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Aging: Searching for the genetic key to a long and healthy life

  1. Joris Deelen  Is a corresponding author
  1. Max Planck Institute for Biology of Ageing, Germany
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Cite this article as: eLife 2020;9:e57242 doi: 10.7554/eLife.57242


A study of over 40,000 individuals suggests that carrying a small number of ultra-rare genetic variants is associated with a longer lifespan.

Main text

For centuries scientists have been attempting to understand why some people live longer than others. Individuals who live to an exceptional old age – defined as belonging to the top 10% survivors of their birth cohort – are likely to pass on their longevity to future generations as an inherited genetic trait (van den Berg et al., 2019). However, recent studies suggest that genetics only accounts for a small fraction (~10%) of our lifespan (Kaplanis et al., 2018; Ruby et al., 2018).

One way to unravel the genetic component of longevity is to carry out genome-wide association studies (GWAS) which explore the genome for genetic variants that appear more or less frequently in individuals who live to an exceptional old age compared to individuals who live to an average age. However, the relatively small sample sizes of these studies has made it difficult to identify variants that are associated with longevity (Melzer et al., 2020).

The emergence of the UK Biobank – a cohort that contains a wide range of health and medical information (including genetic information) on about 500,000 individuals – has made it easier to investigate the relationship between genetics and longevity. Although it is not yet possible to study longevity directly with the data in the UK Biobank, several GWAS have used these data to study alternative lifespan-related traits, such as the parental lifespan and healthspan of individuals (defined as the number of years lived in the absence of major chronic diseases). These studies have been reasonably successful in identifying new genetic variants that influence human lifespan, but these variants can only explain ~5% of the heritability of the lifespan-related traits (Timmers et al., 2019; Zenin et al., 2019).

The GWAS have only focused on relatively common genetic variants (which have minor allele frequencies (MAFs) of ≥1%), and it is possible that rare variants might be able to explain what is sometimes called the ‘missing heritability’. Now, in eLife, Vadim Gladyshev (Harvard Medical School) and co-workers – including Anastasia Shindyapina (Harvard) and Aleksandr Zenin (Lomonosov Moscow State University) as joint first authors – report how they analyzed data from the UK Biobank and the UK Brain Bank Network (which stores and provides brain tissue for researchers) to investigate how rare genetic variants affect lifespan and healthspan (Shindyapina et al., 2020).

One type of rare genetic variant, called a protein-truncating variant, can dramatically impact gene expression by disrupting the open reading frame and shortening the genetic sequence coding for a protein. The team calculated how many of these rare protein-truncating variants, also known as PTVs, were present in the genome of each individual, and found ultra-rare PTVs (which have MAFs of <0.01%) to be negatively associated with lifespan and healthspan. This suggests that individuals with a small number of ultra-rare PTVs are more likely to have longer, healthier lives. Stratifying the data by sex showed that the association with healthspan was female-specific, while the association with lifespan was observed in both sexes.

Further analyses revealed that certain types of ultra-rare PTVs (such as stop-gain and frameshift mutations) were more likely to be associated with changes in lifespan. Shindyapina et al. also found that the impact of the variants depended on the damage they caused: for example, if the ultra-rare PTVs resulted in loss-of-function mutations, or if they affected genes that are expressed in many different cell types, the reduction in lifespan was greater. Ultra-rare PTVs were found to be spread across the genome, and only a small group of about 1500 seemingly essential genes did not have these variants. It is likely that damage to any of these 1500 or so genes leads to embryonic lethality or early mortality.

This work is the first to show that rare genetic variants play a role in lifespan-related traits, which is in line with previous studies showing rare PTVs to be linked to a variety of diseases (DeBoever et al., 2018). However, these variants only have a relatively small effect on human lifespan and cannot fully explain how longevity is genetically passed down to future generations. To explain the remaining ‘missing heritability’, future studies should try to focus on gene-by-gene and gene-by-environment interactions.

The UK Biobank is known to have a selection bias towards healthy individuals and the restricted age range of this cohort resulted in most of the individuals studied still being alive at the end of the follow-up period (Fry et al., 2017). Future studies should investigate whether cohorts with a broader age range and more reported deaths (including those of non-European ancestry) can replicate these findings. These studies could also determine whether individuals who live to an exceptional old age (as defined using the criteria outlined in van den Berg et al., 2019) have fewer or complete absence of ultra-rare PTVs.


