Genomics of 1 million parent lifespans implicates novel pathways and common diseases and distinguishes survival chances
Human lifespan is a highly complex trait , the product of myriad factors involving health , lifestyle , genetics , environment , and chance . The extent of the role of genetic variation in human lifespan has been widely debated ( van den Berg et al . , 2017 ) , with estimates of broad sense heritability ranging from around 25% based on twin studies ( Ljungquist et al . , 1998; Herskind et al . , 1996; McGue et al . , 1993 ) ( perhaps over-estimated [Young et al . , 2018] ) to around 16 . 1% , ( narrow sense 12 . 2% ) based on large-scale population data ( Kaplanis et al . , 2018 ) . One very recent study suggests it is much lower still ( <7% ) ( Ruby et al . , 2018 ) , 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 , using a variety of trait definitions and study designs ( Deelen et al . , 2011; Sebastiani et al . , 2012; Beekman et al . , 2013; Broer et al . , 2015; Joshi et al . , 2016; Pilling et al . , 2016; Zeng et al . , 2016; Pilling et al . , 2017 ) . GWAS have primarily focused on extreme cases of long-livedness ( longevity ) – individuals surviving past a certain age threshold – and scanning for differences in genetic variation from controls . While this case-control design has the advantage of focusing on highly statistically-informative individuals , who also often exhibit extreme healthspan and have potentially unique genetic attributes ( Sebastiani et al . , 2013; Sebastiani et al . , 2016 ) , the exceptional nature of the phenotype precludes collection of large samples , and differences in definitions of longevity complicate meta-analysis . As a result , only two robustly replicated , genome-wide significant associations ( near APOE and FOXO3 ) have been made to date ( Broer et al . , 2015; Deelen et al . , 2014 ) . An alternative approach is to study lifespan as a quantitative trait in the general population and use survival models ( such as Cox proportional hazards [Cox , 1972] ) 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 ( an instance of Wacholder’s kin-cohort method [Wacholder et al . , 1998] ) , circumventing the long wait associated with studying age at death in a prospective study ( Joshi et al . , 2016; Pilling et al . , 2016 ) . In addition , the recent increase in genotyped population cohorts around the world , and in particular the creation of UK Biobank ( Bycroft et al . , 2017 ) , 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 using Mendelian Randomisation . These causal estimates can then be used in a Bayesian framework to inform previously observed SNP associations with lifespan ( McDaid et al . , 2017 ) . 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 ( Joshi et al . , 2017 ) to carry out a GWAS of parental survival , quantified using Cox models . We then supplement this with data from 58 GWAS on mortality risk factors to conduct a Bayesian prior-informed GWAS ( iGWAS ) . 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 also examine the diseases associated with lifespan-altering variants and the effect of known disease variants on lifespan , to provide insight into the interplay between lifespan and disease . Finally , we use our GWAS results to implicate specific genes , biological pathways , and cell types , and use our findings to create and test whole-genome polygenic scores for survival .
We carried out GWAS of survival in a sample of 1 , 012 , 240 parents ( 60% deceased ) of European ancestry from UK Biobank and a previously published meta-analysis of 26 additional population cohorts ( LifeGen [Joshi et al . , 2017]; Table 1—source data 1 ) . We performed a sex-stratified analysis and then combined the allelic effects in fathers and mothers into a single parental survival association in two ways . First , we assumed genetic variants with common effect sizes ( CES ) for both parents , maximising power if the effect is indeed the same . Second , we allowed for sex-specific effect sizes ( SSE ) , maximising power to detect sexually dimorphic variants , including those only affecting one sex . The latter encompasses a conventional sex-stratified analysis , but uses only one statistical test for the much more general alternative hypothesis that there is an effect in at least one sex . We find 12 genomic regions with SNPs passing genome-wide significance for one or both analyses ( p < 2 . 