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Epigenetic and proteomic signatures associate with clonal hematopoiesis expansion rate

Nature Aging volume 4, pages 1043–1052 (2024)Cite this article

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Abstract

Clonal hematopoiesis of indeterminate potential (CHIP), whereby somatic mutations in hematopoietic stem cells confer a selective advantage and drive clonal expansion, not only correlates with age but also confers increased risk of morbidity and mortality. Here, we leverage genetically predicted traits to identify factors that determine CHIP clonal expansion rate. We used the passenger-approximated clonal expansion rate method to quantify the clonal expansion rate for 4,370 individuals in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) cohort and calculated polygenic risk scores for DNA methylation aging, inflammation-related measures and circulating protein levels. Clonal expansion rate was significantly associated with both genetically predicted and measured epigenetic clocks. No associations were identified with inflammation-related lab values or diseases and CHIP expansion rate overall. A proteome-wide search identified predicted circulating levels of myeloid zinc finger 1 and anti-Müllerian hormone as associated with an increased CHIP clonal expansion rate and tissue inhibitor of metalloproteinase 1 and glycine N-methyltransferase as associated with decreased CHIP clonal expansion rate. Together, our findings identify epigenetic and proteomic patterns associated with the rate of hematopoietic clonal expansion.

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Fig. 1: A schematic representation of the study design.
Fig. 2: Linear regression analyses of clonal expansion rate with DNA methylation aging measures.
Fig. 3: Linear regressions of clonal expansion rate with inflammation.
Fig. 4: Linear regressions of clonal expansion rate with predicted protein levels.

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Data availability

WGS data for individuals in the TOPMed cohort and the CHIP variant call sets are available through restricted access from the dbGaP TOPMed Exchange Area available to TOPMed investigators to protect patient privacy. Individuals who want to apply for access to TOPMed data should follow the steps listed online (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/about.html#request-controlled). Summary statistics for the measures of DNA methylation aging are available at https://datashare.ed.ac.uk/handle/10283/3645 and summary statistics for the four significant proteins are available at www.omicspred.org. All other data supporting the findings of the study are available from the authors upon request.

Code availability

Code developed to call passenger mutations using the Mutect2 WDL pipeline is available at https://dockstore.org/workflows/github.com/broadinstitute/gatk/mutect2:4.1.8.1?tab=info. Code on passenger variant filtering and quality control is available at https://github.com/weinstockj/passenger_count_variant_calling. Code to calculate PRSs is available for PRScs at https://github.com/getian107/PRScs and PRScsx at https://github.com/getian107/PRScsx.

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Acknowledgements

WGS for the TOPMed program was supported by the NHLBI. Centralized read mapping and genotype calling, along with variant quality metrics and filtering, were provided by the TOPMed Informatics Research Center (3R01HL-117626-02S1; contract HHSN268201800002I). Phenotype harmonization, data management, sample-identity quality control and general study coordination were provided by the TOPMed Data Coordinating Center (R01HL-120393; U01HL-120393; contract HHSN268201800001I). We thank the study participants who provided biological samples and data for TOPMed. The full study-specific acknowledgments are included in the Supplementary Acknowledgements. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the NHLBI, the National Institutes of Health or the US Department of Health and Human Services. S.J. is supported by the Burroughs Wellcome Foundation Career Award for Medical Scientists, Foundation Leducq (TNE-18CVD04), the Ludwig Center for Cancer Stem Cell Research, the American Society of Hematology Scholar Award, the NIH Director’s New Innovator Award (DP2-HL157540) and a Leukemia and Lymphoma Society Discovery Grant. A.G.B. is supported by a Burroughs Wellcome Foundation Career Award for Medical Scientists, the NIH Director’s Early Independence Award (DP5-OD029586) and the Pew-Stewart Scholar for Cancer Research award, supported by the Pew Charitable Trusts and the Alexander and Margaret Stewart Trust.

