Genetics of extreme human longevity to guide drug discovery for healthy ageing

Abstract

Ageing is the greatest risk factor for most common chronic human diseases, and it therefore is a logical target for developing interventions to prevent, mitigate or reverse multiple age-related morbidities. Over the past two decades, genetic and pharmacologic interventions targeting conserved pathways of growth and metabolism have consistently led to substantial extension of the lifespan and healthspan in model organisms as diverse as nematodes, flies and mice. Recent genetic analysis of long-lived individuals is revealing common and rare variants enriched in these same conserved pathways that significantly correlate with longevity. In this Perspective, we summarize recent insights into the genetics of extreme human longevity and propose the use of this rare phenotype to identify genetic variants as molecular targets for gaining insight into the physiology of healthy ageing and the development of new therapies to extend the human healthspan.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Examples of conserved pathways of ageing.
Fig. 2: Genetic architecture of human ageing.
Fig. 3: Discovery of causal variants and genes in genetic studies of complex human traits.
Fig. 4: An integrated approach to drug discovery for healthy human ageing, on the basis of the genomic analysis of extreme longevity.
Fig. 5: Schematic illustration of the InPOINT pipeline.

References

  1. 1.

    World Population Ageing 2017 (United Nations, 2017).

  2. 2.

    Partridge, L., Deelen, J. & Slagboom, P. E. Facing up to the global challenges of ageing. Nature 561, 45–56 (2018).

    CAS  PubMed  Google Scholar 

  3. 3.

    Barnett, K. et al. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet 380, 37–43 (2012).

    PubMed  Google Scholar 

  4. 4.

    Marengoni, A. et al. Aging with multimorbidity: a systematic review of the literature. Ageing Res. Rev. 10, 430–439 (2011).

    PubMed  Google Scholar 

  5. 5.

    Goldman, D. The economic promise of delayed aging. Cold Spring Harb. Perspect. Med. 6, a025072 (2015).

    PubMed  Google Scholar 

  6. 6.

    Fontana, L., Partridge, L. & Longo, V. D. Extending healthy life span: from yeast to humans. Science 328, 321–326 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    van der Spoel, E. et al. Association analysis of insulin-like growth factor-1 axis parameters with survival and functional status in nonagenarians of the Leiden Longevity Study. Aging (Albany N.Y.) 7, 956–963 (2015).

    Google Scholar 

  8. 8.

    Passtoors, W. M. et al. Gene expression analysis of mTOR pathway: association with human longevity. Aging Cell 12, 24–31 (2013).

    CAS  PubMed  Google Scholar 

  9. 9.

    Kenyon, C., Chang, J., Gensch, E., Rudner, A. & Tabtiang, R. A C. elegans mutant that lives twice as long as wild type. Nature 366, 461–464 (1993).

    CAS  PubMed  Google Scholar 

  10. 10.

    Fontana, L. & Partridge, L. Promoting health and longevity through diet: from model organisms to humans. Cell 161, 106–118 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Vellai, T. et al. Genetics: influence of TOR kinase on lifespan in C. elegans. Nature 426, 620 (2003).

    CAS  PubMed  Google Scholar 

  12. 12.

    Kapahi, P. et al. Regulation of lifespan in Drosophila by modulation of genes in the TOR signaling pathway. Curr. Biol. 14, 885–890 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Johnson, S. C. et al. mTOR inhibition alleviates mitochondrial disease in a mouse model of Leigh syndrome. Science 342, 1524–1528 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Zhang, Q. et al. Systems-level analysis of human aging genes shed new light on mechanisms of aging. Hum. Mol. Genet. 25, 2934–2947 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Johnson, S. C., Rabinovitch, P. S. & Kaeberlein, M. mTOR is a key modulator of ageing and age-related disease. Nature 493, 338–345 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Harrison, D. E. et al. Rapamycin fed late in life extends lifespan in genetically heterogeneous mice. Nature 460, 392–395 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Wilkinson, J. E. et al. Rapamycin slows aging in mice. Aging Cell 11, 675–682 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Bjedov, I. et al. Mechanisms of life span extension by rapamycin in the fruit fly Drosophila melanogaster. Cell Metab. 11, 35–46 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Powers, R. W. III, Kaeberlein, M., Caldwell, S. D., Kennedy, B. K. & Fields, S. Extension of chronological life span in yeast by decreased TOR pathway signaling. Genes Dev. 20, 174–184 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Robida-Stubbs, S. et al. TOR signaling and rapamycin influence longevity by regulating SKN-1/Nrf and DAF-16/FoxO. Cell Metab. 15, 713–724 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Miller, R. A. et al. An Aging Interventions Testing Program: study design and interim report. Aging Cell 6, 565–575 (2007).