5 × 10–8 , accounting for the two tests CES/SSE ) ( Figure 1; Table 1 ) . Among these are five loci discovered here for the first time , at or near MAGI3 , KCNK3 , HTT , HP , and LDLR . Carrying one copy of a life-extending allele is associated with an increase in lifespan between 0 . 23 and 1 . 07 years ( around 3 to 13 months ) . Despite our sample size exceeding 1 million phenotypes , a variant had to have a minor allele frequency exceeding 5% and an effect size of 0 . 35 years of life or more per allele for our study to detect it with 80% power . We also attempted to validate novel lifespan SNPs discovered by Pilling et al . ( 2017 ) in UK Biobank at an individual level by using the LifeGen meta-analysis as independent replication sample . Testing 20 candidate SNPs for which we had data available , we find directionally consistent , nominally significant associations for six loci ( p < 0 . 05 , one-sided test ) , of which three have sex-specific effects . We also provide evidence against three putative loci but lack statistical power to assess the remaining 11 ( Figure 2 , Figure 2—source data 1 ) . We then used our full sample to test six candidate SNPs previously associated with longevity ( Zeng et al . , 2016; Deelen et al . , 2014; Flachsbart et al . , 2009; Sebastiani et al . , 2017 ) for association with lifespan , and find directionally consistent evidence for SNPs near FOXO3 and EBF1 . The remaining SNPs did not associate with lifespan despite apparently adequate power to detect any effect similar to that originally reported ( Figure 2 , Figure 2—source data 1 ) . Finally , we tested a deletion , d3-GHR , reported to affect male lifespan by 10 years when homozygous ( Ben-Avraham et al . , 2017 ) by converting its effect size to one we expect to observe when fitting an additive model . We used a SNP tagging the deletion and estimated the expected effect size in a linear regression for the ( postulated ) recessive effect across the three genotypes , given their frequency ( see Materials and methods ) . While this additive model reduces power relative to the correct model , our large sample size is more than able to offset the loss of power , and we find evidence d3-GHR does not associate with lifespan with any ( recessive or additive ) effect similar to that originally reported ( Figure 2 , Figure 2—source data 1 ) . We integrated 58 publicly available GWAS on mortality risk factors with our CES lifespan GWAS , creating Bayesian priors for each SNP effect based on causal effect estimates of 16 independent risk factors on lifespan . These included body mass index , blood biochemistry , CVD , type 2 diabetes , schizophrenia , multiple sclerosis , education levels , and smoking traits . The integrated analysis reveals an additional seven genome-wide significant associations with lifespan ( Bayes Factor permutation p < 2 . 5 × 10–8 ) , of which SNPs near TMEM18 , GBX2/ASB18 , IGF2R , POM12C , ZC3HC1 , and ABO are reported at genome-wide significance for the first time ( Figure 3; Table 2 ) . A total of 82 independent SNPs associate with lifespan when allowing for a 1% false discovery rate ( FDR ) ( Table 2—source data 2 ) . As has become increasingly common ( Pilling et al . , 2017 ) , we attempted to replicate our genome-wide significant findings collectively , rather than individually . This is usually done by constructing polygenic risk scores from genotypic information in an independent cohort and testing for association with the trait of interest subject-by-subject . We used publicly available summary statistics on extreme longevity as an independent replication dataset ( Broer et al . , 2015; Deelen et al . , 2014 ) , but lacking individual data from such studies , we calculated the collective effect of lifespan SNPs on longevity using the same method as inverse-variance meta-analysis two-sample Mendelian randomisation ( MR ) using summary statistics ( Hemani et al . , 2018 ) , which gives equivalent results . Prior to doing this , all effects observed in the external longevity studies were converted to hazard ratios using the APOE variant effect size as an empirical conversion factor , to allow the longevity studies to be meta-analysed despite their different study designs ( and to be adjusted for sample overlap; see Materials and methods ) . Although the focus is on collective replication , our method has the advantage of transparency at an individual variant level , which is of particular importance for researchers seeking to follow-up individual loci . Remarkably , all lead lifespan variants show directional consistency with the independent longevity sample , and 4 SNPs or close proxies ( r2 > 0 . 8 ) reach nominal replication ( p < 0 . 