Author information

Author notes

  1. These authors contributed equally: Taralynn M. Mack, Michael A. Raddatz.

Authors and Affiliations

  1. Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA

    Taralynn M. Mack, Michael A. Raddatz, Yash Pershad & Alexander G. Bick

  2. Department of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Michael A. Raddatz

  3. Department of Pathology, Stanford University, Stanford, CA, USA

    Daniel C. Nachun & Siddhartha Jaiswal

  4. 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

    Kent D. Taylor, Xiuqing Guo & Jerome I. Rotter

  5. Department of Medicine, University of Maryland, Baltimore, Baltimore, MD, USA

    Alan R. Shuldiner & Jeffrey R. O’Connell

  6. Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Eimear E. Kenny

  7. The Charles Bronfman Institute of Personalized Medicine, Mount Sinai Hospital, New York City, NY, USA

    Ruth J. F. Loos

  8. The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Ruth J. F. Loos

  9. Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    Susan Redline & Brian E. Cade

  10. Harvard Medical School, Boston, MA, USA

    Susan Redline & Brian E. Cade

  11. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Brian E. Cade

  12. Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA

    Bruce M. Psaty, Joshua C. Bis, Jennifer A. Brody & Kerri L. Wiggins

  13. Department of Epidemiology, University of Washington, Seattle, WA, USA

    Bruce M. Psaty, Susan R. Heckbert & Nicholas L. Smith

  14. Department of Medicine, University of Washington, Seattle, WA, USA

    Bruce M. Psaty

  15. Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA

    Bruce M. Psaty

  16. Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    Edwin K. Silverman, Jeong H. Yun, Michael H. Cho & Dawn L. DeMeo

  17. National Heart, Lung and Blood Institute, Population Sciences Branch, Framingham, MA, USA

    Daniel Levy & Andrew D. Johnson

  18. Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Rasika A. Mathias & Lisa R. Yanek

  19. Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA

    Susan R. Heckbert & Nicholas L. Smith

  20. Seattle Epidemiologic Research and Information Center, Department of Veterans Affairs Office of Research and Development, Seattle, WA, USA

    Nicholas L. Smith

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

    Laura M. Raffield

  22. Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA

    April P. Carson

  23. Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA

    Stephen S. Rich & Ani W. Manichaikul

  24. Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA

    C. Charles Gu

  25. Medical Genetics Translational Genomics and Population Sciences (TGPS), Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Torrance, CA, USA

    Yii-Der Ida Chen

  26. Department of Medical Research, Taichung Veterans General Hospital, Taichung City, Taiwan

    Wen-Jane Lee

  27. Division of Cardiology, Vanderbilt University Medical Center, Nashville, TN, USA

    M. Benjamin Shoemaker

  28. Departments of Medicine, Pharmacology, and Biomedical Informatics, Vanderbilt University, Nashville, TN, USA

    Dan M. Roden

  29. Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Charles Kooperberg & Alexander P. Reiner

  30. Division of Biostatistics, Institute for Health and Equity, and Cancer Center, Medical College of Wisconsin, Milwaukee, WI, USA

    Paul L. Auer

  31. Division of Hematology and Oncology, Weill Cornell Medicine, New York, NY, USA

    Pinkal Desai

  32. Englander Institute of Precision Medicine, Weill Cornell Medicine, New York, NY, USA

    Pinkal Desai

  33. Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA

    Thomas W. Blackwell, Albert V. Smith & Joshua S. Weinstock

  34. Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

    Alexander G. Bick

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  1. Taralynn M. Mack
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  2. Michael A. Raddatz
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Contributions

T.M.M. and M.A.R. conceptualized and designed the study, performed the analyses and wrote the manuscript. Y.P. performed analyses. A.G.B., J.S.W. and S.J. conceptualized PACER and contributed to the study design. J.S.W. performed somatic variant calling. A.G.B. supervised the work and contributed to the drafting of the manuscript. D.C.N., K.D.T., X.G., A.R.S., J.R.O., E.E.K., R.J.F.L., S.R., B.E.C., B.M.P., J.C.B., J.A.B., E.K.S., J.H.Y., M.H.C., D.L.D., D.L., A.D.J., R.A.M., L.R.Y., S.R.H., N.L.S., K.L.W., L.M.R., A.P.C., J.I.R., S.S.R., A.W.M., C.C.G., Y.I.C., W.L., M.B.S., D.R., C.K., P.L.A., P.D., T.W.B., A.V.S., A.P.R. and the NHLBI TOPMed Consortium contributed to sequencing and phenotyping of the included NHLBI TOPMed cohorts. All authors read, revised and approved the manuscript.

Corresponding author

Correspondence to Alexander G. Bick.

