This is a preprint.

It has not yet been peer reviewed by a journal.

The National Library of Medicine is running a pilot to include preprints that result from research funded by NIH in PMC and PubMed.

[Preprint]. 2025 Feb 21:2025.02.19.25322561.

doi: 10.1101/2025.02.19.25322561.

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

Peter Orchard¹, Thomas W Blackwell^{2

3}, Linda Kachuri⁴, Peter J Castaldi⁵, Michael H Cho⁵, Stephanie A Christenson⁶, Peter Durda⁷, Stacey Gabriel⁸, Craig P Hersh^{5

9}, Scott Huntsman¹⁰, Seungyong Hwang¹¹, Roby Joehanes^{12

13}, Mari Johnson¹⁴, Xingnan Li^{15

16}, Honghuang Lin^{13

17}, Ching-Ti Liu¹⁸, Yongmei Liu¹⁹, Angel C Y Mak¹⁰, Ani W Manichaikul²⁰, David Paik²¹, Aabida Saferali⁵, Joshua D Smith²², Kent D Taylor²³, Russell P Tracy²⁴, Jiongming Wang², Mingqiang Wang²¹, Joshua S Weinstock²⁵, Jeffrey Weiss²², Heather E Wheeler^{26

27}, Ying Zhou¹⁴, Sebastian Zoellner^{2

3

28}, Joseph C Wu²¹, Luisa Mestroni²⁹, Sharon Graw²⁹, Matthew R G Taylor²⁹, Victor E Ortega³⁰, Craig W Johnson³¹, Weiniu Gan³², Goncalo Abecasis^{2

3}, Deborah A Nickerson^{22

33}, Namrata Gupta⁸, Kristin Ardlie⁸, Prescott G Woodruff^{6

34}, Yinan Zheng³⁵, Russell P Bowler³⁶, Deborah A Meyers³⁷, Alex Reiner³⁸, Charles Kooperberg¹⁴, Elad Ziv^{10

39

40

41}, Vasan S Ramachandran^{42

43}, Martin G Larson⁴², L Adrienne Cupples^{42

18}, Esteban G Burchard¹⁰, Edwin K Silverman^{5

9}, Stephen S Rich²⁰, Nancy Heard-Costa⁴⁴, Hua Tang¹¹, Jerome I Rotter²³, Albert V Smith^{2

3}, Daniel Levy^{12

13}; NHLBI TOPMed Consortium Multi-Omics Working Group; NHLBI TOPMed Consortium; François Aguet⁴⁵, Laura Scott^{2

3}, Laura M Raffield⁴⁶, Stephen C J Parker^{1

2

47}

Affiliations

¹ Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
² Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
³ Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
⁴ Department of Epidemiology and Population Health, School of Medicine, Stanford University, Stanford, CA, USA.
⁵ Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
⁶ Division of Pulmonary, Critical Care, Allergy, and Sleep Medicine, School of Medicine, University of California, San Francisco, San Francisco, CA, USA.
⁷ Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA.
⁸ Broad Institute of MIT & Harvard, Cambridge, MA, USA.
⁹ Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
¹⁰ Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
¹¹ Department of Genetics, Stanford University, Stanford, CA, USA.
¹² Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
¹³ Framingham Heart Study, National Heart, Lung, and Blood Institute, National Institutes of Health and Boston University, Framingham, MA, USA.
¹⁴ Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA.
¹⁵ Division of Data Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sanai, New York, NY, USA.
¹⁶ Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sanai, New York, NY, USA.
¹⁷ Department of Medicine, University of Massachusetts Chan Medical School, University of Massachusetts, Worcester, MA, USA.
¹⁸ Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA.
¹⁹ Department of Medicine, School of Medicine, Duke University, Durham, NC, USA.
²⁰ Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA, USA.
²¹ Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA.
²² Northwest Genomics Center, University of Washington, Seattle, WA, USA.
²³ The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
²⁴ Laboratory for Clinical Biochemistry Research, Larner College of Medicine, University of Vermont, Burlington, VT, USA.
²⁵ Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
²⁶ Program in Bioinformatics, Loyola University Chicago, Chicago, IL, USA.
²⁷ Department of Biology, Loyola University Chicago, Chicago, IL, USA.
²⁸ Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA.
²⁹ Department of Medicine, Adult Medical Genetics Program, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
³⁰ Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Phoenix, AZ, USA.
³¹ Collaborative Health Studies Coordinating Center, University of Washington, Seattle, WA, USA.
³² National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
³³ Department of Genome Sciences, University of Washington, Seattle, WA, USA.
³⁴ Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA, USA.
³⁵ Department of Preventive Medicine, Northwestern University, Chicago, IL, USA.
³⁶ Department of Genomic Sciences and Systems Biology, Cleveland Clinic, Cleveland, OH, USA.
³⁷ Department of Medicine, Division of Genetics, Genomics and Precision Medicine, University of Arizona, Tucson, AZ, USA.
³⁸ Department of Epidemiology, University of Washington, Seattle, WA, USA.
³⁹ Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA.
⁴⁰ Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
⁴¹ Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.
⁴² Framingham Heart Study, National Heart, Lung, and Blood Institute and Boston University, Framingham, MA, USA.
⁴³ Department of Medicine, School of Medicine, Boston University, Boston, MA, USA.
⁴⁴ Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, USA.
⁴⁵ Illumina Artificial Intelligence Laboratory, Illumina, Foster City, CA, USA.
⁴⁶ Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
⁴⁷ Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA.

PMID: 40034763
PMCID: PMC11875316
DOI: 10.1101/2025.02.19.25322561

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

Peter Orchard et al. medRxiv. 2025.

[Preprint]. 2025 Feb 21:2025.02.19.25322561.

doi: 10.1101/2025.02.19.25322561.

Authors

Affiliations

¹ Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
² Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
³ Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
⁴ Department of Epidemiology and Population Health, School of Medicine, Stanford University, Stanford, CA, USA.
⁵ Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
⁶ Division of Pulmonary, Critical Care, Allergy, and Sleep Medicine, School of Medicine, University of California, San Francisco, San Francisco, CA, USA.
⁷ Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA.
⁸ Broad Institute of MIT & Harvard, Cambridge, MA, USA.
⁹ Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
¹⁰ Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
¹¹ Department of Genetics, Stanford University, Stanford, CA, USA.
¹² Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
¹³ Framingham Heart Study, National Heart, Lung, and Blood Institute, National Institutes of Health and Boston University, Framingham, MA, USA.
¹⁴ Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA.
¹⁵ Division of Data Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sanai, New York, NY, USA.
¹⁶ Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sanai, New York, NY, USA.
¹⁷ Department of Medicine, University of Massachusetts Chan Medical School, University of Massachusetts, Worcester, MA, USA.
¹⁸ Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA.
¹⁹ Department of Medicine, School of Medicine, Duke University, Durham, NC, USA.
²⁰ Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA, USA.
²¹ Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA.
²² Northwest Genomics Center, University of Washington, Seattle, WA, USA.
²³ The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
²⁴ Laboratory for Clinical Biochemistry Research, Larner College of Medicine, University of Vermont, Burlington, VT, USA.
²⁵ Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
²⁶ Program in Bioinformatics, Loyola University Chicago, Chicago, IL, USA.
²⁷ Department of Biology, Loyola University Chicago, Chicago, IL, USA.
²⁸ Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA.
²⁹ Department of Medicine, Adult Medical Genetics Program, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
³⁰ Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Phoenix, AZ, USA.
³¹ Collaborative Health Studies Coordinating Center, University of Washington, Seattle, WA, USA.
³² National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
³³ Department of Genome Sciences, University of Washington, Seattle, WA, USA.
³⁴ Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA, USA.
³⁵ Department of Preventive Medicine, Northwestern University, Chicago, IL, USA.
³⁶ Department of Genomic Sciences and Systems Biology, Cleveland Clinic, Cleveland, OH, USA.
³⁷ Department of Medicine, Division of Genetics, Genomics and Precision Medicine, University of Arizona, Tucson, AZ, USA.
³⁸ Department of Epidemiology, University of Washington, Seattle, WA, USA.
³⁹ Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA.
⁴⁰ Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
⁴¹ Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.
⁴² Framingham Heart Study, National Heart, Lung, and Blood Institute and Boston University, Framingham, MA, USA.
⁴³ Department of Medicine, School of Medicine, Boston University, Boston, MA, USA.
⁴⁴ Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, USA.
⁴⁵ Illumina Artificial Intelligence Laboratory, Illumina, Foster City, CA, USA.
⁴⁶ Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
⁴⁷ Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA.

PMID: 40034763
PMCID: PMC11875316
DOI: 10.1101/2025.02.19.25322561

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.

PubMed Disclaimer

Conflict of interest statement

PJC has received grant support from Bayer and consultant fees from Verona pharmaceuticals. MHC has received grant support from Bayer. JCW is co-founder of Greenstone Biosciences. EKS has received grant funding from Bayer. VEO previously served on independent data and monitoring committees (IDMC) for Regeneron and Sanofi. VEO receives compensation from the American Medical Association for his role as associate editor for JAMA. FA is an employee of Illumina, Inc. and is an inventor on a patent application related to TensorQTL filed by the Broad Institute. LMR is a consultant for the TOPMed Administrative Coordinating Center (through Westat). SCJP is supported by Pfizer. GRA is an employee of Regeneron Pharmaceuticals and owns stock and stock options for Regeneron Pharmaceuticals.

Figures

**Figure 1.**
Study design. (A) RNA-seq sample sizes for cis- and trans-e/sQTL scans, by TOPMed study and tissue. The full TOPMed study names and accompanying abbreviations are: Framingham Heart Study (FHS); Gene-Environments and Admixture in Latino Asthmatics (GALA II); Study of African Americans, Asthma, Genes, & Environments (SAGE); Subpopulations and Intermediate Outcome Measures In COPD Study (SPIROMICS); Women’s Health Initiative (WHI); COPDGene Study (COPDGene); Multi-Ethnic Study of Atherosclerosis (MESA); Lung Tissue Research Consortium (LTRC). Diagram generated with SankeyMATIC. (B) cis-e/sQTL scans tested MAF ≥ 0.01 variants within 1Mb of gene TSS (for whole blood, a scan with variant MAF ≥ 0.001 was also performed). Trans scans tested variant - gene pairs on separate chromosomes (variants with MAF ≥ 0.05). Splicing phenotypes (intron excision ratios) were derived using LeafCutter (Li et al., 2018). (C) cis-e/sQTL signals were colocalized with trans-e/sQTL signals to nominate genes mediating trans effects, and cis- and trans-e/sQTL signals were colocalized with 34,107 GWAS signals from 164 UK BioBank GWAS to nominate genes and molecular mechanisms underlying GWAS signals.

**Figure 2.**
cis-e/sQTL summary. (A) Sample sizes per tissue (top panel), number of genes with a significant cis-e/sQTL (cis-e/sGenes) and number of SuSiE credible sets discovered per cis-e/sGene (second from top), credible set sizes (third from top), and number of primary or secondary (including tertiary, quaternary, etc.) cis-e/sQTL signals per tissue (bottom). (B) Cis-eQTL saturation analysis, and comparison to other published datasets (from eQTL-Catalogue or GTEx). Number of cis-eGenes discovered at each downsampled sample size (left) and total number of cis-eQTL signals discovered (right; 95% credible sets). Results are shown for 1% FDR cis-Genes, as eQTL-Catalogue fine-maps QTL signals for 1% FDR cis-e/sGenes. (C) Functional annotation enrichments for whole blood cis-e/sQTLs. Enrichment calculated relative to control credible sets matched on MAF, LD, and number of genes tested against; error bars represent 95% confidence intervals. (D) Effect size (absolute allelic fold change) for cis-eQTL in whole blood scan with MAF 0.1% threshold.

**Figure 3.**
trans-e/sQTL results. (A) number of trans-eGenes and trans-sGenes as a function of sample size. TOPMed tissues are linked to the corresponding GTEx and DIRECT tissues. Unlike TOPMed and GTEx, DIRECT did not apply a MAF threshold. TOPMed and GTEx defined trans as ‘different chromosome’, while DIRECT defined trans as ‘different chromosome or gene - variant pair ≥ 5Mb apart’. (B) Effect size (absolute allelic fold change) for trans-eQTL and cis-eQTL with MAF ≥ 0.05 in whole blood (cis-eQTL effect sizes from MAF ≥ 0.001 scan). (C) Number of trans-eQTL and trans-sQTL credible sets discovered when fine-mapping the region around primary trans-e/sQTL signals (+/− 1Mb). (D), (E) Two (of three total) whole blood trans-eQTL signals for gene *AGAP2*. One colocalizes with a *RREB1* cis-sQTL, one contains a *RREB1* missense variant, suggesting that both variants impact *AGAP2* expression via distinct functional effects on *RREB1*. The credible set variants for the third *AGAP2* trans-eQTL signal are in an *RREB1* intron.

**Figure 4.**
Colocalization of e/sQTL with UKBB GWAS signals. (A) Number of GWAS signals colocalizing with at least one e/sQTL credible set from each tissue and modality (cross-ancestry e/sQTL scans). (B) Heatmap displaying, for each GWAS signal with at least one e/sQTL colocalization, the maximum coloc posterior probability of colocalization for each tissue and modality. (C,D) Two *IL2RA* cis-eQTL signals colocalize with two albumin/globulin ratio GWAS signals. Marginal p-values are displayed in (C), and log Bayes factors for each of the two colocalizing effects, represented by the two colors, are displayed in (C); for the eQTL panel, the sign of each variant reflects the direction of effect on the gene’s expression for the GWAS trait-increasing allele in the colocalizing GWAS effect. (E,F) Three *HK1* cis-eQTL signals colocalize with three mean corpuscular volume GWAS signals.

See this image and copyright information in PMC

References

1. Aguet F., Brown A. A., Castel S. E., Davis J. R., He Y., Jo B., Mohammadi P., Park Y., Parsana P., Segrè A. V., Strober B. J., Zappala Z., Cummings B. B., Gelfand E. T., Hadley K., Huang K. H., Lek M., Li X., Nedzel J. L., … Zhu J. (2017). Genetic effects on gene expression across human tissues. Nature, 550(7675), 204–213. 10.1038/nature24277 - DOI - PMC - PubMed
1. Araujo D. S., Nguyen C., Hu X., Mikhaylova A. V., Gignoux C., Ardlie K., Taylor K. D., Durda P., Liu Y., Papanicolaou G., Cho M. H., Rich S. S., Rotter J. I., NHLBI TOPMed Consortium, Im H. K., Manichaikul A., & Wheeler H. E. (2023). Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations. HGG Advances, 4(4), 100216. 10.1016/j.xhgg.2023.100216 - DOI - PMC - PubMed
1. Asai Y., Eslami A., van Ginkel C. D., Akhabir L., Wan M., Ellis G., Ben-Shoshan M., Martino D., Ferreira M. A., Allen K., Mazer B., de Groot H., de Jong N. W., Gerth van Wijk R. N., Dubois A. E. J., Chin R., Cheuk S., Hoffman J., Jorgensen E., … Daley D. (2018). Genome-wide association study and meta-analysis in multiple populations identifies new loci for peanut allergy and establishes C11orf30/EMSY as a genetic risk factor for food allergy. The Journal of Allergy and Clinical Immunology, 141(3), 991–1001. 10.1016/j.jaci.2017.09.015 - DOI - PubMed
1. Astle W. J., Elding H., Jiang T., Allen D., Ruklisa D., Mann A. L., Mead D., Bouman H., Riveros-Mckay F., Kostadima M. A., Lambourne J. J., Sivapalaratnam S., Downes K., Kundu K., Bomba L., Berentsen K., Bradley J. R., Daugherty L. C., Delaneau O., … Soranzo N. (2016). The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease. Cell, 167(5), 1415–1429.e19. 10.1016/j.cell.2016.10.042 - DOI - PMC - PubMed
1. Brown A. A., Fernandez-Tajes J. J., Hong M., Brorsson C. A., Koivula R. W., Davtian D., Dupuis T., Sartori A., Michalettou T.-D., Forgie I. M., Adam J., Allin K. H., Caiazzo R., Cederberg H., De Masi F., Elders P. J. M., Giordano G. N., Haid M., Hansen T., … Viñuela A. (2023). Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits. Nature Communications, 14(1), Article 1. 10.1038/s41467-023-40569-3 - DOI - PMC - PubMed

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

This is a preprint.

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

Affiliations

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

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

Publication types

Grants and funding

LinkOut - more resources

Full Text Sources