05 , one-sided test ) ( Figure 4—source data 1 ) . Of these , SNPs near ABO , ZC3HC1 , and IGF2R are replicated for the first time , and thus appear to affect overall survival and survival to extreme age . The overall ratio of replication effect sizes to discovery effect sizes – excluding APOE – is 0 . 42 ( 95% CI 0 . 23–0 . 61; p = 1 . 35 × 10−5 ) . The fact this ratio is significantly greater than zero indicates most lifespan SNPs are indeed longevity SNPs . However , the fact most SNPs have a ratio smaller than one indicates they may affect early mortality more than survival to extreme age , relative to APOE ( which itself has a greater effect on late-life mortality than early mortality ) ( Figure 4 ) . We stratified our UK Biobank sample ( for which we had individual level data ) by sex and age bands to identify sex- and age-specific effects for survival SNPs discovered and/or replicated in this study . Although power was limited , as we sought contrasts in small effect sizes , we find 5 SNPs with differential effects on lifespan when stratified ( FDR 5% across the 24 variants considered ) . The effect of the APOE variant increases with age: the ε4 log hazard ratio on individuals older than 70 years is around 3 times greater than those between ages 40–70 . In contrast , the effect of lead variants near CHRNA3/5 , CDKN2B-AS1 , and ABO tends to decline after age 60 , at least when expressed as hazard ratios ( Figure 5A ) . Independent of age , lead variants near APOE and PSORS1C3 also show an effect ( lnHR ) of 0 . 036; 0 . 038 greater in women ( 95% CI 0 . 013–0 . 059; 0 . 019–0 . 056 , respectively ) , compared to men ( Figure 5B ) . Notably , the SNP near ZW10 , which was identified by Pilling et al . ( 2017 ) in fathers , and which replicated in LifeGen fathers , may affect men and women equally ( 95% CI years gained per effect allele , men 0 . 17–0 . 42 , women 0 . 04–0 . 31 ) , as measured in our meta-analysis of UK Biobank and LifeGen . We used SMR-HEIDI to look for causal effects of gene expression or changes in methylation on lifespan within the 24 loci discovered or replicated in our study . Using blood eQTL summary statistics from two studies ( Westra et al . , 2013; Lloyd-Jones et al . , 2017 ) , we suggest causal roles for expression of PSRC1 , SESN1 , SH2B3 , PSMA4 , FURIN , FES , and KANK2 at 5% FDR ( Supplementary file 1 ) . GTEx tissue-wide expression data suggests further roles for 16 genes across 24 tissues , especially FES ( nine tissues ) , PMS2P3 ( six tissues ) and PSORS1C1 ( four tissues ) . Methylation data reveals roles for 44 CpG sites near nine loci , especially near the PSORS1C3 locus ( 21 sites ) , APOE locus ( nine sites ) , and HLA-DQA1 locus ( four sites ) ( Supplementary file 2 ) . We next used SOJO to perform conditional analysis on the same loci to find additional independent variants associated with lifespan . We find substantial allelic heterogeneity in several association intervals and identify an additional 335 variants , which increase out-of-sample explained variance from 0 . 095% to 0 . 169% ( 78% increase ) . CELSR2/PSRC1 , KCNK3 , HLA-DQA1 , LPA , ZW10 , FURIN/FES , and APOE are amongst the most heterogeneous loci with at least 25 variants per locus showing independent effects ( Supplementary file 3 ) . We next sought to understand the link between our lifespan variants and disease . We looked up known associations with our top hits and proxies ( r2 > 0 . 6 ) in the GWAS catalog ( MacArthur et al . , 2017 ) and PhenoScanner ( Staley et al . , 2016 ) , excluding loci identified in iGWAS as these used disease associations to build the effect priors . We also excluded trait associations discovered solely in UK Biobank , as the overlap with our sample could result in spurious association due to correlations between morbidity and mortality . Under these restrictions , we find alleles which increase lifespan associate with a reduction in cardiometabolic , autoimmune , smoking-related , and neuropsychiatric disease and their disease risk factors ( Table 1 , Table 1—source data 3 ) . None of the loci show any association with cancer other than lung cancer . We then looked up associations of the 81 iGWAS SNPs ( 1% FDR ) with the risk factor GWAMAs used to inform the prior . While associations are a priori limited to the risk factors included in the iGWAS , the pattern of association is still of interest . We find loci show strong clustering in either blood lipids or CVD , show moderate clustering of metabolic and neurological traits , and show weak but