1. Genetics and Genomics

Human genetic analyses of organelles highlight the nucleus in age-related trait heritability

  1. Rahul Gupta  Is a corresponding author
  2. Konrad Karczewski
  3. Daniel Howrigan
  4. Benjamin Neale  Is a corresponding author
  5. Vamsi Mootha, MD MD  Is a corresponding author
  1. Broad Institute of MIT and Harvard, United States
  2. Massachusetts General Hospital, United States
  3. Broad Institute, United States
  4. Howard Hughes Medical Institute, Massachusetts General Hospital, United States
https://doi.org/10.7554/eLife.68610
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  1. Rahul Gupta
  2. Konrad Karczewski
  3. Daniel Howrigan
  4. Benjamin Neale
  5. Vamsi Mootha, MD MD
(2021)
Human genetic analyses of organelles highlight the nucleus in age-related trait heritability
eLife 10:e68610.
https://doi.org/10.7554/eLife.68610
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Abstract

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

    Metabolism, Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, United States
    For correspondence
    rahul@broadinstitute.org
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8263-2455

  2. Konrad Karczewski

    Metabolism, Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, United States
    Competing interests
    Konrad 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 Neale

    Broad Institute, Cambridge, United States
    For correspondence
    bneale@broadinstitute.org
    Competing interests
    Benjamin 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 Mootha, MD MD

    Howard Hughes Medical Institute, Massachusetts General Hospital, Boston, United States
    For correspondence
    vamsi_mootha@hms.harvard.edu
    Competing interests
    Vamsi Mootha, MD, is an advisor to and receives compensation or equity from Janssen Pharmaceuticals, 5am Ventures, and Raze Therapeutics..

Funding

National Institutes of Health (T32AG000222)

  • Rahul Gupta

National Institutes of Health (R35GM122455)

  • Vamsi Mootha, MD 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

Version history

  1. Preprint posted: January 22, 2021 (view preprint)
  2. Received: March 21, 2021
  3. Accepted: August 30, 2021
  4. Accepted Manuscript published: September 1, 2021 (version 1)
  5. Accepted Manuscript updated: September 7, 2021 (version 2)
  6. Version of Record published: September 27, 2021 (version 3)

Copyright

© 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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  1. Rahul Gupta
  2. Konrad Karczewski
  3. Daniel Howrigan
  4. Benjamin Neale
  5. Vamsi Mootha, MD MD
(2021)
Human genetic analyses of organelles highlight the nucleus in age-related trait heritability
eLife 10:e68610.
https://doi.org/10.7554/eLife.68610

Share this article

https://doi.org/10.7554/eLife.68610

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