Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

An open science study of ageing in companion dogs

Nature volume 602, pages 51–57 (2022)Cite this article

Subjects

An Author Correction to this article was published on 08 August 2022

This article has been updated

Abstract

The Dog Aging Project is a long-term longitudinal study of ageing in tens of thousands of companion dogs. The domestic dog is among the most variable mammal species in terms of morphology, behaviour, risk of age-related disease and life expectancy. Given that dogs share the human environment and have a sophisticated healthcare system but are much shorter-lived than people, they offer a unique opportunity to identify the genetic, environmental and lifestyle factors associated with healthy lifespan. To take advantage of this opportunity, the Dog Aging Project will collect extensive survey data, environmental information, electronic veterinary medical records, genome-wide sequence information, clinicopathology and molecular phenotypes derived from blood cells, plasma and faecal samples. Here, we describe the specific goals and design of the Dog Aging Project and discuss the potential for this open-data, community science study to greatly enhance understanding of ageing in a genetically variable, socially relevant species living in a complex environment.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Structure of the DAP cohorts.
Fig. 2: DAP integration.
Fig. 3: Biospecimen and environmental measures.

Similar content being viewed by others

Data availability

The data used to generate Fig. 1b, c are freely available for download at https://data.dogagingproject.org.

Change history

References

  1. Kaeberlein, M., Rabinovitch, P. S. & Martin, G. M. Healthy aging: the ultimate preventative medicine. Science 350, 1191–1193 (2015). This paper makes a compelling argument that treatments that target the underlying causes of ageing could ameliorate the effects of multiple age-related diseases.

    Article  ADS  CAS  Google Scholar 

  2. Melzer, D., Hurst, A. J. & Frayling, T. Genetic variation and human aging: progress and prospects. J. Gerontol. A Biol. Sci. Med. Sci. 62, 301–307 (2007).

    Article  Google Scholar 

  3. Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).

    Article  ADS  CAS  Google Scholar 

  4. Kaeberlein, M., Creevy, K. E. & Promislow, D. E. L. The Dog Aging Project: translational geroscience in companion animals. Mamm. Genome 27, 279–288 (2016).

    Article  CAS  Google Scholar 

  5. Boyko, A. R. et al. A simple genetic architecture underlies morphological variation in dogs. PLoS Biol. 8, e1000451 (2010). This paper demonstrated the tremendous power of the domestic dog as a model for mapping natural variation for complex traits.

    Article  Google Scholar 

  6. Minnema, L. et al. Correlation of artemin and GFRα3 with osteoarthritis pain: early evidence from naturally occurring osteoarthritis-associated chronic pain in dogs. Front. Neurosci. 14, 77 (2020).

    Article  Google Scholar 

  7. Harris, P. A. et al. Research Electronic Data Capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J. Biomed. Inform. 42, 377–381 (2009).

    Article  Google Scholar 

  8. Harris, P. A. et al. The REDCap consortium: building an international community of software platform partners. J. Biomed. Inform. 95, 103208 (2019).

    Article  Google Scholar 

  9. Li, J. H., Mazur, C. A., Berisa, T. & Pickrell, J. K. Low-pass sequencing increases the power of GWAS and decreases measurement error of polygenic risk scores compared to genotyping arrays. Genome Res. 31, 529–537 (2021).

    Article  Google Scholar 

  10. Franceschi, C. & Campisi, J. Chronic inflammation (inflammaging) and its potential contribution to age-associated diseases. J. Gerontol. A Biol. Sci. Med. Sci. 69, S4–S9 (2014).

    Article  Google Scholar 

  11. Lippi, G. et al. Preanalytical challenges—time for solutions. Clin. Chem. Lab. Med. 57, 974–981 (2019).

    Article  CAS  Google Scholar 

  12. Haumann, R. & Verspaget, H. W. Quality-assured biobanking: the Leiden University Medical Center model. Methods Mol. Biol. 1730, 361–370 (2018).

    Article  CAS  Google Scholar 

  13. Simeon-Dubach, D., Zeisberger, S. M. & Hoerstrup, S. P. Quality assurance in biobanking for pre-clinical research. Transfus Med. Hemother. 43, 353–357 (2016).