highly pleiotropic clustering amongst most of the remaining traits ( see Figure 3—figure supplement 1 for clustering of genome-wide significant SNPs ) . In order to study the relative contribution of diseases to lifespan , we approached the question from the other end and looked up known associations for disease categories ( CVD , type 2 diabetes , neurological disease , smoking-related traits , and cancers ) in large numbers ( >20 associations in each category ) from the GWAS catalog ( MacArthur et al . , 2017 ) and used our GWAS to see if the disease loci associate with lifespan . Our measure was lifespan variance explained ( LVE , years2 [Ljungquist et al . , 1998] ) by the locus , which balances effect size against frequency , and is proportional to selection response and the GWAS test statistic and thus monotonic for risk of false positive lifespan associations . Taking each independent disease variant , we ordered them by LVE , excluding any secondary disease where the locus was pleiotropic . The Alzheimer’s disease locus APOE shows the largest LVE ( 0 . 23 years2 ) , consistent with its most frequent discovery as a lifespan SNP in GWAS ( Joshi et al . , 2016; Pilling et al . , 2017; Deelen et al . , 2014; Deelen et al . , 2013 ) . Of the 20 largest LVE SNPs , 12 and 4 associate with CVD and smoking/lung cancer , respectively , while only two associate with other cancers ( near ZW10 and NRG1; neither in the top 15 LVE SNPs ) . Cumulatively , the top 20/45 LVE SNPs explain 0 . 33/0 . 43 years2 through CVD , 0 . 13/0 . 15 years2 through smoking and lung cancer , and 0 . 03/0 . 11 years2 through other cancers ( Figure 6 ) . Strikingly , two of the three largest LVE loci for non-lung cancers ( at or near ATXN2/BRAP and CDKN2B-AS1 ) show increased cancer protection associating with decreased lifespan ( due to antagonistic pleiotropy with CVD ) , while the third ( at or near MAGI3 ) also shows evidence of pleiotropy , having an association with CVD three times as strong as breast cancer , and in the same direction . In addition , 6 out of the 11 remaining cancer-protective loci which increase lifespan and pass FDR ( near ZW10 , NRG1 , C6orf106 , HNF1A , C20orf187 , and ABO ) also show significant associations with CVD but could not be tested for pleiotropy as we did not have data on the relative strength of association of every type of cancer against CVD , and thus ( conservatively from the point of view of our conclusion ) remain counted as cancer SNPs ( Figure 7 , Figure 7—source data 1 ) . Visual inspection also reveals an interesting pattern in the SNPs that did not pass FDR correction for affecting lifespan: cardio-protective variants associate almost exclusively with increased lifespan , while cancer-protective variants appear to associate with lifespan in either direction ( grey dots often appear below the x-axis for other cancers ) . Together , the disease loci included in our study with significant effects on lifespan explain 0 . 95 years2 , or less than 1% of the phenotypic variance of lifespan of European parents in UK Biobank ( 123 years2 ) , and around 5% of the heritability . We used stratified LD-score regression to assess whether cell type-specific regions of the genome are enriched for lifespan variants . As this method derives its power from SNP heritability , we limited the analysis to genomically British individuals in UK Biobank , which showed the lowest heterogeneity and the highest SNP heritability . At an FDR < 5% , we find enrichment in SNP heritability in five categories: two histone and two chromatin marks linked to male and female foetal brain cells , and one histone mark linked to the dorsolateral prefrontal cortex ( DLPC ) of the brain . Despite testing other cell types , such as heart , liver , and immune cells , no other categories are statistically significant after multiple testing correction ( Supplementary file 4 ) . We also determined which biological pathways could explain the associations between our genetic variants and lifespan using three different methods , VEGAS , PASCAL , and DEPICT . VEGAS highlights 33 gene sets at an FDR < 5% , but neither PASCAL nor DEPICT ( with SNP thresholds at p < 5 × 10–8 and p < 1 × 10–5 ) identify any gene sets passing multiple testing correction . The 33 gene sets highlighted by VEGAS are principally for blood lipid metabolism ( 21 ) , with the majority involving lipoproteins ( 14 ) or homeostasis ( 4 ) . Other noteworthy gene sets are neurological structure and function ( 5 ) and vesicle-mediated transport ( 3 ) . Enrichment was also