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

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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

(2014) COMPARTMENTS
COMPARTMENTS Portal.

https://compartments.jensenlab.org/Downloads
(2015) MitoCarta2.0
Broad Institute.

https://www.broadinstitute.org/files/shared/metabolism/mitocarta/human.mitocarta2.0.html
1. Buniello A
2. MacArthur JAL
3. Cerezo M
4. Harris LW
5. Hayhurst J
6. Malangone C
7. McMahon A
8. Morales J
9. Mountjoy E
10. Sollis E
11. Suveges D
12. Vrousgou O
13. Whetzel PL
14. Amode R
15. Guillen JA
16. Riat HS
17. Trevanion SJ
18. Hall P
19. Junkins H
20. Flicek P
21. Burdett T
22. Hindorff LA
23. Cunningham F
24. Parkinson H
(2019) GWAS Catalog, all associations v1.0.2
NHGRI-EBI GWAS Catalog.

https://www.ebi.ac.uk/gwas/docs/file-downloads
1. Abbott L
2. Bryant S
3. Churchhouse C
4. Ganna A
5. Howrigan H
6. Palmer D
7. Neale B
8. Walters R
9. Carey C
10. The Hail team
(2018) Neale Lab UKB Round 2 GWAS Summary Statistics
Neale lab, Broad Institute.

http://www.nealelab.is/uk-biobank/
1. Walters R
2. Baya N
3. Tashman K
4. Chen D
5. Abbott L
6. Carey C
7. Palmer D
8. Neale B
(2019) UKB Round 2 GWAS Heritability Estimates
Dropbox.

https://www.dropbox.com/s/8vca84rsslgbsua/ukb31063_h2_topline.02Oct2019.tsv.gz?dl=1
1. Teslovich TM et al.
(2010) Biological, clinical and population relevance of 95 loci for blood lipids
University of Michigan.

http://csg.sph.umich.edu/willer/public/lipids2010/
1. The International Consortium for Blood Pressure Genome-Wide Association Studies (Ehret et al.)
(2011) Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk
dbGaP phs000585.v1.

https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/analysis.cgi?study_id=phs000585.v2.p1&phv=&phd=&pha=3588&pht=&phvf=&phdf=&phaf=1&phtf=&dssp=1&consent=&temp=1
1. DIAGRAM Consortium (Morris et al.)
(2012) Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes, stage 1 GWAS
DIAGRAM T2D Stage 1 GWAS.

https://www.diagram-consortium.org/downloads.html
1. CARDIoGRAMplusC4D Consortium (Schunkert et al.)
(2011) Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease
CARDIoGRAMplusC4D GWA meta-analysis.

http://www.cardiogramplusc4d.org/media/cardiogramplusc4d-consortium/data-downloads/cardiogram_gwas_results.zip
1. GEnetic Factors for OSteoporosis Consortium (GEFOS
2. Estrada et al.)
(2012) Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture
GEFOS Pooled Femoral Neck Summary Statistics.

http://www.gefos.org/sites/default/files/GEFOS2_FNBMD_POOLED_GC.txt.gz
1. AFGen (Christophersen et al.)
(2017) Large-scale analyses of common and rare variants identify 12 new loci associated with atrial fibrillation
Human Genetics Amplifier.

https://personal.broadinstitute.org/ryank/28416818.2017.AFGen.GWAS.zip
1. CKDGen (Pattaro et al.)
(2016) Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function
CKDGen Data at Medical Center - University of Freiburg, eGFRcrea and CKD.

https://ckdgen.imbi.uni-freiburg.de/#Pattaro2016data
1. Brainstorm
2. IPDGC (Nalls et al.)
(2019) Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies
IPDGC GWAS META5 summary stats (excluding 23andMe).

https://drive.google.com/file/d/1FZ9UL99LAqyWnyNBxxlx6qOUlfAnublN/view?usp=sharing
1. Brainstorm
2. IGAP (Lambert et al.)
(2013) Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease
IGAP Stage 1.

http://web.pasteur-lille.fr/en/recherche/u744/igap/igap_download.php
1. Timmers PRHJ et al.
(2019) Genomics of 1 million parent lifespans implicates novel pathways and common diseases and distinguishes survival chances
University of Edinburgh DataShare, doi:10.7488/ds/2463.

https://datashare.ed.ac.uk/handle/10283/3209
1. Zenin A et al.
(2019) Identification of 12 genetic loci associated with human healthspan
Zenodo, doi:10.5281/zenodo.1302861.

https://zenodo.org/record/1302861
1. GTEx Consortium
(2019) GTEx v8 median expression TPM per tissue
GTEx portal.

https://storage.googleapis.com/gtex_analysis_v8/rna_seq_data/GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_median_tpm.gct.gz
1. GTEx Consortium
(2019) GTEx v8 single tissue eQTLs
GTEx portal.

https://storage.googleapis.com/gtex_analysis_v8/single_tissue_qtl_data/GTEx_Analysis_v8_eQTL.tar

Article and author information

Author details

Rahul Gupta

Department of Molecular Biology, Howard Hughes Medical Institute, Massachusetts General Hospital, Boston, United States

For correspondence
rahul@broadinstitute.org

Competing interests
No competing interests declared.

"This ORCID iD identifies the author of this article:" 0000-0001-8263-2455
Konrad J Karczewski

Broad Institute of MIT and Harvard, Cambridge, United States

Competing interests
Konrad J Karczewski, is a consultant for Vor Biopharma..
Daniel Howrigan

Analytic & Translational Genetics Unit, Massachusetts General Hospital, Boston, United States

Competing interests
No competing interests declared.
Benjamin M Neale

Broad Institute of MIT and Harvard, Cambridge, United States

For correspondence
bneale@broadinstitute.org

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..
Vamsi K Mootha MD

Molecular Biology, Howard Hughes Medical Institute, Massachusetts General Hospital, Boston, United States

For correspondence
vamsi@hms.harvard.edu

Competing interests
Vamsi K Mootha, is an advisor to and receives compensation or equity from Janssen Pharmaceuticals, 5am Ventures, and Raze Therapeutics..

"This ORCID iD identifies the author of this article:" 0000-0001-9924-642X

Funding

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.

Copyright

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.