    CAS  PubMed  Google Scholar 

  22. 22.

    Nadon, N. L. et al. Design of aging intervention studies: the NIA interventions testing program. Age (Dordr.) 30, 187–199 (2008).

    CAS  Google Scholar 

  23. 23.

    Harrison, D. E. et al. Acarbose, 17-α-estradiol, and nordihydroguaiaretic acid extend mouse lifespan preferentially in males. Aging Cell 13, 273–282 (2014).

    CAS  PubMed  Google Scholar 

  24. 24.

    Barzilai, N., Crandall, J. P., Kritchevsky, S. B. & Espeland, M. A. Metformin as a tool to target aging. Cell Metab. 23, 1060–1065 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Justice, J. N. et al. Development of clinical trials to extend healthy lifespan. Cardiovasc Endocrinol Metab 7, 80–83 (2018).

    PubMed  PubMed Central  Google Scholar 

  26. 26.

    de Magalhães, J. P. Why genes extending lifespan in model organisms have not been consistently associated with human longevity and what it means to translation research. Cell Cycle 13, 2671–2673 (2014).

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Perls, T. T., Bubrick, E., Wager, C. G., Vijg, J. & Kruglyak, L. Siblings of centenarians live longer. Lancet 351, 1560 (1998).

    CAS  PubMed  Google Scholar 

  29. 29.

    Schoenmaker, M. et al. Evidence of genetic enrichment for exceptional survival using a family approach: the Leiden Longevity Study. Eur. J. Hum. Genet. 14, 79–84 (2006).

    PubMed  Google Scholar 

  30. 30.

    Herskind, A. M. et al. The heritability of human longevity: a population-based study of 2872 Danish twin pairs born 1870-1900. Hum. Genet. 97, 319–323 (1996).

    CAS  PubMed  Google Scholar 

  31. 31.

    Christensen, K., Johnson, T. E. & Vaupel, J. W. The quest for genetic determinants of human longevity: challenges and insights. Nat. Rev. Genet. 7, 436–448 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Murabito, J. M., Yuan, R. & Lunetta, K. L. The search for longevity and healthy aging genes: insights from epidemiological studies and samples of long-lived individuals. J. Gerontol. A Biol. Sci. Med. Sci. 67, 470–479 (2012).

    PubMed  Google Scholar 

  33. 33.

    Perls, T. T. et al. Life-long sustained mortality advantage of siblings of centenarians. Proc. Natl Acad. Sci. USA 99, 8442–8447 (2002).

    CAS  PubMed  Google Scholar 

  34. 34.

    Robine, J. M. & Allard, M. The oldest human. Science 279, 1834–1835 (1998).

    CAS  PubMed  Google Scholar 

  35. 35.

    Gögele, M. et al. Heritability analysis of life span in a semi-isolated population followed across four centuries reveals the presence of pleiotropy between life span and reproduction. J. Gerontol. A Biol. Sci. Med. Sci. 66, 26–37 (2011).

    PubMed  Google Scholar 

  36. 36.

    van den Berg, N. et al. Longevity defined as top 10% survivors and beyond is transmitted as a quantitative genetic trait. Nat. Commun. 10, 35 (2019).

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Barzilai, N., Gabriely, I., Gabriely, M., Iankowitz, N. & Sorkin, J. D. Offspring of centenarians have a favorable lipid profile. J. Am. Geriatr. Soc. 49, 76–79 (2001).

    CAS  PubMed  Google Scholar 

  38. 38.

    Newman, A. B. et al. Health and function of participants in the Long Life Family Study: a comparison with other cohorts. Aging (Albany N.Y.) 3, 63–76 (2011).

    Google Scholar 

  39. 39.

    Deelen, J. et al. Employing biomarkers of healthy ageing for leveraging genetic studies into human longevity. Exp. Gerontol. 82, 166–174 (2016).

    CAS  PubMed  Google Scholar 

  40. 40.