found for organic hydroxy compound transport , macromolecular complex remodelling , signalling events mediated by stem cell factor receptor ( c-kit ) , and regulation of amyloid precursor protein catabolism ( Supplementary file 5 ) . Finally , we performed an analysis to assess whether genes that have been shown to change their expression with age ( Peters et al . , 2015 ) are likely to have a causal effect on lifespan itself . Starting with a set of independent SNPs affecting gene expression ( eQTLs ) , we created categories based on whether gene expression was age-dependent and whether the SNP was associated with lifespan in our study ( at varying levels of significance ) . We find eQTLs associated with lifespan are 1 . 69 to 3 . 39 times more likely to have age-dependent gene expression , depending on the P value threshold used to define the set of lifespan SNPs ( Supplementary file 6 ) . We calculated polygenic risk scores ( PRS ) for lifespan for two subsamples of UK Biobank ( Scottish individuals and a random selection of English/Welsh individuals ) , and one sample from the Estonian Biobank . The PRS were based on ( recalculated ) lifespan GWAS summary statistics that excluded these samples to ensure independence between training and testing datasets . When including all independent markers , we find an increase of one standard deviation in PRS increases lifespan by 0 . 8 to 1 . 1 years , after doubling observed parent effect sizes to compensate for the imputation of their genotypes ( see Table 3—source data 1 for a comparison of performance of different PRS thresholds ) . Correspondingly – again after doubling for parental imputation – we find a difference in median survival for the top and bottom deciles of PRS of 5 . 6/5 . 6 years for Scottish fathers/mothers , 6 . 4/4 . 8 for English and Welsh fathers/mothers and 3 . 0/2 . 8 for Estonian fathers/mothers . In the Estonian Biobank , where data is available for a wider range of subject ages ( i . e . beyond median survival age ) we find a contrast of 3 . 5/2 . 7 years in survival for male/female subjects , across the PRS tenth to first deciles ( Table 3 , Figure 8 ) . Finally , as we did for individual variants , we looked at the age- and sex-specific nature of the PRS on parental lifespan and then tested for associations with ( self-reported ) age-related diseases in subjects and their kin . We find a high PRS has a larger protective effect on lifespan for mothers than fathers in UK Biobank subsamples ( p = 0 . 0071 ) , and has a larger protective effect on lifespan in younger age bands ( p = 0 . 0001 ) ( Figure 9 ) , although in both cases , it should be borne in mind that women and younger people have a lower baseline hazard , so a greater improvement in hazard ratio does not necessarily mean a larger absolute protection . We find that overall , higher PRS scores ( i . e . genetically longer life ) are associated with less heart disease , diabetes , hypertension , respiratory disease and lung cancer , but increased prevalence of Alzheimer’s disease , Parkinson’s disease , prostate cancer and breast cancer , the last three primarily in parents . We find no association between the score and prevalence of cancer in subjects . ( Figure 10 ) .
Applying the kin-cohort method in a GWAS and mortality risk factor iGWAS across UK Biobank and the LifeGen meta-analysis , 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 . Conversely , all individual variants identified in our analyses showed directionally consistent effects in a meta-analysis of two European-ancestry studies of extreme longevity , and a test of association of a polygenic risk score of the variants was highly significant in the longevity dataset ( p < 1 . 5 × 10−5 ) . Our findings validate the results of a previous Bayesian analysis performed on a subset ( N = 116 , 279 ) of the present study’s discovery sample ( McDaid et al . , 2017 ) , which highlighted two loci which are now genome-wide significant in conventional GWAS in the present study’s larger sample . iGWAS thus appears to be an effective method able to identify lifespan-associated variants in smaller samples than standard GWAS , albeit relying on known biology . With the curious exception of a locus near HTT ( the Huntington's disease gene ) , all lead SNPs are known to associate with autoimmune , cardiometabolic , neuropsychiatric , or smoking-related disease , and it is plausible these are the major pathways through which the variants affect lifespan . Whole-genome polygenic risk scores showed similar associations with disease , excluding late-onset disorders such as Alzheimer’s and Parkinson’s , where polygenic risk scores for extended lifespan increased risk ( of survival to age at onset ) of the disease . Genetic variants affecting lifespan were enriched for pathways involving the transport , homeostasis and metabolism of lipoprotein particles , validating previous reports ( McDaid et al . , 2017 ) . We also identified new pathways including vesicle transport , metabolism of acylglycerol and sterols , and synaptic and dendritic function . We discovered genomic regions with epigenetic marks determining cell differentiation into foetal brain and DLPC cells were enriched for genetic variants affecting lifespan . Finally , we showed that we can use our GWAS results to construct a polygenic risk score , which makes 3 to 5 year distinctions in life expectancy at birth between individuals from the score’s top and bottom deciles . 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 by Wood et al . , 2014 . found 423 loci . This difference can partly be explained by the much lower heritability of lifespan ( 0 . 12; Kaplanis et al . , 2018 ) ( cf . 0 . 8 for height [Wood et al . , 2014] ) , 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 ( the kin-cohort method ) 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 ( see Materials and methods ) . 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 as Wood et al . At the same time , our inability to replicate several previous borderline significant longevity and lifespan findings suggests research into survival in general requires substantial increases in power to robustly identify loci . Meta-analysis of mothers and fathers , permitting common or sex-specific effect sizes , of course , doubled effective sample size , with slight attenuation to reflect the observed correlation ( ~10% ) between father and mother traits ( consistent with previous studies [Kaplanis et al . , 2018] ) . This correlation indicates the presence of assortative mating on traits which correlate with lifespan ( as lifespan itself is of course not observed until later ) , or post-pairing environmental convergence . We note that in principle , assortative mating could lead to allelic correlations at causal loci for the contributing traits , causing departures from Hardy-Weinberg equilibrium , and increasing the genotypic variance and thus power to detect association . However , in practice , at least for lifespan , the effects are too small for the effect to be material . The association of lifespan variants with well-known , life-shortening diseases ( cardiovascular , autoimmune , smoking-related diseases and lung cancer; Mathers et al . , 2018 ) is not surprising , but the paucity of associations with other forms of cancer – without pleiotropic effects on CVD – is . This paucity suggests cancer deaths may often be due to ( perhaps many ) rarer variants or environmental exposures , although effect sizes might simply be slightly below our cut-off threshold to detect . Disappointingly , the variants and pathways we identified do not appear to underpin a generalised form of ageing independent of disease . Our finding that lifespan genetics are enriched for lipid metabolism genes is in line with expectations , given lipid metabolites – especially cholesterol metabolites – have well-established effects on atherosclerosis , type 2 diabetes , Alzheimer’s disease , osteoporosis , and age-related cancers ( Zarrouk et al . , 2014 ) . Pilling et al . ( 2017 ) implicated nicotinic acetylcholine receptor pathways in human lifespan , which we detected at nominal significance ( p = 2 × 10−4 ) but not at 5% FDR correction ( q = 0 . 0556 ) . Instead we highlighted more general synapse and dendrite pathways and identified foetal brain and DLPC cells as important in ageing . The DLPC is involved in smoking addiction ( Hayashi et al . , 2013 ) , dietary self-control ( Lowe et al . , 2014 ) , and is susceptible to neurodegeneration ( Morrison and Baxter , 2012 ) , which could explain why genetic variation for lifespan is specifically enriched in these cells , mediated through smoking-related , cardiometabolic , and neuropsychiatric disease . Much work has been done implicating FOXO3 as an ageing gene in model organisms ( Kenyon et al . , 1993; Hwangbo et al . , 2004 ) , however we found the association in humans at that locus may be driven by expression of SESN1 ( admittedly a finding restricted to peripheral blood tissue ) . SESN1 is a gene connected to the FOXO3 promoter via chromatin interactions and is involved in the