Ethics declarations

Competing interests

S.J. is on advisory boards for Novartis, AVRO Bio and Roche Genentech, reports speaking fees and honorarium from GSK and is on the scientific advisory board of Bitterroot Bio. P.N. reports grants support from Amgen, AstraZeneca, Apple, Novartis and Boston Scientific, is a paid consultant for Apple, AstraZeneca, Novartis, Genentech and Blackstone Life Sciences and has spousal employment at Vertex, all unrelated to the present work. S.J., A.G.B. and P.N. are paid consultants for Foresite Labs and cofounders, equity holders and scientific advisory board members of TenSixteen Bio. Stanford University has filed a patent application for the use of PACER to identify therapeutic targets, on which S.J., A.G.B. and J.S.W. are listed as inventors (US patent 63/141,333). The patent has been licensed to TenSixteen Bio. A.S. is an employee of Regeneron Pharmaceuticals. B.M.P. serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson and Johnson. E.S. receives grant support from Bayer and GSK. J.Y. and D.D. receive grant support from Bayer. M.C. receives grant support from Bayer and GSK and consulting and speaking fees from Illumina and AstraZeneca. S.S.R. and L.M.R. are paid consultants for Westat, the Administrative Coordinating Center for the NHLBI TOPMed program. The remaining authors declare no competing interests.

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Nature Aging thanks Siddhartha Kar, Jennifer Kwan and Kristina Kirschner for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Description of CHIP cohort characteristics.

A density plot displaying the distribution of driver gene VAF across the cohort stratified by driver gene as well as the overall driver gene count across the cohort.

Extended Data Fig. 2 Epigenetic aging correlation measures.

Correlation in methylation measures. (a) Correlation matrix within measured epigenetic aging data. (b) Correlation matrix within predicted epigenetic aging data. (c) Box plot showing correlation between calculated PRS (n = 297 individuals) (grouped into quartiles) and z-scored measured methylation data. Color denotes a significant association between measured and PRS data. The center line is the median value, with the bounds of the box representing the first through third quartiles and the whiskers representing the minimum and maximum while any remaining points are outliers.

Extended Data Fig. 3 Full regression results for inflammation analyses.

Linear regressions of clonal expansion rate with inflammation (full results). (a) Heat map showing associations between clonal expansion rate and PRS for inflammation-related proteins from the Somalogic database stratified by CHIP gene. Significance and direction of effect are denoted by the color of the box. (b) Heat map showing associations between clonal growth and PRS for inflammation-related phenotypes. Significance and direction of effect are denoted by the color of the box.

Extended Data Fig. 4 Gene-specific circulating protein regression results.

Linear regressions of clonal expansion rate with predicted protein levels in CHIP gene-specific analyses. Volcano plots showing significantly associated protein PRS from the Somalogic database. Colored points are statistically significant. The color of the point shows the direction of the effect (blue = negative, red = positive). To account for multiple testing, we used a Bonferroni corrected p-value of 2.3 × 10−5. (a) DNMT3A-specific CHIP (b) TET2-specific CHIP (c) SRSF2/SF3B1/U2AF1-specific CHIP.

Extended Data Fig. 5 Sex-stratified circulating protein regression results.

Linear regression analyses of clonal expansion rate with Somalogic protein PRS stratified by biological sex (n = 4,370 individuals). To account for multiple testing, we used a Bonferroni corrected p-value of 0.01.

Extended Data Fig. 6 Full results of GSEA analyses.

Hallmark pathways in Gene Set Enrichment Analysis (GSEA). (a) Heat map showing the pathways most implicated in both overall CHIP and gene-specific CHIP. Color denotes the direction of the association. GSEA is a statistical functional enrichment analysis. (b) Protein PRSs contributing to the hallmark pathways most strongly associated with clonal expansion rate. Color denotes the direction of the association.

Extended Data Fig. 7 Gene-specific GSEA results.

Gene Set Enrichment Analysis (GSEA) results for CHIP gene-specific analyses. (a) Pathways implicated in the associations between clonal expansion rate and circulating protein PRS in DNMT3A-specific CHIP. (b) TET2-specific CHIP. (c) Pearson’s correlation test between pathways implicated in DNMT3A & TET2-specific CHIP. The translucent error bars around the linear regression line represent 95% confidence intervals.

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Mack, T.M., Raddatz, M.A., Pershad, Y. et al. Epigenetic and proteomic signatures associate with clonal hematopoiesis expansion rate. Nat Aging 4, 1043–1052 (2024). https://doi.org/10.1038/s43587-024-00647-7

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