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.
- 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
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Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general populationAmerican Journal of Epidemiology 186:1026–1034.https://doi.org/10.1093/aje/kwx246
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The genetics of human ageingNature Reviews Genetics 21:88–101.https://doi.org/10.1038/s41576-019-0183-6
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Identification of 12 genetic loci associated with human healthspanCommunications Biology 2:41.https://doi.org/10.1038/s42003-019-0290-0
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© 2020, Deelen
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Aging: Searching for the genetic key to a long and healthy life
eLife 9:e57242.
https://doi.org/10.7554/eLife.57242
Further reading
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Genetic effects on complex traits may depend on context, such as age, sex, environmental exposures, or social settings. However, it remains often unclear if the extent of context dependency, or gene-by-environment interaction (GxE), merits more involved models than the additive model typically used to analyze data from genome-wide association studies (GWAS). Here, we suggest considering the utility of GxE models in GWAS as a trade-off between bias and variance parameters. In particular, we derive a decision rule for choosing between competing models for the estimation of allelic effects. The rule weighs the increased estimation noise when context is considered against the potential bias when context dependency is ignored. In the empirical example of GxSex in human physiology, the increased noise of context-specific estimation often outweighs the bias reduction, rendering GxE models less useful when variants are considered independently. However, for complex traits, we argue that the joint consideration of context dependency across many variants mitigates both noise and bias. As a result, polygenic GxE models can improve both estimation and trait prediction. Finally, we exemplify (using GxDiet effects on longevity in fruit flies) how analyses based on independently ascertained ‘top hits’ alone can be misleading, and that considering polygenic patterns of GxE can improve interpretation.
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Deep Mutational Scanning (DMS) is an emerging method to systematically test the functional consequences of thousands of sequence changes to a protein target in a single experiment. Because of its utility in interpreting both human variant effects and protein structure-function relationships, it holds substantial promise to improve drug discovery and clinical development. However, applications in this domain require improved experimental and analytical methods. To address this need, we report novel DMS methods to precisely and quantitatively interrogate disease-relevant mechanisms, protein-ligand interactions, and assess predicted response to drug treatment. Using these methods, we performed a DMS of the melanocortin-4 receptor (MC4R), a G-protein-coupled receptor (GPCR) implicated in obesity and an active target of drug development efforts. We assessed the effects of >6600 single amino acid substitutions on MC4R’s function across 18 distinct experimental conditions, resulting in >20 million unique measurements. From this, we identified variants that have unique effects on MC4R-mediated Gαs- and Gαq-signaling pathways, which could be used to design drugs that selectively bias MC4R’s activity. We also identified pathogenic variants that are likely amenable to a corrector therapy. Finally, we functionally characterized structural relationships that distinguish the binding of peptide versus small molecule ligands, which could guide compound optimization. Collectively, these results demonstrate that DMS is a powerful method to empower drug discovery and development.
