The influence of genetic variants on natural variations in human longevity is a very complex matter. The evidence to date supports a model in which thousands of genes have individually tiny, conditional effects. Near all associations identified in any given study population have failed to appear in any of the other study populations, and effect sizes for the very few longevity-associated genes that do appear in multiple studies are not large in the grand scheme of things. These variants provide a small increase in the odds of living to be very old, but the individuals bearing them are still diminished and damaged by aging. The genetics that determine how cellular metabolism gives rise to variations in aging are of great scientific interest, but there is nothing here that can act as the foundation for therapies that will help people to live significantly longer.
The extent of the role of genetic variation in human lifespan has been widely debated, with estimates of broad sense heritability ranging from around 25% based on twin studies to around 16.1% based on large-scale population data. One very recent study suggests it is much lower still (less than 7%), pointing to assortative mating as the source of resemblance amongst kin. Despite this modest heritability, extensive research has gone into genome-wide association studies (GWAS) finding genetic variants influencing human survival. Only two robustly replicated, genome-wide significant associations (near APOE and FOXO3) have been made to date, however.
An alternative approach is to study lifespan as a quantitative trait in the general population and use survival models to allow long-lived survivors to inform analysis. However, given the incidence of mortality in middle-aged subjects is low, studies have shifted to the use of parental lifespans with subject genotypes, circumventing the long wait associated with studying age at death in a prospective study. In addition, the recent increase in genotyped population cohorts around the world, and in particular the creation of UK Biobank, has raised GWAS sample sizes to hundreds of thousands of individuals, providing the statistical power necessary to detect genetic effects on mortality. A third approach is to gather previously published GWAS on risk factors thought to possibly affect lifespan, such as smoking behaviour and cardiovascular disease (CVD), and estimate their actual independent, causal effects on mortality.
Here, we blend these three approaches to studying lifespan and perform the largest GWAS on human lifespan to date. First, we leverage data from UK Biobank and 26 independent European-heritage population cohorts to carry out a GWAS of parental survival. We then supplement this with data from 58 GWAS on mortality risk factors. Finally, we use publicly available case-control longevity GWAS statistics to compare the genetics of lifespan and longevity and provide collective replication of our lifespan GWAS results.
We identified 11 novel genome-wide significant associations with lifespan and replicated six previously discovered loci. We also replicated long-standing longevity SNPs near APOE, FOXO3, and 5q33.3/EBF1 - albeit with smaller effect sizes in the latter two cases - but found evidence of no association (at effect sizes originally published) with lifespan for more recently published longevity SNPs near IL6, ANKRD20A9P, USP42, and TMTC2. Despite studying over 1 million lives, our standard GWAS only identified 12 variants influencing lifespan at genome-wide significance. This contrasts with height (another highly polygenic trait) where a study of around 250,000 individuals found 423 loci.
This difference can partly be explained by the much lower heritability of lifespan (0.12 versus 0.8 for height), consistent with evolution having a stronger influence on the total heritability of traits more closely related to fitness and limiting effect sizes. In addition, the use of indirect genotypes reduces the effective sample size to 1/4 for the parent-offspring design. When considering these limitations, we calculate our study was equal in power to a height study of only around 23,224 individuals, were lifespan to have a similar genetic architecture to height. Under this assumption, we would require a sample size of around 10 million parents (or equivalently 445,000 nonagenarian cases, with even more controls) to detect a similar number of loci.
Individual genetic variants that increase dementia, cardiovascular disease, and lung cancer - but not other cancers - explain the most variance in lifespan. We hoped to narrow down the search and discover specific genes that directly influence how quickly people age, beyond diseases. If such genes exist, their effects were too small to be detected in this study. The next step will be to expand the study to include more participants, which will hopefully pinpoint further genomic regions and help disentangle the biology of ageing and disease.