Article | Published:

Antagonistic pleiotropy and mutation accumulation influence human senescence and disease

Nature Ecology & Evolution volume 1, Article number: 0055 (2017) | Download Citation


Senescence has long been a public health challenge as well as a fascinating evolutionary problem. There is neither a universally accepted theory for its ultimate causes, nor a consensus about what may be its impact on human health. Here we test the predictions of two evolutionary explanations of senescence—mutation accumulation and antagonistic pleiotropy—which postulate that genetic variants with harmful effects in old ages can be tolerated, or even favoured, by natural selection at early ages. Using data from genome-wide association studies (GWAS), we study the effects of genetic variants associated with diseases appearing at different periods in life, when they are expected to have different impacts on fitness. Data fit theoretical expectations. Namely, we observe higher risk allele frequencies combined with large effect sizes for late-onset diseases, and detect a significant excess of early–late antagonistically pleiotropic variants that, strikingly, tend to be harboured by genes related to ageing. Beyond providing systematic, genome-wide evidence for evolutionary theories of senescence in our species and contributing to the long-standing question of whether senescence is the result of adaptation, our approach reveals relationships between previously unrelated pathologies, potentially contributing to tackling the problem of an ageing population.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


  1. 1.

    & Treat ageing. Nature 511, 405–407 (2014).

  2. 2.

    et al. A novel classification system for evolutionary aging theories. Front. Genet. 4, 1–8 (2013).

  3. 3.

    An Unsolved Problem of Biology: An Inaugural Lecture Delivered at University College, London, 6 December, 1951 (H. K. Lewis, 1952).

  4. 4.

    Pleiotropy, natural selection, and the evolution of senescence. Evolution 11, 398–411 (1957).

  5. 5.

    Evolution of ageing. Nature 170, 201–204 (1977).

  6. 6.

    Patterns of age-specific means and genetic variances of mortality rates predicted by the mutation-accumulation theory of ageing. J. Theor. Biol. 210, 47–65 (2001).

  7. 7.

    Evolution in Age-Structured Populations. (Cambridge Univ. Press, 1980).

  8. 8.

    & Age-dependent mutational effects curtail the evolution of senescence by antagonistic pleiotropy. J. Evol. Biol. 22, 2409–2419 (2009).

  9. 9.

    & The evolution of ageing and longevity. Proc. R. Soc. Lond. B 205, 531–546 (1979).

  10. 10.

    & Evolutionary theories of aging and longevity. ScientificWorldJournal 2, 339–356 (2002).

  11. 11.

    Survival costs of reproduction in Drosophila . Exp. Gerontol. 46, 369–375 (2011).

  12. 12.

    Ageing: diet and longevity in the balance. Nature 462, 989–990 (2009).

  13. 13.

    , , & Experimental evolution of aging, growth, and reproduction in fruit flies. Proc. Natl Acad. Sci. USA 97, 3309–3313 (2000).

  14. 14.

    Antagonistic pleiotropy, mutation accumulation, and human genetic disease. Genetica 91, 279–286 (1993).

  15. 15.

    Revisiting the antagonistic pleiotropy theory of aging: TOR-driven program and quasi-program. Cell Cycle 9, 3151–3156 (2010).

  16. 16.

    et al. The NCBI dbGaP database of genotypes and phenotypes. Nat. Genet. 39, 1181–1186 (2007).

  17. 17.

    et al. The European Genome-phenome Archive of human data consented for biomedical research. Nat. Genet. 47, 692–695 (2015).

  18. 18.

    et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42, D1001–D1006 (2014).

  19. 19.

    , , , & The hallmarks of aging. Cell 153, 1194–1217 (2013).

  20. 20.

    & High trans-ethnic replicability of GWAS results implies common causal variants. PLoS Genet. 9, e1003566 (2013).

  21. 21.

    & Medscape (Harvard Business School Cases 1, 2000);

  22. 22.

