Genome-wide analysis of mitochondrial DNA copy number reveals loci implicated in nucleotide metabolism, platelet activation, and megakaryocyte proliferation

R J Longchamps, S Y Yang, C A Castellani, W Shi, J Lane, M L Grove, T M Bartz, C Sarnowski, C Liu, K Burrows, A L Guyatt, T R Gaunt, T Kacprowski, J Yang, P L De Jager, L Yu, A Bergman, R Xia, M Fornage, M F FeitosaM K Wojczynski, A T Kraja, M A Province, N Amin, F Rivadeneira, H Tiemeier, A G Uitterlinden, L Broer, J B J Van Meurs, C M Van Duijn, L M Raffield, L Lange, S S Rich, R N Lemaitre, M O Goodarzi, C M Sitlani, A C Y Mak, D A Bennett, S Rodriguez, J M Murabito, K L Lunetta, N Sotoodehnia, G Atzmon, K Ye, N Barzilai, J A Brody, B M Psaty, K D Taylor, J I Rotter, E Boerwinkle, N Pankratz, D E Arking

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Abstract

Mitochondrial DNA copy number (mtDNA-CN) measured from blood specimens is a minimally invasive marker of mitochondrial function that exhibits both inter-individual and intercellular variation. To identify genes involved in regulating mitochondrial function, we performed a genome-wide association study (GWAS) in 465,809 White individuals from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank (UKB). We identified 133 SNPs with statistically significant, independent effects associated with mtDNA-CN across 100 loci. A combination of fine-mapping, variant annotation, and co-localization analyses was used to prioritize genes within each of the 133 independent sites. Putative causal genes were enriched for known mitochondrial DNA depletion syndromes (p = 3.09 × 10-15) and the gene ontology (GO) terms for mtDNA metabolism (p = 1.43 × 10-8) and mtDNA replication (p = 1.2 × 10-7). A clustering approach leveraged pleiotropy between mtDNA-CN associated SNPs and 41 mtDNA-CN associated phenotypes to identify functional domains, revealing three distinct groups, including platelet activation, megakaryocyte proliferation, and mtDNA metabolism. Finally, using mitochondrial SNPs, we establish causal relationships between mitochondrial function and a variety of blood cell-related traits, kidney function, liver function and overall (p = 0.044) and non-cancer mortality (p = 6.56 × 10-4).

Original languageEnglish
Pages (from-to)127-146
Number of pages20
JournalHuman Genetics
Volume141
Issue number1
Early online date2 Dec 2021
DOIs
Publication statusPublished - Jan 2022

Bibliographical note

Funding Information:
This work was supported by National Heart, Lung and Blood Institute, National Institutes of Health (NIH) grants R01HL13573 and R01HL144569 (RJL, SYY, CAC, DEA), NIH grant P01-AG027734 (GA, YK, NB, AB) and the National Center for Advancing Translational Sciences, NIH, through Grant KL2TR002490 (LMR). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. LMR was also funded by T32 HL129982. This research was conducted using data from the Genotype-Tissue Expression (GTEx) project (dbGaP accession: phs000424.v8.p2). The GTEx project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. This research was also conducted using the UK Biobank Resource under Application Number 17731. The Atherosclerosis Risk in Communities study (dbGaP accession: phs000280.v7.p1) has been funded in whole or in part with Federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services (contract numbers HSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700004I and HHSN268201700005I), R01HL087641, R01HL059367 and R01HL086694; National Human Genome Research Institute contract U01HG004402; and National Institutes of Health contract HHSN268200625226C. Funding support for “Building on GWAS for NHLBI diseases: the U.S. CHARGE consortium” was provided by the NIH through the American Recovery and Reinvestment Act of 2009 (ARRA) (5RC2HL102419). Sequencing was carried out at the Baylor College of Medicine Human Genome Sequencing Center and supported by the National Human Genome Research Institute grants U54 HG003273 and UM1 HG008898. The authors thank the staff and participants of the ARIC study for their important contributions. Infrastructure was partly supported by Grant Number UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research. This CHS research was supported by NHLBI contracts HHSN268201200036C, HHSN268200800007C, HHSN268201800001C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, 75N92021D00006; and NHLBI grants U01HL080295, R01HL087652, R01HL105756, R01HL103612, R01HL120393, and U01HL130114 with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided through R01AG023629 from the National Institute on Aging (NIA). A full list of principal CHS investigators and institutions can be found at CHS-NHLBI.org. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR001881, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center. The FHS phenotype-genotype analyses were supported by the National Institute of Aging (U34AG051418). This research was conducted in part using data and resources from the Framingham Heart Study of the National Heart Lung and Blood Institute of the National Institutes of Health and Boston University School of Medicine. This work was partially supported by the National Heart, Lung and Blood Institute's Framingham Heart Study (Contract No. N01-HC-25195, HHSN268201500001) and its contract with Affymetrix, Inc for genotyping services (Contract No. N02-HL-6-4278). Genotyping, quality control and calling of the Illumina HumanExome BeadChip in the Framingham Heart Study was supported by funding from the National Heart, Lung and Blood Institute Division of Intramural Research (Daniel Levy and Christopher J. O’Donnell, Principle Investigators). The authors thank the participants for their dedication to the study. The authors are pleased to acknowledge that the computational work reported on in this paper was performed on the Shared Computing Cluster which is administered by Boston University’s Research Computing Services. URL: www.bu.edu/tech/support/research/ . MESA and the MESA SHARe projects (dbGaP accession: phs000209.v13.p3) are conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators. Support for MESA is provided by contracts 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420. Funding for SHARe genotyping was provided by NHLBI Contract N02-HL-64278. Genotyping was performed at Affymetrix (Santa Clara, California, USA) and the Broad Institute of Harvard and MIT (Boston, Massachusetts, USA) using the Affymetrix Genome-Wide Human SNP Array 6.0. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR001881, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center. ROS/MAP is supported by the Translational Genomics Research Institute and National Institute on Aging (NIA) through grants U01AG46152, U01AG61256, P30AG10161, R01AG17917, RF1AG15819, R01AG30146. SHIP and SHIP-TREND are part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research (grants no. 01ZZ9603, 01ZZ0103, and 01ZZ0403), the Ministry of Cultural Affairs as well as the Social Ministry of the Federal State of Mecklenburg-West Pomerania, and the network ‘Greifswald Approach to Individualized Medicine (GANI_MED)’ funded by the Federal Ministry of Education and Research (grant 03IS2061A). The UK Medical Research Council and Wellcome [Grant ref: 217065/Z/19/Z] and the University of Bristol provide core support for ALSPAC. This publication is the work of the authors and D.E. Arking will serve as guarantors for the contents of this paper. A comprehensive list of grants funding is available on the ALSPAC website ( http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf ). We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. Work on ALSPAC was carried out in the MRC Integrative Epidemiology Unit (MC_UU_00011/4). This research was funded in whole, or in part, by the Wellcome Trust [Grant ref: 217065/Z/19/Z]. For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.

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