The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Common genetic associations between age-related diseases
Published version of a preprint
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“For longevity, negative pleiotropic effects were more frequently observed among late-onset diseases from the cardiovascular domain48”
From Genetic trade-offs in fertility and longevity explain the maintenance of disease-associated alleles in humans (2026) · cites this paper as [48]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., et al. (2019) · Journal of Open Source Software · doi:10.21105/joss.01686 · Cited by 22,138
A global reference for human genetic variation
The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247
The genetical theory of natural selection
Fisher, R. A. (1930) · doi:10.5962/bhl.title.27468 · Cited by 17,617
López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2013) · Cell · doi:10.1016/j.cell.2013.05.039 · Cited by 14,982
Self-citationSecond-generation PLINK: rising to the challenge of larger and richer datasets
Chang, C. C., Chow, C. C., Tellier, L. C., Vattikuti, S., Purcell, S. M., & Lee, J. J. (2015) · Gigascience · doi:10.1186/s13742-015-0047-8 · Cited by 12,709
Finding Groups in Data: An Introduction to Cluster Analysis
Kaufman, L., & Rousseeuw, P. J. (1990) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316801 · Cited by 7,344
Gene ontology analysis for RNA-seq: accounting for selection bias
Young, M. D., Wakefield, M. J., Smyth, G. K., & Oshlack, A. (2010) · Genome Biology · doi:10.1186/gb-2010-11-2-r14 · Cited by 7,107
LD Score regression distinguishes confounding from polygenicity in genome-wide association studies
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Bulik-Sullivan, B. K., Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., & Neale, B. M. (2015) · Nature Genetics · doi:10.1038/ng.3211 · Cited by 5,559
The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans
The GTEx Consortium, Ardlie, K. G., Deluca, D. S., Segrè, A. V., Sullivan, T. J., Young, T. R., Gelfand, E. T., Trowbridge, C. A., Maller, J. B., Tukiainen, T., et al. (2015) · Science · doi:10.1126/science.1262110 · Cited by 5,062
Flagged first, then by citations
Research works printed but not linked: 59
- Crimmins, E. M. Lifespan and healthspan: past, present, and promise. Gerontologist 55, 901–911 (2015). · DOI · Paper page
- Partridge, L., Deelen, J. & Slagboom, P. E. Facing up to the global challenges of ageing. Nature 561, 45–56 (2018). · DOI · Paper page
- Flatt, T. & Partridge, L. Horizons in the evolution of aging. BMC Biol. 16, 93 (2018). · DOI
- Medvedev, Z. A. An attempt at a rational classification of theories of ageing. Biol. Rev. Camb. Philos. Soc. 65, 375–398 (1990). · DOI
- Medawar, P. B. Unsolved problem of biology. Med. J. Aust. 1, 854–855 (1953). · DOI
- Williams, G. C. Pleiotropy, natural selection, and the evolution of senescence. Evolution 11, 398–411 (1957). · DOI · Paper page
- Pickrell, J. K. et al. Detection and interpretation of shared genetic influences on 42 human traits. Nat. Genet. 48, 709–717 (2016). · DOI · Paper page
- Cross-Disorder Group of the Psychiatric Genomics Consortium et al. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat. Genet. 45, 984–994 (2013). · DOI · Paper page
- Cortes, A., Albers, P. K., Dendrou, C. A., Fugger, L. & McVean, G. Identifying cross-disease components of genetic risk across hospital data in the UK Biobank. Nat. Genet. 52, 126–134 (2020). · DOI
- Solovieff, N., Cotsapas, C., Lee, P. H., Purcell, S. M. & Smoller, J. W. Pleiotropy in complex traits: challenges and strategies. Nat. Rev. Genet. 14, 483–495 (2013). · DOI · Paper page
- Parkes, M., Cortes, A., van Heel, D. A. & Brown, M. A. Genetic insights into common pathways and complex relationships among immune-mediated diseases. Nat. Rev. Genet. 14, 661–673 (2013). · DOI
- Bien, S. A. & Peters, U. Moving from one to many: insights from the growing list of pleiotropic cancer risk genes. Br. J. Cancer 120, 1087–1089 (2019). · DOI
- Johnson, S. C., Dong, X., Vijg, J. & Suh, Y. Genetic evidence for common pathways in human age-related diseases. Aging Cell 14, 809–817 (2015). · DOI · Paper page
- Fernandes, M. et al. Systematic analysis of the gerontome reveals links between aging and age-related diseases. Hum. Mol. Genet. 25, 4804–4818 (2016).
