. 2025 Oct 16;16(1):9194.

doi: 10.1038/s41467-025-64236-x.

Genetic determinants and genomic consequences of non-leukemogenic somatic point mutations

Joshua S Weinstock¹, Sharjeel A Chaudhry^{2

3}, Maria Ioannou⁴, Maria Viskadourou², Paula Reventun², Yasminka A Jakubek⁵, L Alexander Liggett⁶, Cecelia Laurie⁷, Jai G Broome⁸, Alyna Khan⁷, Kent D Taylor⁹, Xiuqing Guo⁹, Patricia A Peyser¹⁰, Eric Boerwinkle¹¹, Nathalie Chami^{12

13}, Eimear E Kenny¹⁴, Ruth J Loos^{12

13}, Bruce M Psaty^{15

16

17}, Russell P Tracy¹⁸, Jennifer A Brody¹⁵, Jeong H Yun¹⁹, Michael H Cho²⁰, Ramachandran S Vasan²¹, Sharon L Kardia²², Jennifer A Smith^{22

23}, Laura M Raffield²⁴, Aurelian Bidulescu²⁵, Emily C O'Brien²⁶, Mariza de Andrade²⁷, Jerome I Rotter⁹, Stephen S Rich²⁸, Russell P Tracy¹⁸, Yii Der Ida Chen⁹, C Charles Gu²⁹, Chao A Hsiung³⁰, Charles Kooperberg³¹, Bernhard Haring^{32

33}, Rami Nassir³⁴, Rasika Mathias³⁵, Alex Reiner³¹, Vijay G Sankaran^{6

36}, Charles J Lowenstein³⁷, Thomas W Blackwell³⁸, Goncalo R Abecasis^{38

39}, Albert V Smith³⁸, Hyun M Kang³⁸, Pradeep Natarajan^{40

41

42}, Siddhartha Jaiswal⁴³, Alexander Bick⁴⁴, Wendy S Post³⁷, Paul Scheet⁴⁵, Paul Auer⁴⁶, Theodoros Karantanos⁴, Alexis Battle^{47

48

49}, Marios Arvanitis^{50

51}

Affiliations

¹ Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA, USA. josh.weinstock@emory.edu.
² Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD, USA.
³ Department of Surgery, Division of Vascular and Endovascular Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
⁴ Division of Hematological Malignancies, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
⁵ Department of Internal Medicine, University of Kentucky, Lexington, KY, USA.
⁶ Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
⁷ Department of Biostatistics, University of Washington, Seattle, WA, USA.
⁸ Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA.
⁹ 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.
¹⁰ Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
¹¹ Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
¹² The Charles Bronfman Institute of Personalized Medicine, New York, NY, USA.
¹³ The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
¹⁴ Institute for Genomic Health, York, NY, USA.
¹⁵ Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA.
¹⁶ Department of Epidemiology, University of Washington, Seattle, WA, USA.
¹⁷ Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA.
¹⁸ Department of Pathology & Laboratory Medicine and Biochemistry, Larner College of Medicine at the University of Vermont, Colchester, VT, USA.
¹⁹ Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA.
²⁰ Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA.
²¹ National Heart Lung and Blood Institute's, Boston University's Framingham Heart Study, Framingham, MA, USA.
²² Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA.
²³ Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA.
²⁴ Department of Genetics, University of North Carolina, Chapel Hill, NC, USA.
²⁵ Department of Epidemiology and Biostatistics, Indiana University School of Public Health - Bloomington, Bloomington, IN, USA.
²⁶ Duke Clinical Research Institute, Durham, NC, USA.
²⁷ Mayo Clinic, Department of Health Sciences Research, Rochester, MN, USA.
²⁸ Department of Public Health Sciences, Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
²⁹ Center for Biostatistics and Data Sciences, Washington University, St. Louis, MO, USA.
³⁰ Department of Medicine, Taipei Veterans General Hospital, Taipei Taiwan, Taipei, Taiwan.
³¹ Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
³² Department of Medicine III, Saarland University Hospital, Homburg, Saarland, Germany; Department of Medicine I, University of Würzburg, Würzburg, Bavaria, Germany.
³³ Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA.
³⁴ University of California Davis, Davis, CA, USA.
³⁵ Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
³⁶ Howard Hughes Medical Institute, Boston, MA, USA.
³⁷ Department of Medicine, Cardiology Division, Johns Hopkins University, Baltimore, MD, USA.
³⁸ Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
³⁹ Regeneron Pharmaceuticals, Tarrytown, NY, USA.
⁴⁰ Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
⁴¹ Program in Medical and Population Genetics, Broad Institute of Harvard & MIT, Cambridge, MA, USA.
⁴² Department of Medicine, Harvard Medical School, Boston, MA, USA.
⁴³ Department of Pathology, Stanford University, Stanford, CA, USA.
⁴⁴ Division of Genetic Medicine, Department of Medicine, Vanderbilt University, Nashville, TN, USA.
⁴⁵ Department of Epidemiology, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA.
⁴⁶ Department of Biostatistics, Medical College of Wisconsin, Division of Biostatistics, Institute for Health and Equity, and Cancer Center, Medical College of Wisconsin, Milwaukee, WI, USA.
⁴⁷ Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA. ajbattle@jhu.edu.
⁴⁸ Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA. ajbattle@jhu.edu.
⁴⁹ Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA. ajbattle@jhu.edu.
⁵⁰ Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD, USA. marvani1@jhmi.edu.
⁵¹ Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA. marvani1@jhmi.edu.