    Ash, A. S. et al. Are members of long-lived families healthier than their equally long-lived peers? Evidence from the Long Life Family Study. J. Gerontol. A Biol. Sci. Med. Sci. 70, 971–976 (2015).

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Ruby, J. G. et al. Estimates of the heritability of human longevity are substantially inflated due to assortative mating. Genetics 210, 1109–1124 (2018).

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Jarry, V., Gagnon, A. & Bourbeau, R. Survival advantage of siblings and spouses of centenarians in 20th-century Quebec. Can. Stud. Popul. 39, 67–78 (2012).

    Google Scholar 

  43. 43.

    van den Berg, N. et al. Longevity Relatives Count score identifies heritable longevity carriers and suggests case improvement in genetic studies. Aging Cell 19, e13139 (2020).

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    McDaid, A. F. et al. Bayesian association scan reveals loci associated with human lifespan and linked biomarkers. Nat. Commun. 8, 15842 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Zenin, A. et al. Identification of 12 genetic loci associated with human healthspan. Commun. Biol. 2, 41 (2019).

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Fernandes, M. et al. Systematic analysis of the gerontome reveals links between aging and age-related diseases. Hum. Mol. Genet. 25, 4804–4818 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Shindyapina, A. V. et al. Germline burden of rare damaging variants negatively affects human healthspan and lifespan. eLife 9, e53449 (2020).

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Cash, T. P. et al. Exome sequencing of three cases of familial exceptional longevity. Aging Cell 13, 1087–1090 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Nygaard, H. B. et al. Whole exome sequencing of an exceptional longevity cohort. J. Gerontol. A Biol. Sci. Med. Sci. 74, 1386–1390 (2019).

    PubMed  Google Scholar 

  50. 50.

    Han, J. et al. Discovery of novel non-synonymous SNP variants in 988 candidate genes from 6 centenarians by target capture and next-generation sequencing. Mech. Ageing Dev. 134, 478–485 (2013).

    CAS  PubMed  Google Scholar 

  51. 51.

    Howden, L. M. & Meyer, J. A. Age and Sex Composition: 2010 (U.S. Census Bureau, 2011).

  52. 52.

    Andersen, S. L., Sebastiani, P., Dworkis, D. A., Feldman, L. & Perls, T. T. Health span approximates life span among many supercentenarians: compression of morbidity at the approximate limit of life span. J. Gerontol. A Biol. Sci. Med. Sci. 67, 395–405 (2012).

    PubMed  Google Scholar 

  53. 53.

    Ismail, K. et al. Compression of morbidity is observed across cohorts with exceptional longevity. J. Am. Geriatr. Soc. 64, 1583–1591 (2016).

    PubMed  PubMed Central  Google Scholar 

  54. 54.

    Sebastiani, P. et al. Families enriched for exceptional longevity also have increased health-span: findings from the Long Life Family Study. Front. Public Health 1, 38 (2013).

    PubMed  PubMed Central  Google Scholar 

  55. 55.

    Hazra, N. C., Rudisill, C. & Gulliford, M. C. Determinants of health care costs in the senior elderly: age, comorbidity, impairment, or proximity to death? Eur. J. Health Econ. 19, 831–842 (2018).

    PubMed  Google Scholar 

  56. 56.

    Abifadel, M. et al. Mutations in PCSK9 cause autosomal dominant hypercholesterolemia. Nat. Genet. 34, 154–156 (2003).

    CAS  PubMed  Google Scholar 

  57. 57.

    Zhao, Z. et al. Molecular characterization of loss-of-function mutations in PCSK9 and identification of a compound heterozygote. Am. J. Hum. Genet. 79, 514–523 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Hooper, A. J., Marais, A. D., Tanyanyiwa, D. M. & Burnett, J. R. The C679X mutation in PCSK9 is present and lowers blood cholesterol in a Southern African population. Atherosclerosis 193, 445–448 (2007).

    CAS  PubMed  Google Scholar 

  59. 59.

    Fitzgerald, K. et al. Effect of an RNA interference drug on the synthesis of proprotein convertase subtilisin/kexin type 9 (PCSK9) and the concentration of serum LDL cholesterol in healthy volunteers: a randomised, single-blind, placebo-controlled, phase 1 trial. Lancet 383, 60–68 (2014).

    CAS  PubMed  Google Scholar 

  60. 60.

    Ridker, P. M. et al. Cardiovascular efficacy and safety of bococizumab in high-risk patients. N. Engl. J. Med. 376, 1527–1539 (2017).

