Human Ageing Genomic Resources: new and updated databases
Open accessPublished version of a preprint
Abstract
In spite of a growing body of research and data, human ageing remains a poorly understood process. Over 10 years ago we developed the Human Ageing Genomic Resources (HAGR), a collection of databases and tools for studying the biology and genetics of ageing. Here, we present HAGR's main functionalities, highlighting new additions and improvements. HAGR consists of six core databases: (i) the GenAge database of ageing-related genes, in turn composed of a dataset of >300 human ageing-related genes and a dataset with >2000 genes associated with ageing or longevity in model organisms; (ii) the AnAge database of animal ageing and longevity, featuring >4000 species; (iii) the GenDR database with >200 genes associated with the life-extending effects of dietary restriction; (iv) the LongevityMap database of human genetic association studies of longevity with >500 entries; (v) the DrugAge database with >400 ageing or longevity-associated drugs or compounds; (vi) the CellAge database with >200 genes associated with cell senescence. All our databases are manually curated by experts and regularly updated to ensure a high quality data. Cross-links across our databases and to external resources help researchers locate and integrate relevant information. HAGR is freely available online (http://genomics.senescence.info/).
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
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?
Other genes associated with brain aging, such as EGR1 and CP, exhibit different trends.
“Other genes known to be associated with brain aging, such as those for EGR1 (early growth response) and CP (ceruloplasmin), exhibit different trends (Fig. 2F and figs. S20 and S21) (21, 31).”
From Comprehensive functional genomic resource and integrative model for the human brain (2018) · cites this paper as [31]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
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 · see citing works
Extending Healthy Life Span—From Yeast to Humans
Fontana, L., Partridge, L., & Longo, V. D. (2010) · Science · doi:10.1126/science.1172539 · Cited by 3,023 · see citing works
Geroscience: Linking Aging to Chronic Disease
Kennedy, B. K., Berger, S. L., Brunet, A., Campisi, J., Cuervo, A. M., Epel, E. S., Franceschi, C., Lithgow, G. J., Morimoto, R. I., Pessin, J. E., et al. (2014) · Cell · doi:10.1016/j.cell.2014.10.039 · Cited by 2,764 · see citing works
Kaeberlein, M., McVey, M., & Guarente, L. (1999) · Genes & Development · doi:10.1101/gad.13.19.2570 · Cited by 2,253 · see citing works
Ageing as a Risk Factor for Disease
Niccoli, T., & Partridge, L. (2012) · Current Biology · doi:10.1016/j.cub.2012.07.024 · Cited by 1,831 · see citing works
mTOR is a key modulator of ageing and age-related disease
Johnson, S. C., Rabinovitch, P. S., & Kaeberlein, M. (2013) · Nature · doi:10.1038/nature11861 · Cited by 1,676 · see citing works
Stress, cell senescence and organismal ageing
de Magalhães, J. P., & Passos, J. F. (2018) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2017.07.001 · Cited by 316 · see citing works
Hierarchical deterioration of body systems in Werner's syndrome: Implications for normal ageing
Goto, M. (1997) · Mechanisms of Ageing and Development · doi:10.1016/s0047-6374(97)00111-5 · Cited by 240 · see citing works
Open-minded scepticism: inferring the causal mechanisms of human ageing from genetic perturbations
de Magalhães, J. P. (2005) · Ageing Research Reviews · doi:10.1016/j.arr.2004.05.003 · Cited by 54 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Comprehensive functional genomic resource and integrative model for the human brain
Wang, D., Liu, S., Warrell, J., Won, H., Shi, X., Navarro, F. C. P., Clarke, D., Gu, M., Emani, P., Yang, Y. T., et al. (2018) · Science · doi:10.1126/science.aat8464 · Cited by 965 · see citing works
The quest to slow ageing through drug discovery
Partridge, L., Fuentealba, M., & Kennedy, B. K. (2020) · Nature Reviews Drug Discovery · doi:10.1038/s41573-020-0067-7 · Cited by 468 · see citing works
Metformin restores CNS remyelination capacity by rejuvenating aged stem cells
Neumann, B., Baror, R., Zhao, C., Segel, M., Dietmann, S., Rawji, K. S., Foerster, S., McClain, C. R., Chalut, K., van Wijngaarden, P., et al. (2019) · Cell Stem Cell · doi:10.1016/j.stem.2019.08.015 · Cited by 449 · see citing works
A multidimensional systems biology analysis of cellular senescence in aging and disease