    Mechanisms in cardiovascular diseases: how useful are medical textbooks, eMedicine, and YouTube? Adv. Physiol. Educ. 38, 124–134 (2014).

  23. 23.

    Longevity: The Biology and Demography of Life Span (Princeton Univ. Press, 2003).

  24. 24.

    Tempo and mode in human evolution. Proc. Natl Acad. Sci. USA 91, 6780–6786 (1994).

  25. 25.

    & Older age becomes common late in human evolution. Proc. Natl Acad. Sci. USA 101, 10895–10900 (2004).

  26. 26.

    et al. Rate of de novo mutations and the importance of father’s age to disease risk. Nature 488, 471–475 (2012).

  27. 27.

    , & Meta-analysis of age-related gene expression profiles identifies common signatures of aging. Bioinformatics 25, 875–881 (2009).

  28. 28.

    et al. Geriatric muscle stem cells switch reversible quiescence into senescence. Nature 506, 316–321 (2014).

  29. 29.

    et al. The human ageing genomic resources: online databases and tools for biogerontologists. Aging Cell 8, 65–72 (2009).

  30. 30.

    et al. Human aging is characterized by focused changes in gene expression and deregulation of alternative splicing. Aging Cell 10, 868–878 (2011).

  31. 31.

    et al. Distinct DNA methylomes of newborns and centenarians. Proc. Natl Acad. Sci. USA 109, 10522–10527 (2012).

  32. 32.

    et al. Common risk alleles for inflammatory diseases are targets of recent positive selection. Am. J. Hum. Genet. 92, 517–529 (2013).

  33. 33.

    et al. Adaptations to climate-mediated selective pressures in humans. PLoS Genet. 7, e1001375 (2011).

  34. 34.

    , , & The evolution of trichromatic color vision by opsin gene duplication in new world and old world primates. Genome Res. 9, 629–638 (1999).

  35. 35.

    et al. Hierarchical boosting: a machine-learning framework to detect and classify hard selective sweeps in human populations. Bioinformatics 31, 3946–3952 (2015).

  36. 36.

    et al. No paradox, no progress: inverse cancer comorbidity in people with other complex diseases. Lancet Oncol. 12, 604–608 (2011).

  37. 37.

    et al. Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients. Nat. Commun. 5, 4022 (2014).

  38. 38.

    , , & The Internet school of medicine: use of electronic resources by medical trainees and the reliability of those resources. J. Surg. Educ. 72, 316–320 (2015).

  39. 39.

    , , , & Pleiotropy in complex traits: challenges and strategies. Nat. Rev. Genet. 14, 483–495 (2013).

  40. 40.

    & The many faces of pleiotropy. Trends Genet. 29, 66–73 (2013).

  41. 41.

    et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

  42. 42.

    et al. Estimation of effect size distribution from genome-wide association studies and implications for future discoveries. Nat. Genet. 42, 570–575 (2010).

Download references


This work was supported by Ministerio de Ciencia e Innovación, Spain (SAF2011-29239 to E.B., and BFU2012-38236 and BFU2015-68649-P to A.N.), by Direcció General de Recerca, Generalitat de Catalunya (2014SGR1311 and 2014SGR866), by the Spanish National Institute of Bioinfomatics of the Instituto de Salud Carlos III (PT13/0001/0026) and by FEDER (Fondo Europeo de Desarrollo Regional)/FSE (Fondo Social Europeo). U.M.M. is supported by Project 3 of NIGMS P01 GM099568 (B. Weir, University of Washington). E.B. is the recipient of an ICREA Academia Award. The authors thank J. Bertranpetit, P. Muñoz-Cánoves, R. Nesse and B. Charlesworth for helpful comments and advice. We also thank H. Laayouni, F. Casals and F. Calafell for comments on the manuscript, and the Navarro Lab members, especially D. Hartasánchez and M. Brasó, for discussion and comments.