response to reactive oxygen species and mTORC1 inhibition ( Donlon et al . , 2017 ) . While fine-mapping studies have specifically found genetic variation within the locus causes differential expression of FOXO3 itself ( Flachsbart et al . , 2017; Grossi et al . , 2018 ) , this does not rule out the effect of co-expression of SESN1 . More powered tissue-specific expression data and experimental work on SESN1 vs . FOXO3 could elucidate the causal mechanism . For now , results from model organisms seem to leave the preponderance of evidence for FOXO3 . Our results suggest disease-associated lifespan variants reduce the chances of extreme long-livedness , but remain agnostic as to the more interesting two-part question: are there longevity variants that have little effect on lifespan in the normal range ( Sebastiani et al . , 2016 ) , and if so , do they control underlying ageing processes ? We note , the genetic overlap between lifespan and extreme long-livedness is high ( 0 . 73 ) , but not complete ( McDaid et al . , 2017 ) . Regardless of this , only a small part of the heritability of both lifespan and longevity has thus far been explained by GWAS . It thus remains plausible that an enlarged long-livedness or lifespan study will find variants controlling the rate of ageing and associated pathways . Curiously , we find little evidence of SNPs of large deleterious effect on lifespan acting with antagonistic pleiotropy on other fitness and developmental component traits , despite long-standing theoretical suggestions to the contrary ( Williams , 1957 ) . However , we did not examine mortality before the age of 40 , or mortality of individuals without offspring ( by definition as we were examining parental lifespans ) , which may exhibit this phenomenon . For the time being , our findings that the improved polygenic risk score for lifespan was associated with an increased prevalence of Alzheimer's disease , Parkinson's disease , and prostate and breast cancer , means we appear to be predominantly measuring a propensity for longer life through avoidance of early disease-induced mortality , rather than healthy ageing or fertility costs . Whilst it has previously been shown that transcriptomic age calculated based on age-related genes is meaningful in the sense that its deviation from the chronological age is associated with biological features linked to ageing ( Peters et al . , 2015 ) , the role of these genes in ageing was unclear . A gene might change expression with age because ( i ) it is a biological clock ( higher expression tracking biological ageing , but not influencing ageing or disease ) ; ( ii ) it is a response to the consequences of ageing ( e . g . a protective response to CVD ) ; ( iii ) it is an indicator of selection bias: if low expression is life-shortening , older people with low expression tend to be eliminated from the study , hence the average expression level of older age groups is higher . However , our results now show that the differential expression of many of the age-related genes discovered by Peters et al . , 2015 are not only biomarkers of ageing , but are also enriched for direct effects on lifespan . There is increasing interest in polygenic risk scores , and their potential clinical utility for some diseases appears to be similar to some Mendelian mutations ( albeit such monogenic tests are usually only applied in the context of family history; Khera et al . , 2018 ) . At first sight , the magnitude of the distinctions in our genetic lifespan score ( 5 years of life between top and bottom deciles , for both the parent and subject generations ) are quite small compared with variability in individual lifespans . However , these distinctions are potentially material at a group level , for example , actuarially . The implied distinction in price ( 14%; Methods ) is greater than some recently reported annuity profit margins ( 8 . 