    CAS  PubMed  Google Scholar 

  61. 61.

    Tall, A. R. & Rader, D. J. Trials and tribulations of CETP inhibitors. Circ. Res. 122, 106–112 (2018).

    CAS  PubMed  Google Scholar 

  62. 62.

    Goodwin, S., McPherson, J. D. & McCombie, W. R. Coming of age: ten years of next-generation sequencing technologies. Nat. Rev. Genet. 17, 333–351 (2016).

    CAS  PubMed  Google Scholar 

  63. 63.

    Gorlov, I. P., Gorlova, O. Y., Sunyaev, S. R., Spitz, M. R. & Amos, C. I. Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms. Am. J. Hum. Genet. 82, 100–112 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Kosmicki, J. A., Churchhouse, C. L., Rivas, M. A. & Neale, B. M. Discovery of rare variants for complex phenotypes. Hum. Genet. 135, 625–634 (2016).

    PubMed  PubMed Central  Google Scholar 

  65. 65.

    Nicolae, D. L. Association tests for rare variants. Annu. Rev. Genomics Hum. Genet. 17, 117–130 (2016).

    CAS  PubMed  Google Scholar 

  66. 66.

    Finan, C. et al. The druggable genome and support for target identification and validation in drug development. Sci. Transl. Med. 9, eaag1166 (2017).

    PubMed  PubMed Central  Google Scholar 

  67. 67.

    Oprea, T. I. et al. Unexplored therapeutic opportunities in the human genome. Nat. Rev. Drug Discov. 17, 317–332 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Plenge, R. M., Scolnick, E. M. & Altshuler, D. Validating therapeutic targets through human genetics. Nat. Rev. Drug Discov. 12, 581–594 (2013).

    CAS  PubMed  Google Scholar 

  69. 69.

    Tazearslan, C., Huang, J., Barzilai, N. & Suh, Y. Impaired IGF1R signaling in cells expressing longevity-associated human IGF1R alleles. Aging Cell 10, 551–554 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Suh, Y. et al. Functionally significant insulin-like growth factor I receptor mutations in centenarians. Proc. Natl Acad. Sci. USA 105, 3438–3442 (2008).

    CAS  PubMed  Google Scholar 

  71. 71.

    Mao, K. et al. Late-life targeting of the IGF-1 receptor improves healthspan and lifespan in female mice. Nat. Commun. 9, 2394 (2018).

    PubMed  PubMed Central  Google Scholar 

  72. 72.

    Vidal, M., Cusick, M. E. & Barabasi, A. L. Interactome networks and human disease. Cell 144, 986–998 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Barabasi, A. L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Pawson, T. & Nash, P. Protein-protein interactions define specificity in signal transduction. Genes Dev. 14, 1027–1047 (2000).

    CAS  PubMed  Google Scholar 

  75. 75.

    Wang, X. et al. Three-dimensional reconstruction of protein networks provides insight into human genetic disease. Nat. Biotechnol. 30, 159–164 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Stenson, P. D. et al. Human Gene Mutation Database (HGMD): 2003 update. Hum. Mutat. 21, 577–581 (2003).

    CAS  PubMed  Google Scholar 

  77. 77.

    Khurana, E. et al. Integrative annotation of variants from 1092 humans: application to cancer genomics. Science 342, 1235587 (2013).

    PubMed  PubMed Central  Google Scholar 

  78. 78.

    Guo, Y. et al. Dissecting disease inheritance modes in a three-dimensional protein network challenges the “guilt-by-association” principle. Am. J. Hum. Genet. 93, 78–89 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. 79.

    Wei, X. et al. A massively parallel pipeline to clone DNA variants and examine molecular phenotypes of human disease mutations. PLoS Genet. 10, e1004819 (2014).

    PubMed  PubMed Central  Google Scholar 

  80. 80.

    Chen, S. et al. An interactome perturbation framework prioritizes damaging missense mutations for developmental disorders. Nat. Genet. 50, 1032–1040 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. 81.

    Braun, P. et al. An experimentally derived confidence score for binary protein-protein interactions. Nat. Meth. 6, 91–97 (2009).

    CAS  Google Scholar 

  82. 82.

    Yu, H. et al. High-quality binary protein interaction map of the yeast interactome network. Science 322, 104–110 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. 83.

