larson, Matthew R.G. Taylor, Luisa Mestroni, Namrata Gupta, Prescott G. Woodruff, Stephen S. Rich, Jiongming Wang, Esteban G. Burchard, Ching‐Ti Liu, Ming‐Qiang Wang, Scott Huntsman, Yongmei Liu, Albert V. Smith, Victor E. Ortega, Thomas W. Blackwell, Deborah A. Nickerson, Gonçalo Abecasis, Hua Tang, Joshua D. Smith, Jeffrey Weiss, Alex Reiner, Sebastian Zoellner, François Aguet, Kristin G. Ardlie
article en
13 citations

Abstract

Abstract Most genetic variants associated with complex traits and diseases occur in non-coding genomic regions and are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterize 14,324 ancestrally diverse RNA-sequencing samples from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program and integrate whole genome sequencing data to perform cis and trans expression and splicing quantitative trait locus ( cis -/trans-e/sQTL) analyses in six tissues and cell types, most notably whole blood (N=6,454) and lung (N=1,291). We show this dataset enables greater detection of secondary cis-e/sQTL signals than was achieved in previous studies, and that secondary cis-eQTL and primary trans-eQTL signal discovery is not saturated even though eGene discovery is. Most TOPMed trans-eQTL signals colocalize with cis-e/sQTL signals, suggesting many trans signals are mediated by cis signals. We fine-map European UK BioBank GWAS signals from 164 traits and colocalize the resulting 34,107 fine-mapped GWAS signals with TOPMed e/sQTL signals, finding that of 10,611 GWAS signals with a colocalization, 7,096 GWAS signals colocalize with at least one secondary e/sQTL signal. These results demonstrate that larger e/sQTL analyses will continue to uncover secondary e/sQTL signals, and that these new signals will benefit GWAS interpretation.

ScienceVol. 393(6808)
Broad Institute (US), Boston University (US), University of Vermont (US), University of North Carolina at Chapel Hill (US), University of Maryland, Baltimore (US), Brigham and Women's Hospital (US), National Institutes of Health (US), Cleveland Clinic (US), Cape Town HVTN Immunology Laboratory / Hutchinson Centre Research Institute of South Africa (ZA), Children's Hospital of Philadelphia (US), Harvard University (US), University of Arizona (US), Johns Hopkins University (US), University of California, Los Angeles (US), National Health Research Institutes (TW), University of Massachusetts Chan Medical School (US), National Taiwan University (TW), University of Pittsburgh (US), Duke University (US), Illumina (United States) (US), University of California, San Francisco (US), Baylor College of Medicine (US), Loyola University Chicago (US), University of Washington (US), Medical College of Wisconsin (US), Cardiovascular Institute of the South (US), University of Michigan (US), UCLA Medical Center (US), WinnMed (US), University of California System (US), National Jewish Health (US), University of Mississippi (US), Fred Hutch Cancer Center (US), The Coordinating Center (US), National Heart, Lung, and Blood Institute (US), Emmes (United States) (US), Framingham Heart Study (US), Mayo Clinic in Arizona (US), Mayo Clinic Hospital (US), National Taiwan University Hospital (TW), Fundação Hemopa (BR), New York Genome Center (US), Baylor Genetics (US), Harbor–UCLA Medical Center (US), UCSF Helen Diller Family Comprehensive Cancer Center (US), University of Virginia (US), University of Colorado Anschutz Medical Campus (US), Washington State University (US), University of San Francisco (US), Columbia University (US), University of Pennsylvania (US), University of Colorado Denver (US), Philadelphia University (US), Stanford University (US), Icahn School of Medicine at Mount Sinai (US), National Taipei University (TW)
Foundation for the National Institutes of Health, Amgen, AstraZeneca, GlaxoSmithKline, Ikaria, Chiesi Farmaceutici, COPD Foundation, American Asthma Foundation, Evans Medical Foundation, Forest Research Institute, Grifols, National Institutes of Health, Genentech, National Heart, Lung, and Blood Institute, National Cancer Institute, National Institute of Environmental Health Sciences
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RNA modifications and cancer
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