- Wang, J., Zhang, S., Wang, Y., Chen, L. & Zhang, X.-S. Disease-aging network reveals significant roles of aging genes in connecting genetic diseases. PLoS Comput. Biol. 5, e1000521 (2009). · DOI
- Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018). · DOI · Paper page
- Chouakria, A. D. & Nagabhushan, P. N. Adaptive dissimilarity index for measuring time series proximity. Adv. Data Anal. Classif. 1, 5–21 (2007). · DOI
- Loh, P.-R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015). · DOI · Paper page
- Anderson, C. A. et al. Data quality control in genetic case-control association studies. Nat. Protoc. 5, 1564–1573 (2010). · DOI · Paper page
- Zhou, W. et al. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nat. Genet. 50, 1335–1341 (2018). · DOI · Paper page
- Pe’er, I., Yelensky, R., Altshuler, D. & Daly, M. Estimation of the multiple testing burden for genomewide association studies of common variants. Nat. Preced. https://doi.org/10.1038/npre.2007.359.1 (2007). · DOI · Paper page
- Panagiotou, O. A. & Ioannidis, J. P. A. What should the genome-wide significance threshold be? Empirical replication of borderline genetic associations. Int. J. Epidemiol. 41, 273–286 (2012). · DOI · Paper page
- Human genome region MHC (Genome Reference Consortium, accessed June 2019); https://www.ncbi.nlm.nih.gov/grc/human/regions/MHC?asm=GRCh37
- Berisa, T. & Pickrell, J. K. Approximately independent linkage disequilibrium blocks in human populations. Bioinformatics 32, 283–285 (2016). · DOI
- Ning, Z., Pawitan, Y. & Shen, X. High-definition likelihood inference of genetic correlations across human complex traits. Nat. Genet. https://doi.org/10.1038/s41588-020-0653-y (2020). · DOI
- Gamazon, E. R. et al. Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation. Nat. Genet. 50, 956–967 (2018). · DOI · Paper page
- Tacutu, R. et al. Human ageing genomic resources: new and updated databases. Nucleic Acids Res. 46, D1083–D1090 (2018). · DOI · Paper page
- Barardo, D. et al. The DrugAge database of aging-related drugs. Aging Cell 16, 594–597 (2017). · DOI · Paper page
- Avelar, R. A. et al. A multidimensional systems biology analysis of cellular senescence in aging and disease. Genome Biol. 21, 91 (2020). · DOI · Paper page
- Adelman, E. R. et al. Aging human hematopoietic stem cells manifest profound epigenetic reprogramming of enhancers that may predispose to leukemia. Cancer Discov. 9, 1080–1101 (2019). · DOI
- Marttila, S. et al. Ageing-associated changes in the human DNA methylome: genomic locations and effects on gene expression. BMC Genomics 16, 179 (2015). · DOI
- Sun, L.-Q. et al. Growth retardation and premature aging phenotypes in mice with disruption of the SNF2-like gene, PASG. Genes Dev. 18, 1035–1046 (2004). · DOI
- Rodríguez, J. A. et al. Antagonistic pleiotropy and mutation accumulation influence human senescence and disease. Nat. Ecol. Evol. 1, 55 (2017). · DOI · Paper page
- Bitarello, B. D. et al. Signatures of long-term balancing selection in human genomes. Genome Biol. Evol. 10, 939–955 (2018). · DOI
- Nielsen, R. et al. A scan for positively selected genes in the genomes of humans and chimpanzees. PLoS Biol. 3, e170 (2005). · DOI
- Shultz, A. J. & Sackton, T. B. Immune genes are hotspots of shared positive selection across birds and mammals. eLife 8, e41815 (2019). · DOI
- Wolfson, M., Budovsky, A., Tacutu, R. & Fraifeld, V. The signaling hubs at the crossroad of longevity and age-related disease networks. Int. J. Biochem. Cell Biol. 41, 516–520 (2009). · DOI
- Kanchi, K. L. et al. Integrated analysis of germline and somatic variants in ovarian cancer. Nat. Commun. 5, 3156 (2014). · DOI
- Khurana, E. et al. Role of non-coding sequence variants in cancer. Nat. Rev. Genet. 17, 93–108 (2016). · DOI
- Poduri, A., Evrony, G. D., Cai, X. & Walsh, C. A. Somatic mutation, genomic variation, and neurological disease. Science 341, 1237758 (2013). · DOI · Paper page
- Sánchez-Valle, J. et al. Interpreting molecular similarity between patients as a determinant of disease comorbidity relationships. Nat. Commun. 11, 2854 (2020). · DOI
- Gutiérrez-Sacristán, A. et al. comoRbidity: an R package for the systematic analysis of disease comorbidities. Bioinformatics 34, 3228–3230 (2018). · DOI
- Montero, P. & Vilar, J. TSclust: an R package for time series clustering. J. Stat. Software, Articles 62, 1–43 (2014).