    Yu, H. et al. Next-generation sequencing to generate interactome datasets. Nat. Methods 8, 478–480 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. 84.

    Sahni, N. et al. Widespread macromolecular interaction perturbations in human genetic disorders. Cell 161, 647–660 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. 85.

    Zhong, Q. et al. Edgetic perturbation models of human inherited disorders. Mol. Syst. Biol. 5, 321 (2009).

    PubMed  PubMed Central  Google Scholar 

  86. 86.

    Fabrizio, P., Pozza, F., Pletcher, S. D., Gendron, C. M. & Longo, V. D. Regulation of longevity and stress resistance by Sch9 in yeast. Science 292, 288–290 (2001).

    CAS  PubMed  Google Scholar 

  87. 87.

    Wang, H. D., Kazemi-Esfarjani, P. & Benzer, S. Multiple-stress analysis for isolation of Drosophila longevity genes. Proc. Natl Acad. Sci. USA 101, 12610–12615 (2004).

    CAS  PubMed  Google Scholar 

  88. 88.

    Muñoz, M. J. & Riddle, D. L. Positive selection of Caenorhabditis elegans mutants with increased stress resistance and longevity. Genetics 163, 171–180 (2003).

    PubMed  PubMed Central  Google Scholar 

  89. 89.

    de Magalhães, J. P. & Toussaint, O. GenAge: a genomic and proteomic network map of human ageing. FEBS Lett. 571, 243–247 (2004).

    PubMed  Google Scholar 

  90. 90.

    Soldner, F. & Jaenisch, R. Stem cells, genome editing, and the path to translational medicine. Cell 175, 615–632 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. 91.

    Lo Sardo, V. et al. Unveiling the role of the most impactful cardiovascular risk locus through haplotype editing. Cell 175, 1796–1810.e1720 (2018).

    CAS  PubMed  Google Scholar 

  92. 92.

    Aguiar-Oliveira, M. H. & Bartke, A. Growth hormone deficiency: health and longevity. Endocr. Rev. 40, 575–601 (2019).

    PubMed  Google Scholar 

  93. 93.

    Tilstra, J. S. et al. NF-κB inhibition delays DNA damage-induced senescence and aging in mice. J. Clin. Invest. 122, 2601–2612 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. 94.

    Niedernhofer, L. J. et al. A new progeroid syndrome reveals that genotoxic stress suppresses the somatotroph axis. Nature 444, 1038–1043 (2006).

    CAS  PubMed  Google Scholar 

  95. 95.

    Hambright, W. S., Niedernhofer, L. J., Huard, J. & Robbins, P. D. Murine models of accelerated aging and musculoskeletal disease. Bone 125, 122–127 (2019).

    CAS  PubMed  Google Scholar 

  96. 96.

    Willcox, B. J. et al. FOXO3A genotype is strongly associated with human longevity. Proc. Natl Acad. Sci. USA 105, 13987–13992 (2008).

    CAS  PubMed  Google Scholar 

  97. 97.

    Morris, B. J., Willcox, D. C., Donlon, T. A. & Willcox, B. J. FOXO3: a major gene for human longevity: a mini-review. Gerontology 61, 515–525 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. 98.

    Cautain, B. et al. Discovery of a novel, isothiazolonaphthoquinone-based small molecule activator of FOXO nuclear-cytoplasmic shuttling. PLoS ONE 11, e0167491 (2016).

    PubMed  PubMed Central  Google Scholar 

  99. 99.

    Belguise, K., Guo, S. & Sonenshein, G. E. Activation of FOXO3a by the green tea polyphenol epigallocatechin-3-gallate induces estrogen receptor alpha expression reversing invasive phenotype of breast cancer cells. Cancer Res. 67, 5763–5770 (2007).

    CAS  PubMed  Google Scholar 

  100. 100.

    Cho, S. et al. Syringaresinol protects against hypoxia/reoxygenation-induced cardiomyocytes injury and death by destabilization of HIF-1α in a FOXO3-dependent mechanism. Oncotarget 6, 43–55 (2015).

    PubMed  Google Scholar 

  101. 101.

    Bowman, L. et al. Effects of anacetrapib in patients with atherosclerotic vascular disease. N. Engl. J. Med. 377, 1217–1227 (2017).

    PubMed  Google Scholar 

  102. 102.

