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

A study of over 40,000 individuals suggests that carrying a small number of ultra-rare genetic variants is associated with a longer lifespan.
  1. Joris Deelen  Is a corresponding author
  1. Max Planck Institute for Biology of Ageing, Germany

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.

References

    1. Timmers PR
    2. Mounier N
    3. Lall K
    4. Fischer K
    5. Ning Z
    6. Feng X
    7. Bretherick AD
    8. Clark DW
    9. Agbessi M
    10. Ahsan H
    11. Alves I
    12. Andiappan A
    13. Awadalla P
    14. Battle A
    15. Bonder MJ
    16. Boomsma D
    17. Christiansen M
    18. Claringbould A
    19. Deelen P
    20. van Dongen J
    21. Esko T
    22. Favé M
    23. Franke L
    24. Frayling T
    25. Gharib SA
    26. Gibson G
    27. Hemani G
    28. Jansen R
    29. Kalnapenkis A
    30. Kasela S
    31. Kettunen J
    32. Kim Y
    33. Kirsten H
    34. Kovacs P
    35. Krohn K
    36. Kronberg-Guzman J
    37. Kukushkina V
    38. Kutalik Z
    39. Kähönen M
    40. Lee B
    41. Lehtimäki T
    42. Loeffler M
    43. Marigorta U
    44. Metspalu A
    45. van Meurs J
    46. Milani L
    47. Müller-Nurasyid M
    48. Nauck M
    49. Nivard M
    50. Penninx B
    51. Perola M
    52. Pervjakova N
    53. Pierce B
    54. Powell J
    55. Prokisch H
    56. Psaty BM
    57. Raitakari O
    58. Ring S
    59. Ripatti S
    60. Rotzschke O
    61. Ruëger S
    62. Saha A
    63. Scholz M
    64. Schramm K
    65. Seppälä I
    66. Stumvoll M
    67. Sullivan P
    68. Teumer A
    69. Thiery J
    70. Tong L
    71. Tönjes A
    72. Verlouw J
    73. Visscher PM
    74. Võsa U
    75. Völker U
    76. Yaghootkar H
    77. Yang J
    78. Zeng B
    79. Zhang F
    80. Agbessi M
    81. Ahsan H
    82. Alves I
    83. Andiappan A
    84. Awadalla P
    85. Battle A
    86. Bonder MJ
    87. Boomsma D
    88. Christiansen M
    89. Claringbould A
    90. Deelen P
    91. van Dongen J
    92. Esko T
    93. Favé M
    94. Franke L
    95. Frayling T
    96. Gharib SA
    97. Gibson G
    98. Hemani G
    99. Jansen R
    100. Kalnapenkis A
    101. Kasela S
    102. Kettunen J
    103. Kim Y
    104. Kirsten H
    105. Kovacs P
    106. Krohn K
    107. Kronberg-Guzman J
    108. Kukushkina V
    109. Kutalik Z
    110. Kähönen M
    111. Lee B
    112. Lehtimäki T
    113. Loeffler M
    114. Marigorta U
    115. Metspalu A
    116. van Meurs J
    117. Milani L
    118. Müller-Nurasyid M
    119. Nauck M
    120. Nivard M
    121. Penninx B
    122. Perola M
    123. Pervjakova N
    124. Pierce B
    125. Powell J
    126. Prokisch H
    127. Psaty BM
    128. Raitakari O
    129. Ring S
    130. Ripatti S
    131. Rotzschke O
    132. Ruëger S
    133. Saha A
    134. Scholz M
    135. Schramm K
    136. Seppälä I
    137. Stumvoll M
    138. Sullivan P
    139. Teumer A
    140. Thiery J
    141. Tong L
    142. Tönjes A
    143. Verlouw J
    144. Visscher PM
    145. Võsa U
    146. Völker U
    147. Yaghootkar H
    148. Yang J
    149. Zeng B
    150. Zhang F
    151. Shen X
    152. Esko T
    153. Kutalik Z
    154. Wilson JF
    155. Joshi PK
    156. eQTLGen Consortium
    (2019) Genomics of 1 million parent lifespans implicates novel pathways and common diseases and distinguishes survival chances
    eLife 8:e39856.
    https://doi.org/10.7554/eLife.39856

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
    Joris.Deelen@age.mpg.de
    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)

Copyright

© 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.

Metrics

  • 2,850
    views
  • 234
    downloads
  • 3
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Joris Deelen
(2020)
Aging: Searching for the genetic key to a long and healthy life
eLife 9:e57242.
https://doi.org/10.7554/eLife.57242
  1. Further reading

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Weichen Song, Yongyong Shi, Guan ning Lin
    Tools and Resources

    We propose a new framework for human genetic association studies: at each locus, a deep learning model (in this study, Sei) is used to calculate the functional genomic activity score for two haplotypes per individual. This score, defined as the Haplotype Function Score (HFS), replaces the original genotype in association studies. Applying the HFS framework to 14 complex traits in the UK Biobank, we identified 3619 independent HFS–trait associations with a significance of p < 5 × 10−8. Fine-mapping revealed 2699 causal associations, corresponding to a median increase of 63 causal findings per trait compared with single-nucleotide polymorphism (SNP)-based analysis. HFS-based enrichment analysis uncovered 727 pathway–trait associations and 153 tissue–trait associations with strong biological interpretability, including ‘circadian pathway-chronotype’ and ‘arachidonic acid-intelligence’. Lastly, we applied least absolute shrinkage and selection operator (LASSO) regression to integrate HFS prediction score with SNP-based polygenic risk scores, which showed an improvement of 16.1–39.8% in cross-ancestry polygenic prediction. We concluded that HFS is a promising strategy for understanding the genetic basis of human complex traits.

    1. Genetics and Genomics
    2. Immunology and Inflammation
    Jean-David Larouche, Céline M Laumont ... Claude Perreault
    Research Article

    Transposable elements (TEs) are repetitive sequences representing ~45% of the human and mouse genomes and are highly expressed by medullary thymic epithelial cells (mTECs). In this study, we investigated the role of TEs on T-cell development in the thymus. We performed multiomic analyses of TEs in human and mouse thymic cells to elucidate their role in T-cell development. We report that TE expression in the human thymus is high and shows extensive age- and cell lineage-related variations. TE expression correlates with multiple transcription factors in all cell types of the human thymus. Two cell types express particularly broad TE repertoires: mTECs and plasmacytoid dendritic cells (pDCs). In mTECs, transcriptomic data suggest that TEs interact with transcription factors essential for mTEC development and function (e.g., PAX1 and REL), and immunopeptidomic data showed that TEs generate MHC-I-associated peptides implicated in thymocyte education. Notably, AIRE, FEZF2, and CHD4 regulate small yet non-redundant sets of TEs in murine mTECs. Human thymic pDCs homogenously express large numbers of TEs that likely form dsRNA, which can activate innate immune receptors, potentially explaining why thymic pDCs constitutively secrete IFN ɑ/β. This study highlights the diversity of interactions between TEs and the adaptive immune system. TEs are genetic parasites, and the two thymic cell types most affected by TEs (mTEcs and pDCs) are essential to establishing central T-cell tolerance. Therefore, we propose that orchestrating TE expression in thymic cells is critical to prevent autoimmunity in vertebrates.