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Hotspots of human mutation point to clonal expansions in spermatogonia

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Abstract

In renewing tissues, mutations conferring selective advantage may result in clonal expansions1,2,3,4. In contrast to somatic tissues, mutations driving clonal expansions in spermatogonia (CES) are also transmitted to the next generation. This results in an effective increase of de novo mutation rate for CES drivers5,6,7,8. CES was originally discovered through extreme recurrence of de novo mutations causing Apert syndrome5. Here, we develop a systematic approach to discover CES drivers as hotspots of human de novo mutation. Our analysis of 54,715 trios ascertained for rare conditions9,10,11,12,13, 6,065 control trios12,14,15,16,17,18,19 and population variation from 807,162 mostly healthy individuals20 identifies genes manifesting rates of de novo mutations inconsistent with plausible models of disease ascertainment. We propose 23 genes hypermutable at loss-of-function (LoF) sites as candidate CES drivers. An extra 17 genes feature hypermutable missense mutations at individual positions, suggesting CES acting through gain of function. CES increases the average mutation rate roughly 17-fold for LoF genes in both control trios and sperm and roughly 500-fold for pooled gain-of-function sites in sperm21. Positive selection in the male germline elevates the prevalence of genetic disorders and increases polymorphism levels, masking the effect of negative selection in human populations. Despite the excess of mutations in disease cohorts for 19 LoF CES driver candidates, only 9 show clear evidence of disease causality22, suggesting that CES may lead to false-positive disease associations.

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Fig. 1: CES and disease ascertainment.
Fig. 2: LoF-1 set of putative CES drivers.
Fig. 3: Effect of CES on de novo mutations in disease and on the levels of LoF polymorphism in population.
Fig. 4: Comparison with direct sequencing data.

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Data availability

All data used in this study, including intermediate processed datasets, are provided in Supplementary Tables 1–20. Associated summary files are also available on Zenodo (https://zenodo.org/records/15660433)61.

Code availability

The full analysis pipeline, including code to reproduce figures and statistical analyses, is available on GitHub at https://github.com/mikemoldovan/CES_Discovery.

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Acknowledgements

We thank C. Boix, C. Chiang and R. Stana as well as A. Quinlan and J. Kunisaki for valuable discussions and helpful suggestions that improved the analyses presented in this study. This work was supported by the National Institutes of Health through grant nos. R35GM12713, R01MH101244 and U01HG012009.

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Author notes

  1. These authors contributed equally: Vladimir Seplyarskiy, Mikhail A. Moldovan

Authors and Affiliations

  1. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Vladimir Seplyarskiy, Mikhail A. Moldovan, Evan Koch, Prathitha Kar & Shamil Sunyaev

  2. Brigham and Women’s Hospital, Division of Genetics, Harvard Medical School, Boston, MA, USA

    Vladimir Seplyarskiy & Shamil Sunyaev

  3. Cancer, Ageing and Somatic Mutation, Wellcome Sanger Institute, Hinxton, UK

    Matthew D. C. Neville & Raheleh Rahbari

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Contributions

V.S., M.A.M. and S.S. jointly conceived the study and developed the methodological framework. V.S., M.A.M., E.K., P.K. and M.D.C.N. performed data analysis and interpreted results. V.S., M.A.M., E.K., P.K., M.D.C.N., R.R. and S.S. collaboratively drafted and revised the paper. S.S., R.R., V.S. and M.A.M. jointly supervised the project. All authors reviewed and approved the final version of the paper.

Corresponding authors

Correspondence to Vladimir Seplyarskiy, Mikhail A. Moldovan or Shamil Sunyaev.

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The authors declare no competing interests.

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Nature thanks Ziyue Gao, Mikkel Schierup and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Counts of de novo variants in the NDD cohort.

(a) Numbers of de novo missense variants stratified by recurrence. Genes harboring variants occurring >10 times in the cohort are shown in blue. (b) Numbers of de novo loss-of-function variants aggregated by gene stratified by recurrence. (c) Scatter plot of observed vs. expected de novo loss-of-function variant counts in the NDD cohort. LoF-1 set genes are shown in red; LoF-2 set genes are shown in purple. Upper bound of disease ascertainment in Eq. (1) given by the lower bound of prevalence of 1% is shown as a dashed line.

Extended Data Fig. 2 Expression of the identified CES drivers in germline tissues.

(a) nTPM values for spermatogonia reported in The Human Protein Atlas single-cell dataset. (b) nTPM values normalized by the maximal expression across all tissues for each gene. (c) nTPM values in oocytes. (d) Normalized nTPM values in oocytes.

Extended Data Fig. 3 Stability of LoF-2 set with respect to the metric of loss-of-function constraint.

(a) Observed-to-expected variant count ratio (o/e) for de novo LoFs in genes with FDR < 0.1 in the neurodevelopmental disorder cohort (NDD) merged with the autism spectrum disorder (ASD) cohort plotted against the Loss-of-function Observed/Expected Lower-bound Fraction (LOELF) scores. The dashed violet line indicates the minimal LOELF value across LoF-2 genes of 0.23. LoF-2 genes are shown in violet, LoF-1 genes are shown in red, genes above the chosen LOELF threshold but not included in the LoF-2 set (SHANK3 and ZMYM2) are shown in black. (b) Same as in (a), but for the Loss-of-function Observed/Expected (LOE) metric. The upper bound for LOE (shown as violet dashed line) is 0.355.

Extended Data Fig. 4 Ratio of observed-to-expected counts of LoF de novo mutations in a cohort of control trios for LoF-2 set genes.

The ratio is shown as a function of the LOEUF threshold: we aggregate all genes with LOEUF values lower than the value indicated on the x-axis and calculate the cumulative observed-to-expected ratio. The shaded grey area represents the 95% confidence interval obtained by permuting the LOEUF labels.

Extended Data Fig. 5 Validation of LoF-2 genes with a non-LOE metric.

Prior of the GeneBayes shet calculated using biological features of genes (x-axis) and the shet values updated with LoF polymorphism data from gnomAD-v4 (y-axis). See section ‘GeneBayes update and CES’ for details.

Extended Data Fig. 6 Expected LoF rate in NanoSeq data for LoF-1 and LoF-2 genes.

Rates are shown separately for genes overlapping with those significant in NanoSeq and for private LoF-1/2 genes. An asterisk (*) indicates p < 0.05 from the Mann–Whitney U test.

Extended Data Fig. 7 Power analysis of paternal transmission.

Statistical power (i.e., the probability of correctly detecting a signal when it exists calculated as the complement of type-2 error rate ß) of the Binomial test for paternal overtransmission relative to the baseline of 0.75 is shown across the range of CES-related mutation rate inflations κ and counts of observed variants. Results are presented for three significance levels: 0.05, 0.01, and 0.001.

Supplementary information

Supplementary Text

This file contains four Supplementary Notes 1–4. Note 1, Equivalence of de novo mutation rate and mutation probability under low mutation rates: justification for using the Bernoulli approximation to the Poisson distribution when the expected count is low. Note 2, CHIP leads to misinterpretation of LOEUF: rationale for, and description of, further filtering of results based on the involvement of genes in clonal haematopoiesis. Note 3, Misannotations of protein-truncating variants (PTVs) lead to misinterpretations of LOEUF: rationale for, and description of, further filtering of results to account for misannotation of predicted LoF variants. Note 4, Ascertainment of disease-causing variants under arbitrary trait architectures: derivation of equation (1) in the main text and discussion of its applicability to traits with non-strictly monogenic architecture.

Reporting Summary

Supplementary Fig. 1

Heatmap of expression of identified CES drivers in fetal single-cell expression clusters identified by Xu et al. (ref. 57).

Supplementary Tables

This file contains Supplementary Tables 1–20.

Peer Review File

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Seplyarskiy, V., Moldovan, M.A., Koch, E. et al. Hotspots of human mutation point to clonal expansions in spermatogonia. Nature (2025). https://doi.org/10.1038/s41586-025-09579-7

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