    Hall, S. S. Genetics: a gene of rare effect. Nature 496, 152–155 (2013).

    CAS  PubMed  Google Scholar 

  103. 103.

    Rajpathak, S. N. et al. Lifestyle factors of people with exceptional longevity. J. Am. Geriatr. Soc. 59, 1509–1512 (2011).

    PubMed  PubMed Central  Google Scholar 

  104. 104.

    Bitto, A., Wang, A. M., Bennett, C. F. & Kaeberlein, M. Biochemical genetic pathways that modulate aging in multiple species. Cold Spring Harb. Perspect. Med. 5, a025114 (2015).

    PubMed  PubMed Central  Google Scholar 

  105. 105.

    Taniguchi, C. M., Emanuelli, B. & Kahn, C. R. Critical nodes in signalling pathways: insights into insulin action. Nat. Rev. Mol. Cell Biol. 7, 85–96 (2006).

    CAS  PubMed  Google Scholar 

  106. 106.

    Kennedy, B. K. & Lamming, D. W. The mechanistic target of rapamycin: the grand conductor of metabolism and aging. Cell Metab. 23, 990–1003 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  107. 107.

    Kahn, A. J. FOXO3 and related transcription factors in development, aging, and exceptional longevity. J. Gerontol. A Biol. Sci. Med. Sci. 70, 421–425 (2015).

    CAS  PubMed  Google Scholar 

  108. 108.

    Roczniak-Ferguson, A. et al. The transcription factor TFEB links mTORC1 signaling to transcriptional control of lysosome homeostasis. Sci. Signal. 5, ra42 (2012).

    PubMed  PubMed Central  Google Scholar 

  109. 109.

    Mammucari, C. et al. FoxO3 controls autophagy in skeletal muscle in vivo. Cell Metab. 6, 458–471 (2007).

    CAS  PubMed  Google Scholar 

  110. 110.

    Kops, G. J. et al. Forkhead transcription factor FOXO3a protects quiescent cells from oxidative stress. Nature 419, 316–321 (2002).

    CAS  PubMed  Google Scholar 

  111. 111.

    Greer, E. L. et al. The energy sensor AMP-activated protein kinase directly regulates the mammalian FOXO3 transcription factor. J. Biol. Chem. 282, 30107–30119 (2007).

    CAS  PubMed  Google Scholar 

  112. 112.

    Cantó, C. et al. AMPK regulates energy expenditure by modulating NAD+ metabolism and SIRT1 activity. Nature 458, 1056–1060 (2009).

    PubMed  PubMed Central  Google Scholar 

  113. 113.

    Brunet, A. et al. Stress-dependent regulation of FOXO transcription factors by the SIRT1 deacetylase. Science 303, 2011–2015 (2004).

    CAS  PubMed  Google Scholar 

  114. 114.

    Yeung, F. et al. Modulation of NF-κB-dependent transcription and cell survival by the SIRT1 deacetylase. EMBO J. 23, 2369–2380 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. 115.

    Tasselli, L., Zheng, W. & Chua, K. F. SIRT6: novel mechanisms and links to aging and disease. Trends Endocrinol. Metab. 28, 168–185 (2017).

    CAS  PubMed  Google Scholar 

  116. 116.

    Roichman, A. et al. SIRT6 overexpression improves various aspects of mouse healthspan. J. Gerontol. A Biol. Sci. Med. Sci. 72, 603–615 (2017).

    CAS  PubMed  Google Scholar 

  117. 117.

    Tian, X. et al. SIRT6 is responsible for more efficient DNA double-strand break repair in long-lived species. Cell 177, 622–638.e622 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  118. 118.

    Di Francesco, A., Di Germanio, C., Bernier, M. & de Cabo, R. A time to fast. Science 362, 770–775 (2018).

    PubMed  Google Scholar 

  119. 119.

    Mannick, J. B. et al. mTOR inhibition improves immune function in the elderly. Sci. Transl. Med. 6, 268ra179 (2014).

    PubMed  Google Scholar 

  120. 120.

    Timmers, S. et al. Calorie restriction-like effects of 30 days of resveratrol supplementation on energy metabolism and metabolic profile in obese humans. Cell Metab. 14, 612–622 (2011).

    CAS  PubMed  Google Scholar 

  121. 121.