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    Genomics of 1 million parent lifespans implicates novel pathways and common diseases and distinguishes survival chances
    1. PR Timmers
    2. N Mounier
    3. K Lall
    4. K Fischer
    5. Z Ning
    6. X Feng
    7. AD Bretherick
    8. DW Clark
    9. M Agbessi
    10. H Ahsan
    11. I Alves
    12. A Andiappan
    13. P Awadalla
    14. A Battle
    15. MJ Bonder
    16. D Boomsma
    17. M Christiansen
    18. A Claringbould
    19. P Deelen
    20. J van Dongen
    21. T Esko
    22. M Favé
    23. L Franke
    24. T Frayling
    25. SA Gharib
    26. G Gibson
    27. G Hemani
    28. R Jansen
    29. A Kalnapenkis
    30. S Kasela
    31. J Kettunen
    32. Y Kim
    33. H Kirsten
    34. P Kovacs
    35. K Krohn
    36. J Kronberg-Guzman
    37. V Kukushkina
    38. Z Kutalik
    39. M Kähönen
    40. B Lee
    41. T Lehtimäki
    42. M Loeffler
    43. U Marigorta
    44. A Metspalu
    45. J van Meurs
    46. L Milani
    47. M Müller-Nurasyid
    48. M Nauck
    49. M Nivard
    50. B Penninx
    51. M Perola
    52. N Pervjakova
    53. B Pierce
    54. J Powell
    55. H Prokisch
    56. BM Psaty
    57. O Raitakari
    58. S Ring
    59. S Ripatti
    60. O Rotzschke
    61. S Ruëger
    62. A Saha
    63. M Scholz
    64. K Schramm
    65. I Seppälä
    66. M Stumvoll
    67. P Sullivan
    68. A Teumer
    69. J Thiery
    70. L Tong
    71. A Tönjes
    72. J Verlouw
    73. PM Visscher
    74. U Võsa
    75. U Völker
    76. H Yaghootkar
    77. J Yang
    78. B Zeng
    79. F Zhang
    80. M Agbessi
    81. H Ahsan
    82. I Alves
    83. A Andiappan
    84. P Awadalla
    85. A Battle
    86. MJ Bonder
    87. D Boomsma
    88. M Christiansen
    89. A Claringbould
    90. P Deelen
    91. J van Dongen
    92. T Esko
    93. M Favé
    94. L Franke
    95. T Frayling
    96. SA Gharib
    97. G Gibson
    98. G Hemani
    99. R Jansen
    100. A Kalnapenkis
    101. S Kasela
    102. J Kettunen
    103. Y Kim
    104. H Kirsten
    105. P Kovacs
    106. K Krohn
    107. J Kronberg-Guzman
    108. V Kukushkina
    109. Z Kutalik
    110. M Kähönen
    111. B Lee
    112. T Lehtimäki
    113. M Loeffler
    114. U Marigorta
    115. A Metspalu
    116. J van Meurs
    117. L Milani
    118. M Müller-Nurasyid
    119. M Nauck
    120. M Nivard
    121. B Penninx
    122. M Perola
    123. N Pervjakova
    124. B Pierce
    125. J Powell
    126. H Prokisch
    127. BM Psaty
    128. O Raitakari
    129. S Ring
    130. S Ripatti
    131. O Rotzschke
    132. S Ruëger
    133. A Saha
    134. M Scholz
    135. K Schramm
    136. I Seppälä
    137. M Stumvoll
    138. P Sullivan
    139. A Teumer
    140. J Thiery
    141. L Tong
    142. A Tönjes
    143. J Verlouw
    144. PM Visscher
    145. U Võsa
    146. U Völker
    147. H Yaghootkar
    148. J Yang
    149. B Zeng
    150. F Zhang
    151. X Shen
    152. T Esko
    153. Z Kutalik
    154. JF Wilson
    155. PK Joshi
    156. eQTLGen Consortium
    eLife 8:e39856.
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Article and author information

Author details

  1. Joris Deelen

    Joris Deelen is in the Max Planck Institute for Biology of Ageing, Cologne, Germany

    For correspondence
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4483-3701

Publication history

  1. Version of Record published: April 24, 2020 (version 1)


© 2020, Deelen

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.


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