PMID: 41102182
PMCID: PMC12532822
DOI: 10.1038/s41467-025-64236-x

Genetic determinants and genomic consequences of non-leukemogenic somatic point mutations

Joshua S Weinstock et al. Nat Commun. 2025.

. 2025 Oct 16;16(1):9194.

doi: 10.1038/s41467-025-64236-x.

Authors

Affiliations

¹ Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA, USA. josh.weinstock@emory.edu.
² Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD, USA.
³ Department of Surgery, Division of Vascular and Endovascular Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
⁴ Division of Hematological Malignancies, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
⁵ Department of Internal Medicine, University of Kentucky, Lexington, KY, USA.
⁶ Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
⁷ Department of Biostatistics, University of Washington, Seattle, WA, USA.
⁸ Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA.
⁹ 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.
¹⁰ Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
¹¹ Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
¹² The Charles Bronfman Institute of Personalized Medicine, New York, NY, USA.
¹³ The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
¹⁴ Institute for Genomic Health, York, NY, USA.
¹⁵ Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA.
¹⁶ Department of Epidemiology, University of Washington, Seattle, WA, USA.
¹⁷ Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA.
¹⁸ Department of Pathology & Laboratory Medicine and Biochemistry, Larner College of Medicine at the University of Vermont, Colchester, VT, USA.
¹⁹ Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA.
²⁰ Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA.
²¹ National Heart Lung and Blood Institute's, Boston University's Framingham Heart Study, Framingham, MA, USA.
²² Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA.
²³ Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA.
²⁴ Department of Genetics, University of North Carolina, Chapel Hill, NC, USA.
²⁵ Department of Epidemiology and Biostatistics, Indiana University School of Public Health - Bloomington, Bloomington, IN, USA.
²⁶ Duke Clinical Research Institute, Durham, NC, USA.
²⁷ Mayo Clinic, Department of Health Sciences Research, Rochester, MN, USA.
²⁸ Department of Public Health Sciences, Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
²⁹ Center for Biostatistics and Data Sciences, Washington University, St. Louis, MO, USA.
³⁰ Department of Medicine, Taipei Veterans General Hospital, Taipei Taiwan, Taipei, Taiwan.
³¹ Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
³² Department of Medicine III, Saarland University Hospital, Homburg, Saarland, Germany; Department of Medicine I, University of Würzburg, Würzburg, Bavaria, Germany.
³³ Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA.
³⁴ University of California Davis, Davis, CA, USA.
³⁵ Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
³⁶ Howard Hughes Medical Institute, Boston, MA, USA.
³⁷ Department of Medicine, Cardiology Division, Johns Hopkins University, Baltimore, MD, USA.
³⁸ Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
³⁹ Regeneron Pharmaceuticals, Tarrytown, NY, USA.
⁴⁰ Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
⁴¹ Program in Medical and Population Genetics, Broad Institute of Harvard & MIT, Cambridge, MA, USA.
⁴² Department of Medicine, Harvard Medical School, Boston, MA, USA.
⁴³ Department of Pathology, Stanford University, Stanford, CA, USA.
⁴⁴ Division of Genetic Medicine, Department of Medicine, Vanderbilt University, Nashville, TN, USA.
⁴⁵ Department of Epidemiology, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA.
⁴⁶ Department of Biostatistics, Medical College of Wisconsin, Division of Biostatistics, Institute for Health and Equity, and Cancer Center, Medical College of Wisconsin, Milwaukee, WI, USA.
⁴⁷ Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA. ajbattle@jhu.edu.
⁴⁸ Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA. ajbattle@jhu.edu.
⁴⁹ Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA. ajbattle@jhu.edu.
⁵⁰ Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD, USA. marvani1@jhmi.edu.
⁵¹ Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA. marvani1@jhmi.edu.

PMID: 41102182
PMCID: PMC12532822
DOI: 10.1038/s41467-025-64236-x

Abstract

Clonal hematopoiesis (CH) is defined by the expansion of a lineage of genetically identical cells in blood. Genetic lesions that confer a fitness advantage, such as leukemogenic point mutations or mosaic chromosomal alterations (mCAs), are frequent mediators of CH. However, recent analyses of both single cell-derived colonies of hematopoietic cells and population sequencing cohorts have revealed CH frequently occurs in the absence of known driver genetic lesions. To characterize CH without known driver genetic lesions, we use 51,399 deeply sequenced whole genomes from the NHLBI TOPMed sequencing initiative to perform simultaneous germline and somatic mutation analyses among individuals without leukemogenic point mutations (LPM), which we term CH-LPMneg. We quantify CH by estimating the total mutation burden. Because estimating somatic mutation burden without a paired-tissue sample is challenging, we develop a novel statistical method, the Genomic and Epigenomic informed Mutation (GEM) rate, that uses external genomic and epigenomic data sources to distinguish artifactual signals from true somatic mutations. We perform a genome-wide association study of GEM to discover the germline determinants of CH-LPMneg. We identify seven genes associated with CH-LPMneg (TCL1A, TERT, SMC4, NRIP1, PRDM16, MSRA, SCARB1).Functional analyses of SMC4 and NRIP1 implicated altered hematopoietic stem cell self-renewal and proliferation as the primary mediator of mutation burden in blood. We then perform comprehensive multi-tissue transcriptomic analyses, finding that the expression levels of 404 genes are associated with GEM. Finally, we perform phenotypic association meta-analyses across four cohorts, finding that GEM is associated with increased white blood cell count, but is not significantly associated with incident stroke or coronary disease events. Overall, we develop GEM for quantifying mutation burden from WGS and use GEM to discover the genetic, genomic, and phenotypic correlates of CH-LPMneg.

PubMed Disclaimer

Conflict of interest statement

Competing interests: L.M.R. is a consultant for the TOPMed Administrative Coordinating Center (through Westat). B.M.P. serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. J.Y. reports grant support from Bayer. M.C. reports grant support from Bayer and GSK, Consulting and speaking fees from Illumina and AstraZeneca. A.G.B., P.N., and S.J. are cofounders, equity holders, and on the scientific advisory board of TenSixteen Bio. G.R.A. is an employee of Regeneron Pharmaceuticals and receives a salary, stock and stock options as compensation. A.B. is a co-founder and equity holder of CellCipher, Inc., a stockholder in Alphabet, Inc. The remaining authors declare no competing interests.

Figures

**Fig. 1. Study overview.**
Schematic, describing the development of GEM (top panel), the use of GEM to discover the genetic determinants of mutation burden in blood (middle panel), and the use of GEM to identify the transcriptomic and clinical correlates of mutation burden in blood (bottom panel).

**Fig. 2. Development of GEM.**
A The Spearman correlation between mutation burden and chronological age stratified by chromHMM annotations in CD34 + cells. B The Spearman correlation between mutation burden and chronological age stratified by functional consequence as annotated by the variant effect predictor (VEP). C The Spearman correlation between mutation burden and chronological age, stratified by quintiles of CADD scores. D Plate annotation for the GEM statistical model. θ_0 and are intercepts; θ_1reflects the association between the log2-transformed value of ∑z_ij and chronological age Y_i; z_ij denotes the probability that the jth mutation in the ith individual is a true somatic mutation. X is a matrix of annotations.

**Fig. 3. The genetic determinants of GEM.**
A The GWAS of GEM. Summary statistics were estimated with SAIGE, λ^gc = 1.0. B Fine-mapping of the TCL1A locus. Note rs11846938 is 10 bp from rs2887399. Fine-mapping was performed with SuSIE.