    Dai, H., Sinclair, D. A., Ellis, J. L. & Steegborn, C. Sirtuin activators and inhibitors: promises, achievements, and challenges. Pharmacol. Ther. 188, 140–154 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  122. 122.

    van Heemst, D. et al. Reduced insulin/IGF-1 signalling and human longevity. Aging Cell 4, 79–85 (2005).

    PubMed  Google Scholar 

  123. 123.

    Milman, S. et al. Low insulin-like growth factor-1 level predicts survival in humans with exceptional longevity. Aging Cell 13, 769–771 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  124. 124.

    Deelen, J. et al. Gene set analysis of GWAS data for human longevity highlights the relevance of the insulin/IGF-1 signaling and telomere maintenance pathways. Age (Dordr.) 35, 235–249 (2013).

    CAS  Google Scholar 

  125. 125.

    Pawlikowska, L. et al. Association of common genetic variation in the insulin/IGF1 signaling pathway with human longevity. Aging Cell 8, 460–472 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  126. 126.

    Kichaev, G. et al. Integrating functional data to prioritize causal variants in statistical fine-mapping studies. PLoS Genet. 10, e1004722 (2014).

    PubMed  PubMed Central  Google Scholar 

  127. 127.

    Hormozdiari, F. et al. Colocalization of GWAS and eQTL signals detects target genes. Am. J. Hum. Genet. 99, 1245–1260 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  128. 128.

    Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  129. 129.

    Holmans, P. et al. Gene ontology analysis of GWA study data sets provides insights into the biology of bipolar disorder. Am. J. Hum. Genet. 85, 13–24 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  130. 130.

    Jia, P., Zheng, S., Long, J., Zheng, W. & Zhao, Z. dmGWAS: dense module searching for genome-wide association studies in protein-protein interaction networks. Bioinformatics 27, 95–102 (2011).

    CAS  PubMed  Google Scholar 

  131. 131.

    de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLOS Comput. Biol. 11, e1004219 (2015).

    PubMed  PubMed Central  Google Scholar 

  132. 132.

    Taşan, M. et al. Selecting causal genes from genome-wide association studies via functionally coherent subnetworks. Nat. Methods 12, 154–159 (2015).

    PubMed  Google Scholar 

  133. 133.

    Lin, J. R. et al. PGA: post-GWAS analysis for disease gene identification. Bioinformatics 34, 1786–1788 (2018).

    CAS  PubMed  Google Scholar 

  134. 134.

    Lin, J. R. et al. Integrated Post-GWAS analysis sheds new light on the disease mechanisms of schizophrenia. Genetics 204, 1587–1600 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  135. 135.

    Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  136. 136.

    Sundaram, L. et al. Predicting the clinical impact of human mutation with deep neural networks. Nat. Genet. 50, 1161–1170 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  137. 137.

    Lin, J. R., Zhang, Q., Cai, Y., Morrow, B. E. & Zhang, Z. D. Integrated rare variant-based risk gene prioritization in disease case-control sequencing studies. PLoS Genet. 13, e1007142 (2017).

    PubMed  PubMed Central  Google Scholar 

  138. 138.

    Landrum, M. J. et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 42, D980–D985 (2014).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank M. Guo and R. Kohanski at NIA for their scientific input, which contributed to the concepts outlined in this Perspective. This work was supported by grant U19 AG056278 from the US National Institutes of Health.

Author information

Affiliations

Authors

Contributions

Z.D.Z., V.G., W.C.L., L.J.N., Y.S., P.D.R. and J.V. substantially contributed to the discussion and the writing of the content. Z.D.Z., S.M., J.-R.L., S.W., H.Y., N.B., V.G., W.C.L., L.J.N., Y.S., P.D.R. and J.V. researched content for the article, contributed to writing, and reviewed and edited the manuscript before submission.

Corresponding author

Correspondence to Zhengdong D. Zhang.

Ethics declarations

Competing interests

J.V. is a founder of Singulomics Corp. P.R. and L.N. are co-founders of NRTK Biosciences. All other authors declare no competing interests.

Additional information

Peer review information Primary Handling Editor: Pooja Jha.

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

Supplementary information

Supplementary Information

Supplementary Tables 1 and 2

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, Z.D., Milman, S., Lin, J. et al. Genetics of extreme human longevity to guide drug discovery for healthy ageing. Nat Metab 2, 663–672 (2020). https://doi.org/10.1038/s42255-020-0247-0

Download citation

Search

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing