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. 2023 Nov 6;55(12):2149–2159. doi: 10.1038/s41588-023-01555-z

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Extended Data Fig. 1 — a, Frequency distribution in UKB of singleton mutations: Mutations that were observed only once in the cohort were plotted by variant allele fraction (VAF). The counts were further stratified by the age of the subject at blood draw. Note that there is a ‘bump’ in the distribution starting below a VAF of approximately 0.3 and that the size of this ‘bump’ is age dependent. This distribution was modeled to identify people with more than the expected number of low-VAF mutations, as explained further in the Methods. b, Proportion of subjects with CH increases with age. The line connects the observed CH proportions, error bars are 95%CI. Data are from the ISL sample (n = 45,510), which has a larger age range than UKB. c, Effects of current and previous smoking on CH by age: CH was modeled by age and stratified by current or previous smoking status using sex, Pack-Years and Years Since Stopped Smoking as covariates. Points correspond to observed CH proportions and error bars are 95%CI. Lines correspond to a logistic regression fit. Data are from the UKB sample (n = 130,709).