1. Genetics and Genomics
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Human genetic analyses of organelles highlight the nucleus in age-related trait heritability

  1. Rahul Gupta  Is a corresponding author
  2. Konrad J Karczewski
  3. Daniel Howrigan
  4. Benjamin M Neale  Is a corresponding author
  5. Vamsi K Mootha MD  Is a corresponding author
  1. Howard Hughes Medical Institute, Massachusetts General Hospital, United States
  2. Broad Institute of MIT and Harvard, United States
  3. Massachusetts General Hospital, United States
Research Article
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Cite this article as: eLife 2021;10:e68610 doi: 10.7554/eLife.68610


Most age-related human diseases are accompanied by a decline in cellular organelle integrity, including impaired lysosomal proteostasis and defective mitochondrial oxidative phosphorylation. An open question, however, is the degree to which inherited variation in or near genes encoding each organelle contributes to age-related disease pathogenesis. Here, we evaluate if genetic loci encoding organelle proteomes confer greater-than-expected age-related disease risk. As mitochondrial dysfunction is a 'hallmark' of aging, we begin by assessing nuclear and mitochondrial DNA loci near genes encoding the mitochondrial proteome and surprisingly observe a lack of enrichment across 24 age-related traits. Within nine other organelles, we find no enrichment with one exception: the nucleus, where enrichment emanates from nuclear transcription factors. In agreement, we find that genes encoding several organelles tend to be 'haplosufficient', while we observe strong purifying selection against heterozygous protein-truncating variants impacting the nucleus. Our work identifies common variation near transcription factors as having outsize influence on age-related trait risk, motivating future efforts to determine if and how this inherited variation then contributes to observed age-related organelle deterioration.

Data availability

Heritability point estimates and standard errors for age-related traits are listed in Supplementary File 1. Genetic and phenotypic correlation point estimates and standard errors/p-values plotted in Figure 1B are available in Figure 1-Source data 1. Summary statistics from mtDNA-GWAS (plotted in Figure 2 and Figure 2-Figure supplement 9) are available in Source data 2. All gene-based enrichment analysis p-values and point estimates are available in Source data 1 and Source data 3. Period prevalence data for diseases in the UK can be obtained from Kuan et al. 2019. Gene-sets can be found using COMPARTMENTS (https://compartments.jensenlab.org), MitoCarta 2.0 (https://www.broadinstitute.org/files/shared/metabolism/mitocarta/human.mitocarta2.0.html), Lambert et al. 2018 (DOI: 10.1016/j.cell.2018.01.029), Frazier et al. 2019 (DOI: 10.1074/jbc.R117.809194), Finucane et al. 2018 (https://alkesgroup.broadinstitute.org/LDSCORE/), Kapopoulou et al. 2015 (DOI: 10.1111/evo.12819), and the Macarthur laboratory (https://github.com/macarthur-lab/gene_lists). Gene age estimates were obtained from Litman, Stein 2019 (DOI: 10.1053/j.seminoncol.2018.11.002). GWAS catalog annotations can be obtained from: https://www.ebi.ac.uk/gwas. Heritability estimates across UKB can be obtained at: https://nealelab.github.io/UKBB_ldsc/. UKB summary statistics can be obtained from Neale lab GWAS round 2: https://github.com/Nealelab/UK_Biobank_GWAS. Annotations for the Baseline v1.1 and BaselineLD v2.2 models as well as other relevant reference data, including the 1000G EUR reference panel, can be obtained from https://alkesgroup.broadinstitute.org/LDSCORE/. eQTL and expression data in human tissues can be obtained from GTEx (https://www.gtexportal.org). Constraint estimates can be found via gnomAD: https://gnomad.broadinstitute.org. See citations for publicly available GWAS meta-analysis summary statistics (28,29,51,52,30-37).

The following previously published data sets were used

Article and author information

Author details

  1. Rahul Gupta

    Department of Molecular Biology, Howard Hughes Medical Institute, Massachusetts General Hospital, Boston, United States
    For correspondence
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8263-2455
  2. Konrad J Karczewski

    Broad Institute of MIT and Harvard, Cambridge, United States
    Competing interests
    Konrad J Karczewski, is a consultant for Vor Biopharma..
  3. Daniel Howrigan

    Analytic & Translational Genetics Unit, Massachusetts General Hospital, Boston, United States
    Competing interests
    No competing interests declared.
  4. Benjamin M Neale

    Broad Institute of MIT and Harvard, Cambridge, United States
    For correspondence
    Competing interests
    Benjamin M Neale, BMN is a member of the scientific advisory board at Deep Genomics and RBNC Therapeutics. BMN is a consultant for Camp4 Therapeutics, Takeda Pharmaceutical and Biogen..
  5. Vamsi K Mootha MD

    Molecular Biology, Howard Hughes Medical Institute, Massachusetts General Hospital, Boston, United States
    For correspondence
    Competing interests
    Vamsi K Mootha, is an advisor to and receives compensation or equity from Janssen Pharmaceuticals, 5am Ventures, and Raze Therapeutics..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9924-642X


National Institutes of Health (T32AG000222)

  • Rahul Gupta

National Institutes of Health (R35GM122455)

  • Vamsi K Mootha MD

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Sara Hägg, Karolinska Institutet, Sweden

Publication history

  1. Received: March 21, 2021
  2. Accepted: August 30, 2021
  3. Accepted Manuscript published: September 1, 2021 (version 1)
  4. Accepted Manuscript updated: September 7, 2021 (version 2)


© 2021, Gupta et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.


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    The virus SARS-CoV-2 can exploit biological vulnerabilities (e.g. host proteins) in susceptible hosts that predispose to the development of severe COVID-19.


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    MAK, JSc, JH, AB, DO, MC, EMM, MG, ID were funded by Open Targets. J.Z. and T.R.G were funded by the UK Medical Research Council Integrative Epidemiology Unit (MC_UU_00011/4). JSh and GJW were funded by the Wellcome Trust Grant 206194. This research was funded in part by the Wellcome Trust [Grant 206194]. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.