    Article  Google Scholar 

  14. US Department of the Census. American Community Survey (ACS): Public Use Microdata Sample (PUMS), 2009. https://doi.org/10.3886/ICPSR33802.v1 (Inter-university Consortium for Political and Social Research, 2013).

  15. Kim, S.-Y. et al. Concentrations of criteria pollutants in the contiguous U.S., 1979–2015: role of prediction model parsimony in integrated empirical geographic regression. PLoS ONE 15 e0228535 (2020).

    Article  CAS  Google Scholar 

  16. Vose, R. S. et al. NOAA Monthly U.S. Climate Divisional Database (NClimDiv). https://doi.org/10.7289/V5M32STR (NOAA National Climatic Data Center, 2014).

  17. Mooney, S. J. et al. Residential neighborhood features associated with objectively measured walking near home: revisiting walkability using the Automatic Context Measurement Tool (ACMT). Health Place 63, 102332 (2020).

    Article  Google Scholar 

  18. Wilfond, B. S., Porter, K. M., Creevy, K. E., Kaeberlein, M. & Promislow, D. Research to promote longevity and health span in companion dogs: a pediatric perspective. Am. J. Bioeth. 18 64–65 (2018).

    Article  Google Scholar 

  19. Taylor, H. A., Morales, C., Johnson, L.-M. & Wilfond, B. S. A randomized trial of rapamycin to increase longevity and healthspan in companion animals: navigating the boundary between protections for animal research and human subjects research. Am. J. Bioeth. 18, 58–59 (2018).

    Article  Google Scholar 

  20. Bisong, E. in Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners (ed. Bisong, E.) 7–10 (Apress, 2019).

  21. Salvin, H. E., McGreevy, P. D., Sachdev, P. S. & Valenzuela, M. J. The canine cognitive dysfunction rating scale (CCDR): a data-driven and ecologically relevant assessment tool. Vet. J. 188, 331–336 (2011).

    Article  Google Scholar 

Download references

Acknowledgements

All dog research described here, including informed owner consent, is approved by the Texas A&M University Institutional Animal Care and Use Committee, under AUPs 2018-0401 CAM and 2018-0368 CAM. The DAP is supported by grant U19AG057377 from the National Institute on Aging, a part of the National Institutes of Health, and by private donations. We thank S. Moon for help in preparing figures.

Author information

Authors and Affiliations

  1. Department of Small Animal Clinical Sciences, Texas A&M University College of Veterinary Medicine & Biomedical Sciences, College Station, TX, USA

    Kate E. Creevy, Brian G. Barnett, Lucy Chou, Jeremy Evans, Jonathan M. Levine, Kellyn E. McNulty, Amanda K. Tinkle & M. Katherine Tolbert

  2. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA

    Joshua M. Akey & William Thistlethwaite

  3. Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA, USA

    Matt Kaeberlein, Daniel E. L. Promislow, Brooke Benton, Erica C. Jonlin & Silvan R. Urfer

  4. Department of Biology, University of Washington, Seattle, WA, USA

    Daniel E. L. Promislow

  5. Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel

    Elhanan Borenstein

  6. Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel

    Elhanan Borenstein

  7. Santa Fe Institute, Santa Fe, NM, USA

    Elhanan Borenstein

  8. Cornell Veterinary Biobank, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA

    Marta G. Castelhano

  9. Department of Sociology, University of Washington, Seattle, WA, USA

    Devin Collins, Kyle Crowder & Hannah Lee

  10. Department of Small Animal Medicine and Surgery, University of Georgia College of Veterinary Medicine, Athens, GA, USA

    Amanda E. Coleman

  11. Center for Studies in Demography and Ecology, Seattle, WA, USA

    Kyle Crowder & Matthew D. Dunbar

  12. Department of Veterinary Physiology and Pharmacology, Texas A&M University College of Veterinary Medicine & Biomedical Sciences, College Station, TX, USA

    Virginia R. Fajt

  13. Department of Family Medicine, University of Washington, Seattle, WA, USA

    Annette L. Fitzpatrick

  14. Department of Epidemiology, University of Washington, Seattle, WA, USA

    Annette L. Fitzpatrick & Stephen M. Schwartz

  15. Department of Global Health, University of Washington, Seattle, WA, USA

    Annette L. Fitzpatrick

  16. Department of Veterinary Pathobiology, Texas A&M University College of Veterinary Medicine & Biomedical Sciences, College Station, TX, USA

