Mechanism of age-related accumulation of mtDNA mutations in human blood

Article Mechanism of age-related accumulation of mtDNA mutations in human blood https://doi.org/10.1038/s41586-026-10569-6 Received: 6 May 2025 Accepted: 20 April 2026 Rahul Gupta1,2,3,4,5,6 ✉, Timothy J. Durham1,4,6, Grant Chau2,5,7, Masahiro Kanai5,8, Md Mesbah Uddin5,9,10, Wenhan Lu2,5,7, M. Austin Argentieri2,5,7, Konrad J. Karczewski2,5,11, Daniel Howrigan2,5,7, Pradeep Natarajan3,5,9,10, Wei Zhou5,7,12, Benjamin M. Neale2,5,7,11 ✉ & Vamsi K. Mootha1,3,4,6 ✉ Published online: xx xx xxxx Open access Check for updates Accumulation of mutant mitochondrial DNA (mtDNA) heteroplasmy is among the strongest signatures of ageing1. Here we investigated the underlying mechanism by calling mtDNA sequence, mtDNA abundance and mtDNA heteroplasmic variants in human blood using whole-genome sequences from approximately 750,000 individuals. We observed that mtDNA single-nucleotide variants (mtSNVs) accumulate sharply at age 60 years, occur at low levels of heteroplasmy, exhibit little evidence of positive selection and are likely to be predominantly neutral. The mutational spectrum of mtSNVs does not reflect oxidative lesions, as is commonly invoked, but is more consistent with mtDNA replication errors. To understand why mtSNVs become detectable with age, we performed a genome-wide association study for heteroplasmic mtSNV burden, identifying germline variants near TERT, TCL1A and SMC4, all of which have been linked to clonal haematopoiesis (CH)2. Rare-variant analysis also showed that high mtSNV burden is associated with mutations in numerous CH driver genes. These genetic associations persisted even after exclusion of individuals with known CH driver mutations. Our results support a model in which ‘cryptic’ mtDNA mutations initially arise randomly as replication errors but are undetectable in bulk. They then become apparent only through age-related expansion of cellular clones in blood. We propose that the high copy number and mutation rate of mtDNA make it a sensitive blood-based marker of somatic mosaicism due to CH. Our work mechanistically unifies three prominent signatures of ageing: common germline variants in TERT, CH and observed accrual of mtDNA mutations. Mitochondrial DNA (mtDNA) heteroplasmy arises when a cell or tissue contains a mixture of two or more different mtDNA alleles. Heteroplasmy dynamics tend to be complex, varying across generations, during development, in disease and with ageing. Historically, most studies of mtDNA heteroplasmy in humans have focused on rare, maternally transmitted disorders, which are typically driven by loss-of-function mtDNA mutations at high levels of heteroplasmy. However, there is growing evidence that low levels of mtDNA heteroplasmic variants are found in nearly all humans3. Using biobank-scale genomics, we previously reported that nearly everyone harbours two different classes of such variants in blood4. ‘Length heteroplasmies’ (insertion/deletion (indel) mutations within polypyrimidine tracts) do not accumulate with age, tend to be maternally transmitted and, once inherited, exhibit levels of heteroplasmy under nuclear genetic control. The associated nuclear DNA (nucDNA) loci tend to implicate mitochondria-localized proteins with established roles in mtDNA replication and maintenance. By contrast, heteroplasmic mtSNVs tend not to be inherited but rather seem to be somatic in origin and accumulate with age4. The mechanism of this age-related accrual of heteroplasmic mtSNVs in blood is unknown. Classically, oxidative damage to mtDNA from reactive oxygen species has been invoked as a part of a ‘vicious cycle’ in which mtDNA mutations lead to further generation of reactive oxygen species and more mutations5–7. More recent studies of ageing brains8 and tumour samples9 have questioned the role of oxidative damage10 in mutation generation. Once individual mutations arise, it is unclear how they become abundant enough to detect. Here we investigated why mtSNVs accumulate with age in blood. We report an analysis of mtDNA using a callset of approximately Howard Hughes Medical Institute, Massachusetts General Hospital, Boston, MA, USA. 2Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA. 3Harvard Medical School, Boston, MA, USA. 4Metabolism Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA. 5Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. 6Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA. 7Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA. 8Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA. 9Heart and Vascular Institute, Mass General Brigham, Boston, MA, USA. 10Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. 11Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA. 12Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. ✉e-mail: ; ; 1 Nature | www.nature.com | 1 Article mtDNA sequences from 736,038 individuals We applied mtSwirl4, a pipeline for calling mtDNA abundance and heteroplasmic variation (Methods), to whole-genome sequencing (WGS) data from 736,038 diverse individuals in AoU and UKB, representing an approximately 3× increase in sample size compared with ref. 4. mtSwirl reconstructs each individual’s mtDNA to serve as a ‘self-reference’ against which low levels of heteroplasmic variants can be more accurately called4. After quality control, we called a total of 19,051,526 variants (Methods) from 620,385 individuals. Of the 1,151,297 heteroplasmic variants (Supplementary Note 1), 754,108 were indels and 397,189 were SNVs (Extended Data Fig. 1). To benchmark our callset, we performed GWAS of mtDNA copy number (mtCN) and individual common heteroplasmic variants and compared them with the results of our previous study4. GWAS for blood-composition-adjusted mtCN in UKB (mtCNadj; Supplementary Note 2) across 398,250 individuals identified 107 loci (Extended Data Fig. 2c and Methods), many of which were corroborated by fine-mapping and gene-based rare-variant testing (Extended Data Fig. 2d,e). We replicated 44 of 46 loci from our previous mtCNadj GWAS4 (n = 163,372) at genome-wide significance (GWS) and discovered 63 further associations (for instance near LIG3 and TBRG4). As in previous work4,11, adjustment for blood cell composition eliminated or reversed the direction of most associations between reduced mtCN and increased rates of age-related diseases (Extended Data Fig. 2f). GWAS for heteroplasmy of each of 78 common mtDNA variants, most of which were indels (Extended Data Fig. 1), identified 163 associations across 60 nuclear loci (Extended Data Fig. 3a). We replicated virtually (...truncated)