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Genetic Signatures of Exceptional Longevity in Humans

Author

Listed:
  • Paola Sebastiani
  • Nadia Solovieff
  • Andrew T DeWan
  • Kyle M Walsh
  • Annibale Puca
  • Stephen W Hartley
  • Efthymia Melista
  • Stacy Andersen
  • Daniel A Dworkis
  • Jemma B Wilk
  • Richard H Myers
  • Martin H Steinberg
  • Monty Montano
  • Clinton T Baldwin
  • Josephine Hoh
  • Thomas T Perls

Abstract

Like most complex phenotypes, exceptional longevity is thought to reflect a combined influence of environmental (e.g., lifestyle choices, where we live) and genetic factors. To explore the genetic contribution, we undertook a genome-wide association study of exceptional longevity in 801 centenarians (median age at death 104 years) and 914 genetically matched healthy controls. Using these data, we built a genetic model that includes 281 single nucleotide polymorphisms (SNPs) and discriminated between cases and controls of the discovery set with 89% sensitivity and specificity, and with 58% specificity and 60% sensitivity in an independent cohort of 341 controls and 253 genetically matched nonagenarians and centenarians (median age 100 years). Consistent with the hypothesis that the genetic contribution is largest with the oldest ages, the sensitivity of the model increased in the independent cohort with older and older ages (71% to classify subjects with an age at death>102 and 85% to classify subjects with an age at death>105). For further validation, we applied the model to an additional, unmatched 60 centenarians (median age 107 years) resulting in 78% sensitivity, and 2863 unmatched controls with 61% specificity. The 281 SNPs include the SNP rs2075650 in TOMM40/APOE that reached irrefutable genome wide significance (posterior probability of association = 1) and replicated in the independent cohort. Removal of this SNP from the model reduced the accuracy by only 1%. Further in-silico analysis suggests that 90% of centenarians can be grouped into clusters characterized by different “genetic signatures” of varying predictive values for exceptional longevity. The correlation between 3 signatures and 3 different life spans was replicated in the combined replication sets. The different signatures may help dissect this complex phenotype into sub-phenotypes of exceptional longevity.

Suggested Citation

  • Paola Sebastiani & Nadia Solovieff & Andrew T DeWan & Kyle M Walsh & Annibale Puca & Stephen W Hartley & Efthymia Melista & Stacy Andersen & Daniel A Dworkis & Jemma B Wilk & Richard H Myers & Martin , 2012. "Genetic Signatures of Exceptional Longevity in Humans," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-22, January.
    • Handle: RePEc:plo:pone00:0029848
    DOI: 10.1371/journal.pone.0029848
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    1. Zhi Wei & Kai Wang & Hui-Qi Qu & Haitao Zhang & Jonathan Bradfield & Cecilia Kim & Edward Frackleton & Cuiping Hou & Joseph T Glessner & Rosetta Chiavacci & Charles Stanley & Dimitri Monos & Struan F , 2009. "From Disease Association to Risk Assessment: An Optimistic View from Genome-Wide Association Studies on Type 1 Diabetes," PLOS Genetics, Public Library of Science, vol. 5(10), pages 1-11, October.
    2. Sebastian Okser & Terho Lehtimäki & Laura L Elo & Nina Mononen & Nina Peltonen & Mika Kähönen & Markus Juonala & Yue-Mei Fan & Jussi A Hernesniemi & Tomi Laitinen & Leo-Pekka Lyytikäinen & Riikka Ront, 2010. "Genetic Variants and Their Interactions in the Prediction of Increased Pre-Clinical Carotid Atherosclerosis: The Cardiovascular Risk in Young Finns Study," PLOS Genetics, Public Library of Science, vol. 6(9), pages 1-13, September.
    3. B. Devlin & Kathryn Roeder, 1999. "Genomic Control for Association Studies," Biometrics, The International Biometric Society, vol. 55(4), pages 997-1004, December.
    4. Jan Vijg & Judith Campisi, 2008. "Puzzles, promises and a cure for ageing," Nature, Nature, vol. 454(7208), pages 1065-1071, August.
    5. Maria Eriksson & W. Ted Brown & Leslie B. Gordon & Michael W. Glynn & Joel Singer & Laura Scott & Michael R. Erdos & Christiane M. Robbins & Tracy Y. Moses & Peter Berglund & Amalia Dutra & Evgenia Pa, 2003. "Recurrent de novo point mutations in lamin A cause Hutchinson–Gilford progeria syndrome," Nature, Nature, vol. 423(6937), pages 293-298, May.
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    1. Bastian Greshake & Philipp E Bayer & Helge Rausch & Julia Reda, 2014. "openSNP–A Crowdsourced Web Resource for Personal Genomics," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-9, March.

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