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Genetics of varicose veins reveals polygenic architecture and genetic overlap with arterial and venous disease

Nature Cardiovascular Research volume 2, pages 44–57 (2023)Cite this article

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Abstract

Varicose veins represent a common cause of cardiovascular morbidity, with limited available medical therapies. Although varicose veins are heritable and epidemiologic studies have identified several candidate varicose vein risk factors, the molecular and genetic basis remains uncertain. Here we analyzed the contribution of common genetic variants to varicose veins using data from the Veterans Affairs Million Veteran Program and four other large biobanks. Among 49,765 individuals with varicose veins and 1,334,301 disease-free controls, we identified 139 risk loci. We identified genetic overlap between varicose veins, other vascular diseases and dozens of anthropometric factors. Using Mendelian randomization, we prioritized therapeutic targets via integration of proteomic and transcriptomic data. Finally, topological enrichment analyses confirmed the biologic roles of endothelial shear flow disruption, inflammation, vascular remodeling and angiogenesis. These findings may facilitate future efforts to develop nonsurgical therapies for varicose veins.

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Fig. 1: Study design.
Fig. 2: Phenome-wide associations of varicose veins risk variants.
Fig. 3: Bayesian colocalization reveals shared genetic mechanisms.
Fig. 4: Shared heritability of anthropometric and vascular traits.
Fig. 5: Proteome-wide MR.
Fig. 6: Drug-repurposing MR.
Fig. 7: Systems-biology integration of genome-wide significant variants contributing to varicose vein pathophysiology.

Data availability

Full GWAS summary statistics from MVP can be found in dbGaP (https://www.ncbi.nlm.nih.gov/gap/) under the MVP accession (phs001672). GWAS summary statistics from FinnGen (release 4), BioBank Japan and UK Biobank are publicly available from https://www.finngen.fi/en/access_results, https://pheweb.jp/ and https://pan.ukbb.broadinstitute.org. The following datasets were used in the Topological Enrichment Analyses: GEO: GSE121520, GSE201376, GSE53998, GSE29611, GSE60156, GSE41166, GSE31838, GSE31477, GSE33213, GSE33213, GSE43786, GSE50144. GTEx v.8 RNA-seq datasets were downloaded from the GTEx Portal: https://gtexportal.org/.

Code availability

Publicly available software was used to perform analyses.

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Acknowledgements

We thank the participants of the VA Million Veteran Program, UK Biobank, eMERGE, FinnGen and BioBank Japan studies. This research is based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration, and was supported by an award from the Computational Health Analytics for Medical Precision to Improve Outcomes Now (CHAMPION) initiative to S.M.D., P.S.T. and R.K.M. This publication does not represent the views of the Department of Veteran Affairs or the US Government. M.G.L. is supported by the Institute for Translational Medicine and Therapeutics of the Perelman School of Medicine at the University of Pennsylvania and the NIH/NHLBI National Research Service Award postdoctoral fellowship (grant no. T32HL007843). S.M.D. is supported by the US Department of Veterans Affairs award no. IK2-CX001780. This publication does not represent the views of the Department of Veterans Affairs or the US Government. D.A.J., K.A.S., D.K., M.R.G., M.L., M.C. and J.I.M. are supported by funding from the Million Veteran Program Computational Health Analytics for Medical Precision to Improve Outcomes Now (CHAMPION) initiative and NIH grants no. DA051908 and no. DA051913. eMERGE is supported by grant no. U01HG8657 (University of Washington); grant no. U01HG8685 (Brigham and Women’s Hospital); grant no. U01HG8672 (Vanderbilt University Medical Center); grant no. U01HG8666 (Cincinnati Children’s Hospital Medical Center); grant no. U01HG6379 (Mayo Clinic); grant no. U01HG8679 (Geisinger Clinic); grant no. U01HG8680 (Columbia University Health Sciences); grant no. U01HG8684 (Children’s Hospital of Philadelphia); grant no. U01HG8673 (Northwestern University); grant no. U01HG8701 (Vanderbilt University Medical Center serving as the Coordinating Center); grant no. U01HG8676 (Partners Healthcare/Broad Institute); and grant no. U01HG8664 (Baylor College of Medicine). This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract no. DE-AC05-00OR22725. This manuscript has been coauthored by UT-Battelle, LLC under contract no. DE-AC05-00OR22725 with the US Department of Energy. The US Government retains, and the publisher, by accepting the article for publication, acknowledges that the US Government retains, a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan, last accessed 16 September 2020). E.A.J. is supported by the Building Interdisciplinary Research Careers in Women’s Health (BIRCWH) career development program (grant no. K12HD043483). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Division of Cardiovascular Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Michael G. Levin

  2. Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA

    Michael G. Levin, Anurag Verma & Scott M. Damrauer

  3. Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA

    Jennifer E. Huffman

  4. Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Anurag Verma, Marylyn D. Ritchie & Scott M. Damrauer

  5. Computational and Predictive Biology, Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Kyle A. Sullivan, David Kainer, Michael R. Garvin, Mikaela Cashman, J. Izaak Miller & Daniel A. Jacobson

  6. Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA

    Alexis A. Rodriguez & Ravi K. Madduri

  7. Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN, USA

    Matthew Lane

  8. Department of Genetics, University of North Carolina, Chapel Hill, NC, USA

    Hyejung Won

  9. Department of Biomedical Data Science, Stanford University, Stanford, CA, USA

    Binglan Li

  10. Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

    Yuan Luo

  11. Departments of Medicine (Division of Medical Genetics) and Genome Sciences, University of Washington Medical Center, Seattle, WA, USA

    Gail P. Jarvik

  12. Center for Applied Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Hakon Hakonarson

  13. Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN, USA

    Elizabeth A. Jasper

  14. Division of Genetic Medicine, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA

    Alexander G. Bick

  15. VA Palo Alto Health Care System, Palo Alto, CA, USA

    Philip S. Tsao

  16. Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Philip S. Tsao

  17. Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Marylyn D. Ritchie

  18. Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Scott M. Damrauer

Authors
  1. Michael G. Levin
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  22. Scott M. Damrauer
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Contributions

M.G.L., J.E.H., A.V., K.A.S., A.G.B., D.A.J., R.K.M. and S.M.D. designed the study. M.G.L., J.E.H., A.V., K.A.S., A.A.R., D.K., H.W., M.R.G., M.L., M.C. and J.I.M. performed analyses. M.G.L., J.E.H., A.V., A.A.R., A.G.B., R.K.M., P.S.T. and S.M.D. contributed data from MVP. A.V., B.L., Y.L., G.P.J., H.H., E.A.J. and M.D.R. contributed data from eMERGE. M.G.L. drafted the manuscript, and all authors provided critical revisions.

Corresponding author

Correspondence to Scott M. Damrauer.

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Competing interests

S.M.D. receives research support from RenalytixAI and personal consulting fees from Calico Labs, both outside the scope of the current manuscript. S.M.D. is named as a coinventor on a Government-owned US Patent application (US20210113536A1) related to the use of genetic risk prediction for venous thromboembolic disease filed by the US Department of Veterans Affairs in accordance with Federal regulatory requirements, outside the scope of the current manuscript. S.M.D. is named as a coinventor on a Government-owned US Patent application (US20210285050A1) related to the use of PDE3B inhibition for preventing cardiovascular disease filed by the US Department of Veterans Affairs in accordance with Federal regulatory requirements, outside the scope of the current manuscript. The remaining authors declare no competing interests.

Peer review

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Nature Cardiovascular Research thanks Dipender Gill and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Effect Size and Minor Allele Frequency for Novel and Previously Reported Risk Loci.

Absolute effect size after fixed-effects meta-analysis plotted as a function of minor allele frequency for all novel genome-wide significant loci (p < 5 × 10−8), and the 30/36 previously reported loci with p < 0.05 in the current meta-analysis.

Extended Data Fig. 2 Ancestry-Specific Heritability Estimates.

LD-score regression was used to estimate ancestry-specific liability-scale heritability, using ancestry-specific LD scores from Pan UK Biobank (https://pan.ukbb.broadinstitute.org/docs/ld). Bars represent 95% confidence intervals (95% CI).

Extended Data Fig. 3 Mediated Effects of Candidate Varicose Vein Risk Loci.

Genomic Structural Equation Modeling was used to estimate the direct and indirect effects of rs62033413 (located near FTO) and rs6025 (Factor V Leiden) on Varicose Veins after accounting for the effects of each variant on BMI and venous thromboembolism (VTE), respectively. Error bars represent 95% Confidence Intervals (95% CI).

Extended Data Fig. 4 MAGMA Tissue and Gene Set Enrichment.

a) Tissue enrichment was assessed using MAGMA via the online FUMA platform, integrating GWAS summary statistics for varicose veins with gene expression (RNAseq) data from GTEx V8. b) Gene set enrichment results from MAGMA. Tissues (panel A) enriched at a false discovery rate threshold of 5% are noted by red bars. Pathways (panel B) enriched at a Bonferroni-adjusted p < 0.05 points are noted by red dots.

Extended Data Fig. 5 Genetic Correlation Between Varicose Veins and Vascular Traits.

Genetic correlations between varicose veins and other vascular traits were estimated using cross-trait LDSC. Varicose veins were significantly correlated with VTE (venous thromboembolism), PAD (peripheral artery disease), and AAA (abdominal aortic aneurysm), but not STROKE (any ischemic stroke) or CAD (coronary artery disease. Error bars represent 95% Confidence Intervals (95% CI).

Extended Data Fig. 6 Multi-Trait Colocalization.

Multi-trait colocalization was performed across all metabolites, proteins, or UK Biobank traits with shared associations with varicose veins at a given locus. Loci are labeled by chromosome, and each locus is denoted by the sentinel variant from the varicose veins GWAS meta-analysis. The size of points corresponds to the number of colocalizing traits.

Extended Data Fig. 7 Random Walk with Restart-based Topological Gene Enrichment of Genome-wide Significant Varicose Veins Variants.

a) Workflow of cell-type specific SNP-to-gene assignment and gene set enrichment analysis. Hi-C contact maps were generated from human umbilical vein endothelial cells (HUVECs) and human coronary artery smooth muscle cells (HCASMCs) and the intersecting H-MAGMA assigned genes from HUVEC and HCASMC Hi-C data sets. b) Varicose veins GWAS genes are more topologically enriched for GO terms related to known cardiovascular processes compared to other GO terms based on random walk with restart (RWR) ranking of genes associated with each GO term. Panel A created using BioRender.com.

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Levin, M.G., Huffman, J.E., Verma, A. et al. Genetics of varicose veins reveals polygenic architecture and genetic overlap with arterial and venous disease. Nat Cardiovasc Res 2, 44–57 (2023). https://doi.org/10.1038/s44161-022-00196-5

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