1. Genetics and Genomics

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.
https://doi.org/10.7554/eLife.57242
Share this article
Cite this article
  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
  2. Download BibTeX
  3. Download .RIS
  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

  • 2,875
    views
  • 237
    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

Categories and tags

Research organism

    1. Related to

    Germline burden of rare damaging variants negatively affects human healthspan and lifespan

    Anastasia V Shindyapina, Aleksandr A Zenin ... Vadim N Gladyshev
  2. Further reading

Further reading

    1. Genetics and Genomics

    Germline burden of rare damaging variants negatively affects human healthspan and lifespan

    Anastasia V Shindyapina, Aleksandr A Zenin ... Vadim N Gladyshev
    Research Article Updated

    Heritability of human lifespan is 23–33% as evident from twin studies. Genome-wide association studies explored this question by linking particular alleles to lifespan traits. However, genetic variants identified so far can explain only a small fraction of lifespan heritability in humans. Here, we report that the burden of rarest protein-truncating variants (PTVs) in two large cohorts is negatively associated with human healthspan and lifespan, accounting for 0.4 and 1.3 years of their variability, respectively. In addition, longer-living individuals possess both fewer rarest PTVs and less damaging PTVs. We further estimated that somatic accumulation of PTVs accounts for only a small fraction of mortality and morbidity acceleration and hence is unlikely to be causal in aging. We conclude that rare damaging mutations, both inherited and accumulated throughout life, contribute to the aging process, and that burden of ultra-rare variants in combination with common alleles better explain apparent heritability of human lifespan.

    1. Developmental Biology
    2. Genetics and Genomics

    Fetal growth delay caused by loss of non-canonical imprinting is resolved late in pregnancy and culminates in offspring overgrowth

    Ruby Oberin, Sigrid Petautschnig ... Patrick S Western
    Research Article Updated

    Germline epigenetic programming, including genomic imprinting, substantially influences offspring development. Polycomb Repressive Complex 2 (PRC2) plays an important role in Histone 3 Lysine 27 trimethylation (H3K27me3)-dependent imprinting, loss of which leads to growth and developmental changes in mouse offspring. In this study, we show that offspring from mouse oocytes lacking the PRC2 protein Embryonic Ectoderm Development (EED) were initially developmentally delayed, characterised by low blastocyst cell counts and substantial growth delay in mid-gestation embryos. This initial developmental delay was resolved as offspring underwent accelerated fetal development and growth in late gestation resulting in offspring that were similar stage and weight to controls at birth. The accelerated development and growth in offspring from Eed-null oocytes was associated with remodelling of the placenta, which involved an increase in fetal and maternal tissue size, conspicuous expansion of the glycogen-enriched cell population, and delayed parturition. Despite placental remodelling and accelerated offspring fetal growth and development, placental efficiency, and fetal blood glucose levels were low, and the fetal blood metabolome was unchanged. Moreover, while expression of the H3K27me3-imprinted gene and amino acid transporter Slc38a4 was increased, fetal blood levels of individual amino acids were similar to controls, indicating that placental amino acid transport was not enhanced. Genome-wide analyses identified extensive transcriptional dysregulation and DNA methylation changes in affected placentas, including a range of imprinted and non-imprinted genes. Together, while deletion of Eed in growing oocytes resulted in fetal growth and developmental delay and placental hyperplasia, our data indicate a remarkable capacity for offspring fetal growth to be normalised despite inefficient placental function and the loss of H3K27me3-dependent genomic imprinting.

    1. Computational and Systems Biology
    2. Genetics and Genomics

    Spotless, a reproducible pipeline for benchmarking cell type deconvolution in spatial transcriptomics

    Chananchida Sang-aram, Robin Browaeys ... Yvan Saeys
    Research Article

    Spatial transcriptomics (ST) technologies allow the profiling of the transcriptome of cells while keeping their spatial context. Since most commercial untargeted ST technologies do not yet operate at single-cell resolution, computational methods such as deconvolution are often used to infer the cell type composition of each sequenced spot. We benchmarked 11 deconvolution methods using 63 silver standards, 3 gold standards, and 2 case studies on liver and melanoma tissues. We developed a simulation engine called synthspot to generate silver standards from single-cell RNA-sequencing data, while gold standards are generated by pooling single cells from targeted ST data. We evaluated methods based on their performance, stability across different reference datasets, and scalability. We found that cell2location and RCTD are the top-performing methods, but surprisingly, a simple regression model outperforms almost half of the dedicated spatial deconvolution methods. Furthermore, we observe that the performance of all methods significantly decreased in datasets with highly abundant or rare cell types. Our results are reproducible in a Nextflow pipeline, which also allows users to generate synthetic data, run deconvolution methods and optionally benchmark them on their dataset (https://github.com/saeyslab/spotless-benchmark).