Author information


  1. Institute of Evolutionary Biology (UPF-CSIC), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Catalonia 08003, Spain

    • Juan Antonio Rodríguez
    • , Urko M. Marigorta
    • , David A. Hughes
    • , Nino Spataro
    • , Elena Bosch
    •  & Arcadi Navarro
  2. School of Biology, Georgia Institute of Technology, Atlanta, Georgia 30332, USA

    • Urko M. Marigorta
  3. Centre de Regulació Genòmica (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Catalonia 08003, Spain

    • Arcadi Navarro
  4. National Institute for Bioinformatics (INB), Barcelona, Catalonia 08003, Spain

    • Arcadi Navarro
  5. Institució Catalana de Recerca i Estudis Avançats (ICREA), Catalonia 08003, Spain

    • Arcadi Navarro


  1. Search for Juan Antonio Rodríguez in:

  2. Search for Urko M. Marigorta in:

  3. Search for David A. Hughes in:

  4. Search for Nino Spataro in:

  5. Search for Elena Bosch in:

  6. Search for Arcadi Navarro in:


J.A.R., U.M.M., E.B. and A.N. conceived the study. J.A.R. and N.S. performed analyses. J.A.R., U.M.M., D.A.H., N.S., E.B. and A.N. analysed and interpreted the data. J.A.R., D.A.H., N.S., E.B. and A.N. wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Elena Bosch or Arcadi Navarro.

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    Supplementary Discussion; Supplementary Figures 1–6; Supplementary Tables 1–10

Excel files

  1. 1.

    Supplementary Data 1

    Estimated age of onset for the diseases used in the present study.

  2. 2.

    Supplementary Data 2

    List of the associations SNP-disease retrieved from the GWAS Catalog and used for the present study.

  3. 3.

    Supplementary Data 3

    List of the 266 pleiotropies found in the present study. These include both, the ones involving the same SNP in two diseases and these involving pairs of SNPs with r2 ≥ 0.8.

  4. 4.

    Supplementary Data 4

    Chi square 2×2 tables for number of pleiotropies inside each defined category, considering early–late thresholds from 10 to 60.

  5. 5.

    Supplementary Data 5

    Antagonistic early–late pleiotropies (r2 ≥ 0.8.) (n = 26) for an age threshold of 46 years as transition early–late.

  6. 6.

    Supplementary Data 6

    Pleiotropy and comorbidity overlap.

  7. 7.

    Supplementary Data 7

    Excess of pleiotropy in different gene sets at different levels, compared to genome-wide.

  8. 8.

    Supplementary Data 8

    Genes in the Sousa-Victor et al. ageing gene set28.

  9. 9.

    Supplementary Data 9

    Disease–SNP associations reported after crossing ageing genes from Sousa-Victor et al.28 with the GWAS Catalog used in present study.

  10. 10.

    Supplementary Data 10

    Pleiotropies found in the Sousa-Victor et al. ageing gene set28.

  11. 11.

    Supplementary Data 11

    Genes in the Magalhães et al. ageing gene set29.

  12. 12.

    Supplementary Data 12

    Disease–SNP associations reported after crossing genes from Magalhães et al.29 with the GWAS Catalog used in present study.

  13. 13.

    Supplementary Data 13

    Pleiotropies found in the Magalhães et al. ageing gene set29.

  14. 14.

    Supplementary Data 14

    Genes in the Harries et al. age expression changing gene set30.

  15. 15.

    Supplementary Data 15

    Disease–SNP associations reported after crossing ageing genes from Harries et al.30 with the GWAS Catalog used in the present study.

  16. 16.

    Supplementary Data 16

    Pleiotropies found in the Harries et al. age expression changing gene set30.

  17. 17.

    Supplementary Data 17

    Number of SNPs, diseases and average risk allelic frequency for early and late at each age threshold (10 to 60 years) from Fig. 1a.

  18. 18.

    Supplementary Data 18

    Number of SNPs, diseases and average genetic variance for early and late at each age threshold (10 to 60 years) from Fig. 1b.

About this article

Publication history





Further reading