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An atlas of mitochondrial DNA genotype–phenotype associations in the UK Biobank


Mitochondrial DNA (mtDNA) variation in common diseases has been underexplored, partly due to a lack of genotype calling and quality-control procedures. Developing an at-scale workflow for mtDNA variant analyses, we show correlations between nuclear and mitochondrial genomic structures within subpopulations of Great Britain and establish a UK Biobank reference atlas of mtDNA–phenotype associations. A total of 260 mtDNA–phenotype associations were new (P < 1 × 10−5), including rs2853822/m.8655 C>T (MT-ATP6) with type 2 diabetes, rs878966690/m.13117 A>G (MT-ND5) with multiple sclerosis, 6 mtDNA associations with adult height, 24 mtDNA associations with 2 liver biomarkers and 16 mtDNA associations with parameters of renal function. Rare-variant gene-based tests implicated complex I genes modulating mean corpuscular volume and mean corpuscular hemoglobin. Seven traits had both rare and common mtDNA associations, where rare variants tended to have larger effects than common variants. Our work illustrates the value of studying mtDNA variants in common complex diseases and lays foundations for future large-scale mtDNA association studies.

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Fig. 1: Mitochondrial genome PheWAS workflow.
Fig. 2: Distribution of the eight nuclear genome clusters and mtDNA haplogroups across Great Britain.
Fig. 3: mtSNV associations with kidney-related traits.
Fig. 4: PheWAS association results for blood cell and cardiometabolic traits.

Data availability

Full summary statistics are provided at and Zenodo (

We used data from the following publicly available databases:;;

Code availability

Code used to process UKBB data and source data used to generate the main figures are available at:

Source data for Figs. 1–4 are available from:, and

Source data for Extended Data Figs. 13 are available from:,, and


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We are grateful to: G. Hudson and H. Griffin for discussions and the preliminary exploratory work that proceeded this study; P. Surendran and T. Jiang for assistance with the genotype calling scripts; and W. Astle from the University of Cambridge for providing blood cell trait phenotypes and summary statistics. The BHF Cardiovascular Epidemiology Unit is supported by the UK Medical Research Council (MR/L003120/1), British Heart Foundation (RG/13/13/30194 and RG/18/13/33946) and the NIHR Cambridge Biomedical Research Center (BRC-1215-20014) and Health Data Research UK (which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome). P.C. is a Wellcome Trust Principal Research Fellow (212219/Z/18/Z) and a UK NIHR senior investigator, who receives support from the Medical Research Council Mitochondrial Biology Unit (MC_UU_00015/9), the Medical Research Council International Center for Genomic Medicine in Neuromuscular Disease, the Evelyn Trust and the NIHR Cambridge BRC (BRC-1215-20014) [*]. J.H. is funded by the British Heart Foundation (RG/13/13/30194) and the NIHR Cambridge BRC (BRC-1215-20014) [*]. E.Y.-D. was funded by the Isaac Newton Trust/Wellcome Trust ISSF/University of Cambridge Joint Research Grants Scheme. A.G.-D. is funded by the NIHR Cambridge BRC (146281). This research was conducted using the UKBB Resource under application numbers 20480, 7439 and 18794. [*]The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.

Author information




E.Y.-D. and C.C. performed analyses and drafted the manuscript. K.S. and A.G.-D. selected binary traits for analysis and filtering. W.W. performed GenBank data retrieval and initial QC. S.K. performed initial QC of UKBB data. P.F.C. and J.M.M.H. drafted the manuscript and supervised the work. All authors approved the final version of the paper.

Corresponding authors

Correspondence to Patrick F. Chinnery or Joanna M. M. Howson.

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Competing interests

J.M.M.H. and E.Y.D. became full-time employees of Novo Nordisk during the drafting of the manuscript. The remaining authors declare no competing interests.

Additional information

Peer review information Nature Genetics thanks Valerio Carelli and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Distribution of mitochondrial sub-haplogroups across Great Britain.

The European unrelated individuals with birth coordinates (N = 327,665) were clustered based on the first 10 nucPCs, resulting in eight nuclear clusters. The map of Great Britain is colored according to the five regions identified by the most common clusters or combination of clusters in each region: (1) Scotland; (2) North of England (North East and West); (3) North of England (Yorkshire and the Humber, North West of England); (4) South of England (Midlands, London, South East and West of England); (5) Wales. No data were available for Northern Ireland. The stacked bar charts represent the frequency of unrelated individuals in each mitochondrial sub-haplogroups, in the five regions identified by the most common nuclear clusters or combination of nuclear clusters. The star indicates an over-representation (likelihood ratio test, two-sided P < 5 × 10−5) of J1b sub-haplogroup in Scotland compared to the Midlands, London, South East and West region.

Extended Data Fig. 2 Relationship between population structure in the nuclear and mitochondrial genomes.

The figure shows (a) circular Manhattan plots of the association between the first 10 nucPCs and mtSNVs. For each mtSNV, the association was tested using a linear regression model: Y~ β1 x X1 + β2 x X2 + β3 x X3 + β4 x X4 + β5 x X5 where Y is a vector containing the values of a nucPC, X1 is a vector of mtSNV dosages and X2-X5 are vectors containing covariate values (age, age squared, sex, and array) and β1-5 represent the effect of each variable on the mean of Y. Wald test two-sided P-values are presented. The nucPCs are ordered from PC1 to PC10 from outside to in and black dots represent (Wald test, two-sided) P < 5 × 10−5; (b) 3D plots of the first three mtPCs; and (c) the relationship between the first three nuclear principal components (nucPCs, nucPC1 - left, nucPC2 - middle, nucPC3 - right) and the first two mitochondrial principal components (mtPCs). The latter were calculated using mtSNVs with MAF > 0.01 and R2 < 0.2. The mtPCs in (a) and (b) were calculated using the following sets of genotyped mtSNV: (from left to right) all mtSNVs; mtSNVs with MAF > 0.01 only; and mtSNVs with MAF > 0.01 after LD-pruning at R2 < 0.2. N = the number of mtSNVs included in a given analysis. In (b) and (c) individuals are coloured according to macro-haplogroup carrier status.

Extended Data Fig. 3 Principal components analysis of the European set of UK Biobank participants in comparison to European participants in GenBank, 1000 genomes and WTCCC.

Plots of the first three mitochondrial principal components (mtPCs) for individuals in: (a) the European set of UK Biobank (N = 358,916), (b) GenBank reference set used for imputation (N = 6,593), (c) 1000 Genomes individuals (N = 498) and (d) WTCCC controls (N = 747). For each of the three data sets, plots on the left-hand side show mtPCs calculated using pruned SNVs (R2 < 0.2 for UK Biobank and R2 < 0.1 for GenBank, 1000 Genomes and WTCCC) while the plots on the right were generated without LD-pruning. Individuals are colored according to macro-haplogroup carrier status. mtPCs were calculated using genotyped SNVs (MAF > 0.01).

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Yonova-Doing, E., Calabrese, C., Gomez-Duran, A. et al. An atlas of mitochondrial DNA genotype–phenotype associations in the UK Biobank. Nat Genet 53, 982–993 (2021).

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