- Berndt, D. J. & Clifford, J. Using dynamic time warping to find patterns in time series. In Proc. 3rd International Conference on Knowledge Discovery and Data Mining 359–370 (AAAI Press, 1994).
- Tibshirani, R., Walther, G. & Hastie, T. Estimating the number of clusters in a data set via the gap statistic. J. R. Stat. Soc. B 63, 411–423 (2001). · DOI · Paper page
- Stephenson, J. D., Laskowski, R. A., Nightingale, A., Hurles, M. E. & Thornton, J. M. VarMap: a web tool for mapping genomic coordinates to protein sequence and structure and retrieving protein structural annotations. Bioinformatics https://doi.org/10.1093/bioinformatics/btz482 (2019). · DOI
- Obenchain, V. et al. VariantAnnotation: a Bioconductor package for exploration and annotation of genetic variants. Bioinformatics 30, 2076–2078 (2014). · DOI
- Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013). · DOI
- Durinck, S., Spellman, P. T., Birney, E. & Huber, W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat. Protoc. 4, 1184–1191 (2009). · DOI
- Lawrence, M., Gentleman, R. & Carey, V. rtracklayer: an R package for interfacing with genome browsers. Bioinformatics 25, 1841–1842 (2009). · DOI
- Lage, K. et al. A large-scale analysis of tissue-specific pathology and gene expression of human disease genes and complexes. Proc. Natl Acad. Sci. USA 105, 20870–20875 (2008). · DOI · Paper page
- Buniello, A. et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47, D1005–D1012 (2019). · DOI · Paper page
- Kim, S. et al. PubChem 2019 update: improved access to chemical data. Nucleic Acids Res. 47, D1102–D1109 (2019). · DOI · Paper page
- Chambers, J. et al. UniChem: a unified chemical structure cross-referencing and identifier tracking system. J. Cheminform. 5, 3 (2013). · DOI
- Gaulton, A. et al. The ChEMBL database in 2017. Nucleic Acids Res. 45, D945–D954 (2017). · DOI
- Cotto, K. C. et al. DGIdb 3.0: a redesign and expansion of the drug–gene interaction database. Nucleic Acids Res. 46, D1068–D1073 (2018). · DOI
- Gene Ontology Consortium.The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res. 47, D330–D338 (2019). · DOI · Paper page
- Benjamini, Y. & Yekutieli, D. The control of the false discovery rate in multiple testing under dependency. Ann. Stat. 29, 1165–1188 (2001). · DOI · Paper page
- Ooms, J. The jsonlite package: a practical and consistent mapping between JSON data and R objects. Preprint at arXiv https://arxiv.org/abs/1403.2805 (2014). · arXiv
Cited by
Sorted by citations
Plasma proteomic associations with genetics and health in the UK Biobank
Sun, B. B., Chiou, J., Traylor, M., Benner, C., Hsu, Y.-H., Richardson, T. G., Surendran, P., Mahajan, A., Robins, C., Vasquez-Grinnell, S. G., et al. (2023) · Nature · doi:10.1038/s41586-023-06592-6 · Cited by 1,880
Murphy, A. E., Askarova, A., Lenhard, B., Skene, N. G., & Marzi, S. J. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkae1212 · Cited by 10
Brigos-Barril, E., Vasallo, C., Farré, X., Morcillo-Suárez, C., Polo-Alonso, S., Rodríguez-Fernández, B., Valenzuela, A., Sarabia, C., Bosch, E., Laayouni, H., et al. (2026) · Nature Ecology & Evolution · doi:10.1038/s41559-026-03140-z · Cited by 6
Genetic analyses reveal association between rosacea and cardiovascular diseases
Gao, Y., Xu, C., & Yan, Q. (2025) · Scientific Reports · doi:10.1038/s41598-025-91240-4 · Cited by 4
“The GWAS dataset for HTN were included 129,909 cases and 354,689 controls 21 .”
Metadata
- Authors
- Dönertaş, H. M., Fabian, D. K., Fuentealba, M., Partridge, L., & Thornton, J. M.
- Venue
- Nature Aging
- Published
- 2021-04-08
- Volume
- 1
- Issue
- 4
- Pages
- 400-412
- ISSN
- 2662-8465
- PMC
- PMC7610725
- Cited by
- 346
- References
- 17
- Language
- en
- Licence
- Funders
- Wellcome Trust (098565/Z/12/Z)
- European Molecular Biology Laboratory (-)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review