Avelar, R. A., Ortega, J. G., Tacutu, R., Tyler, E. J., Bennett, D., Binetti, P., Budovsky, A., Chatsirisupachai, K., Johnson, E., Murray, A., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-01990-9 · Cited by 399 · see citing works
Self-citationA Single-Cell Transcriptomic Atlas of Human Skin Aging
Zou, Z., Long, X., Zhao, Q., Zheng, Y., Song, M., Ma, S., Jing, Y., Wang, S., He, Y., Esteban, C. R., et al. (2021) · Developmental Cell · doi:10.1016/j.devcel.2020.11.002 · Cited by 402 · see citing works
Transcriptome signature of cellular senescence
Casella, G., Munk, R., Kim, K. M., Piao, Y., De, S., Abdelmohsen, K., & Gorospe, M. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz555 · Cited by 359 · see citing works
Artificial intelligence for aging and longevity research: Recent advances and perspectives
Zhavoronkov, A., Mamoshina, P., Vanhaelen, Q., Scheibye-Knudsen, M., Moskalev, A., & Aliper, A. (2019) · Ageing Research Reviews · doi:10.1016/j.arr.2018.11.003 · Cited by 246 · see citing works
The World Goes Bats: Living Longer and Tolerating Viruses
Gorbunova, V., Seluanov, A., & Kennedy, B. K. (2020) · Cell Metabolism · doi:10.1016/j.cmet.2020.06.013 · Cited by 162 · see citing works
Johnson, A. A., Shokhirev, M. N., Wyss-Coray, T., & Lehallier, B. (2020) · Ageing Research Reviews · doi:10.1016/j.arr.2020.101070 · Cited by 166 · see citing works
Lehallier, B., Shokhirev, M. N., Wyss‐Coray, T., & Johnson, A. A. (2020) · Aging Cell · doi:10.1111/acel.13256 · Cited by 106 · see citing works
Species comparison of liver proteomes reveals links to naked mole-rat longevity and human aging
Heinze, I., Bens, M., Calzia, E., Holtze, S., Dakhovnik, O., Sahm, A., Kirkpatrick, J. M., Szafranski, K., Romanov, N., Sama, S. N., et al. (2018) · BMC Biology · doi:10.1186/s12915-018-0547-y · Cited by 65 · see citing works
Temporal changes in the gene expression heterogeneity during brain development and aging
Işıldak, U., Somel, M., Thornton, J. M., & Dönertaş, H. M. (2020) · Scientific Reports · doi:10.1038/s41598-020-60998-0 · Cited by 53 · see citing works
Mazan-Mamczarz, K., Tsitsipatis, D., Childs, B. G., Carr, A. E., Dos Santos, C. R., Anerillas, C., Romero, B., Gregg, J. M., Henry-Smith, C., Okereke, A. N., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-00889-z · Cited by 37 · see citing works
Clement, J., Yan, Q., Agrawal, M., Coronado, R. E., Sturges, J. A., Horvath, M., Lu, A. T., Brooke, R. T., & Horvath, S. (2022) · Aging Cell · doi:10.1111/acel.13696 · Cited by 34 · see citing works
Wang, J., Zhou, X., Yu, P., Yao, J., Guo, P., Xu, Q., Zhao, Y., Wang, G., Li, Q., Zhu, X., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-00886-2 · Cited by 28 · see citing works
Self-citationThe protein inputs of an ultra-predictive aging clock represent viable anti-aging drug targets
Johnson, A. A., Shokhirev, M. N., & Lehallier, B. (2021) · Ageing Research Reviews · doi:10.1016/j.arr.2021.101404 · Cited by 22 · see citing works
Single-cell and spatial detection of senescent cells using DeepScence
Qu, Y., Ji, B., Dong, R., Gu, L., Chan, C., Xie, J., Glass, C., Wang, X.-F., Nixon, A. B., & Ji, Z. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.101035 · Cited by 30 · see citing works
- Authors
- Tacutu, R., Thornton, D., Johnson, E., Budovsky, A., Barardo, D., Craig, T., Diana, E., Lehmann, G., Toren, D., Wang, J., Fraifeld, V. E., & de Magalhães, J. P.
- Affiliations
- University of Liverpool (GB)
- Ben-Gurion University of the Negev (IL)
- National University of Singapore (SG)
- ORCID
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-11-07
- Volume
- 46
- Issue
- D1
- Pages
- D1083-D1090
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC5753192
- OpenAlex
- W2767517408
- Cited by
- 738
- References
- 9
- Citations by year
- 2026: 58 · 2025: 87 · 2024: 118 · 2023: 131 · 2022: 105 · 2021: 111 · 2020: 57 · 2019: 54 · 2018: 16 · 2015: 1
- FWCI
- 14.79
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- National University of Singapore (submittedVersion, cc-by-nc)
- Funders
- Wellcome Trust (104978, 104978/Z/14/Z)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Health Sciences › Medicine › Physiology › Telomeres, Telomerase, and Senescence
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- human aging, longevity, dietary restriction, lifespan extension, cellular senescence, genetic association studies, model organisms
- MeSH terms
- Aging, Animals, Disease, Humans, Longevity, Genetic Variation, Cellular Senescence, Genomics, Databases, Genetic
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