    Unity Jeffery

  17. Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA

    Erica C. Jonlin

  18. Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA

    Elinor K. Karlsson & Kathleen Morrill

  19. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Elinor K. Karlsson

  20. Department of Biostatistics, University of Washington, Seattle, WA, USA

    Kathleen F. Kerr, Robyn L. McClelland & Yunbi Nam

  21. Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Jing Ma

  22. Department of Population Health Sciences, Virginia-Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA, USA

    Audrey Ruple

  23. Epidemiology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Stephen M. Schwartz

  24. Collaborative Health Studies Coordinating Center, Department of Biostatistics, University of Washington, Seattle, WA, USA

    Sandi Shrager

  25. School of Life Sciences, Arizona State University, Tempe, AZ, USA

    Noah Snyder-Mackler

  26. Center for Evolution and Medicine, Arizona State University, Tempe, AZ, USA

    Noah Snyder-Mackler

  27. School for Human Evolution and Social Change, Arizona State University, Tempe, AZ, USA

    Noah Snyder-Mackler

  28. Treuman Katz Center for Pediatric Bioethics, Seattle Children’s Research Institute, Seattle, WA, USA

    Benjamin S. Wilfond

  29. Division of Bioethics and Palliative Care, Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA

    Benjamin S. Wilfond

Authors
  1. Kate E. Creevy
    View author publications

    Search author on:PubMed Google Scholar

  2. Joshua M. Akey
    View author publications

    Search author on:PubMed Google Scholar

  3. Matt Kaeberlein
    View author publications

    Search author on:PubMed Google Scholar

  4. Daniel E. L. Promislow
    View author publications

    Search author on:PubMed Google Scholar

Consortia

The Dog Aging Project Consortium

  • Kate E. Creevy
  • , Joshua M. Akey
  • , Matt Kaeberlein
  • , Daniel E. L. Promislow
  • , Brian G. Barnett
  • , Brooke Benton
  • , Elhanan Borenstein
  • , Marta G. Castelhano
  • , Lucy Chou
  • , Devin Collins
  • , Amanda E. Coleman
  • , Kyle Crowder
  • , Matthew D. Dunbar
  • , Jeremy Evans
  • , Virginia R. Fajt
  • , Annette L. Fitzpatrick
  • , Unity Jeffery
  • , Erica C. Jonlin
  • , Elinor K. Karlsson
  • , Kathleen F. Kerr
  • , Hannah Lee
  • , Jonathan M. Levine
  • , Jing Ma
  • , Robyn L. McClelland
  • , Kellyn E. McNulty
  • , Kathleen Morrill
  • , Yunbi Nam
  • , Audrey Ruple
  • , Stephen M. Schwartz
  • , Sandi Shrager
  • , Noah Snyder-Mackler
  • , William Thistlethwaite
  • , Amanda K. Tinkle
  • , M. Katherine Tolbert
  • , Silvan R. Urfer
  •  & Benjamin S. Wilfond

Contributions

K.E.C., M.K. and D.E.L.P. conceived of the DAP; J.M.A., K.E.C., M.K. and D.E.L.P. wrote the initial draft of this paper. All authors, including consortium authors, have been involved in the design and implementation of DAP goals, infrastructure and activities, and they have had the opportunity to participate in editing both form and content of this paper and have approved the final version.

Corresponding author

Correspondence to Daniel E. L. Promislow.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review information

Nature thanks Steven Austad, Dario Valenzano and Eric Verdin for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Creevy, K.E., Akey, J.M., Kaeberlein, M. et al. An open science study of ageing in companion dogs. Nature 602, 51–57 (2022). https://doi.org/10.1038/s41586-021-04282-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41586-021-04282-9

This article is cited by

Comments

Commenting on this article is now closed.

  1. David Gardner

    Authors,

    A great project and resource. Cannot agree more with the objectives and 'one-health' approach - using real animal models of disease to look at the real-world G x E interactions. I note one omission: i couldnt see anywhere reference to consideration of nutrition as a determinant of 'health'. All canine participants fed 'complete' diet (despite many not being entirely complete), wet or dry kibble? are you recording these sort of factors that could add significant variability to your dataset.

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research