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An open science study of ageing in companion dogs

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.

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

Data availability

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

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.

    CAS  Article  ADS  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).

    CAS  Article  ADS  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).

    CAS  Article  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).

    CAS  Article  Google Scholar 

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

    CAS  Article  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).

    CAS  Article  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 

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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.

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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.

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Correspondence to Daniel E. L. Promislow.

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The authors declare no competing interests.

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

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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

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