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. 2022 Nov 30;612(7939):301–309. doi: 10.1038/s41586-022-05448-9

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© The Author(s), under exclusive licence to Springer Nature Limited 2022, corrected publication 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

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Extended Data Fig. 1 — A. Graphic depicting at a high-level the workflow used to collect and sequence the exomes of multiple large cohorts and to then identify CHIP mutations from this data. B-C. CHIP prevalence increases with age of donor at time of DNA collection in both the UKB (B, n = 484,629 individuals; one-sided F-test, P < 10⁻¹⁶) and GHS (C, n = 157,724 individuals; one-sided F-test, P < 10⁻¹⁶) cohorts, with the centre line representing the general additive model spline and the shaded region representing the 95% confidence interval. D-E. Similar to B-C, the prevalence of CHIP mutations per CHIP gene for each of the top 8 most common CHIP genes increase with age in the UKB (D, n = 484,629 individuals; one-sided F-test, P < 10⁻¹⁶) and in GHS (E, n = 157,724 individuals; one-sided F-test, P < 10⁻¹⁶).