**Fig. 4. The consequence of SMC4 and NRIP1 knock down on HSC self-renewal.**
A SMC4 and NRIP1 were knocked-down with shRNA, and the proportion of CD34 + cells was quantified with FACs. Quantities were compared referent to a non-targeting control. B proportion of CD34 + CD38- was quantified with FACS. C Number of colonies formed in a colony-forming unit (CFU) assay. D Number of colonies in a burst-forming unit assay. A linear model was used to estimate effect sizes across technical replicates (n = 4). Error bars indicate 95% confidence intervals.

**Fig. 5. The sex specific genetic determinants of mutation burden.**
A Regressions were performed for each quantile-transformed somatic principal component (sPC) on study and sex as covariates. The partial variance explained by sex is displayed on the y-axis. B Circular Manhattan plot. The outer-most ring is the GWAS of GEM on all individuals, the middle ring is the GWAS of GEM on males (λ^gc = 0.99), and the inner-most ring is the GWAS of GEM in females (λ^gc = 0.99). Inset, a scatter plot of the two sex-specific GWAS plotting all SNPs with p-values < 1 × 10-8 in either GWAS. Asymptotic confidence intervals are plotted with a width corresponding to genome-wide significance.

**Fig. 6. The transcriptomic correlates of GEM.**
A Association analyses were performed between GEM and gene expression in whole blood, including age, sex, genotype PCs 1-5, and expression PCs 1-20 as covariates. B Enrichment analyses were performed using pathfindR and KEGG pathways as reference. C Association statistics among CHIP GWAS genes in whole blood. D Association statistics among mCA GWAS genes in whole blood. Analyses were performed using 2248 samples in whole blood, 1215 for PBMC, 347 for T cells, 333 for monocytes, and 323 for nasal epithelial.

**Fig. 7. The phenotype correlates of GEM.**
A Cox proportional-hazard regressions were performed, regressing incident events on a spline of age, sex, smoking status, and germline PCs. Individuals with prevalent disease were excluded. CAD coronary artery disease, PAD peripheral artery disease, CABG coronary artery bypass graft, MI myocardial infarction. CAD events were defined as at least one of an MI, CABG, angina, or angioplasty during the follow-up period. A random effects meta-analysis was performed. GEM was inverse normal transformed. Sex was excluded from the WHI regression, and smoking was excluded from the COPD regression. B A linear regression of the inverse normal transformed biomarker, including a spline of age, sex, smoking status, and germline PCs as covariates. GEM was inverse normal transformed. Sex was excluded from the WHI regression, and smoking was excluding from the COPD regression.

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Update of

The Genetic Determinants and Genomic Consequences of Non-Leukemogenic Somatic Point Mutations.
Weinstock JS, Chaudhry SA, Ioannou M, Viskadourou M, Reventun P, Jakubek YA, Liggett LA, Laurie C, Broome JG, Khan A, Taylor KD, Guo X, Peyser PA, Boerwinkle E, Chami N, Kenny EE, Loos RJ, Psaty BM, Russell TP, Brody JA, Yun JH, Cho MH, Vasan RS, Kardia SL, Smith JA, Raffield LM, Bidulescu A, O'Brien E, de Andrade M, Rotter JI, Rich SS, Tracy RP, Chen YI, Gu CC, Hsiung CA, Kooperberg C, Haring B, Nassir R, Mathias R, Reiner A, Sankaran V, Lowenstein CJ, Blackwell TW, Abecasis GR, Smith AV, Kang HM, Natarajan P, Jaiswal S, Bick A, Post WS, Scheet P, Auer P, Karantanos T, Battle A, Arvanitis M. Weinstock JS, et al. medRxiv [Preprint]. 2024 Aug 26:2024.08.22.24312319. doi: 10.1101/2024.08.22.24312319. medRxiv. 2024. Update in: Nat Commun. 2025 Oct 16;16(1):9194. doi: 10.1038/s41467-025-64236-x. PMID: 39228737 Free PMC article. Updated. Preprint.

References

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1. Desai, P. et al. Somatic mutations precede acute myeloid leukemia years before diagnosis. Nat. Med.24, 1015–1023 (2018). - PMC - PubMed

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Genetic determinants and genomic consequences of non-leukemogenic somatic point mutations

Affiliations

Genetic determinants and genomic consequences of non-leukemogenic somatic point mutations

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

Update of

References

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources

Research Materials