9% ) ( Legal General Group PLC , 2017 ) . In our view , the legal and ethical frameworks ( at least in the UK [Association of British Insurers and UK Government , 2014] ) are presently under-developed for genome-wide scores , whether for disease or lifespan and this needs to be urgently addressed . At the same time , although material in isolation , our lifespan associations may only have practical utility in many applications if they provide additional information than that provided by conventional clinical risk measures ( e . g . the Framingham score [D'Agostino et al . , 2008] ) . Such an assessment has been beyond the scope of this work , in part as such risk measures are not readily available for the parents ( rather than subjects ) studied . One limitation of our study was the power reduction caused by the exclusion of relatives in our study , rather than linear mixed modelling ( LMM ) with a term for kinship as measured by the genomic relationship matrix ( GRM ) ( Pilling et al . , 2017; Loh et al . , 2015 ) . However , as the correct adjustment is not derivable under the kin-cohort method , we felt this was the best approach . To see that the normal adjustment is not correct , consider two siblings . The phenotypes under study are of course identical ( as the parents are the same ) , but the expected correlation under the mixed model would only be 50% of the heritability . Simply excluding siblings , however , is not sufficient . For example , consider two offspring subjects who are first cousins descended from two full brothers . The GRM entry in this situation is 12 . 5% whilst the appropriate relatedness factor for the father trait is 50% and the mother 0% . Exclusion of relatives thus appears the most straightforward solution , although if a pedigree were available , not just a GRM , accurate LMM might have been feasible . The analysis of parent lifespans has enabled us to probe mortality for a generation whose lives are mostly complete and attain increased power in a survival GWAS . However , changes in the environment ( and thus the relative importance of each genetic susceptibility , for example following the smoking ban ) inevitably mean we have less certainty about associations with prospective lifespans for the present generation of middle-aged people , or a different population ( with perhaps different relative importance of disease or even overall heritability of lifespan ) . The 21% reduction in the effect size of the association between our PRS for the UK offspring generation supports this idea , although the estimated contrast in hazard ratios across the deciles was not reduced , which may be a statistical artefact or due to the different periods of life probed . The lower explanatory power of the PRS in Estonia may reflect the differing alleles and LD patterns between the UK training data and the Estonian test data , but also the different environments , in particular the sources of mortality in that country in the Soviet , and early post-Soviet era . In conclusion , recent genomic susceptibility to death in the normal age range seems rooted in modern diseases: Alzheimer’s , CVD and lung cancer; in turn arising from our modern – long-lived , obesogenic and tobacco-laden – environment , however the keys to the distinct traits of ageing and extreme longevity remain elusive . At the same time , genomic information alone can now make material distinctions at a group level in variations in expected length of life , although the limited individual accuracy of these distinctions is far from reaching genetic determinism of that most ( self- ) interesting of traits – your lifespan .
Individual phenotypic and genetic data is available from UK Biobank upon application: https://www . ukbiobank . ac . uk/register-apply/ . Phenotypes used in this work include subject age , sex , ethnicity , relatedness , genotyping batch , array , and principal components , as well as parental age and alive/dead status . Also included are self-reported diseases of subjects and their families . A full description of our application can be found at https://www . ukbiobank . ac . uk/2015/02/dr-james-wilson-university-of-edinburgh-centre-for-population-sciences/ . The results that support our findings , in particular , the GWAS summary statistics we generated for >1 million parental lifespans in this study are available at http://dx . doi . org/10 . 7488/ds/2463 . Gene expression data used in the age-related gene analysis is being made available by the eQTLGen Consortium , see http://www . eqtlgen . org/ and Võsa et al . , 2018 . Single tissue gene expression data used in the SMR-Heidi analysis can be found on the GTEx website https://gtexportal . org/home/datasets , under GTEx_Analysis_v7_eQTL . tar . gz . MultiABEL: https://github . com/xiashen/MultiABEL/ LDSC: https://github . com/bulik/ldsc SMR/HEIDI: https://cnsgenomics . com/software/smr/ SOJO: https://github . com/zhenin/sojo/ DEPICT: https://www . broadinstitute . org/mpg/depict/ PASCAL: https://www2 . unil . ch/cbg/index . php ? title=Pascal GTEx: https://gtexportal . org/home/datasets
We use a genome-wide association of 1 million parental lifespans of genotyped subjects and data on mortality risk factors to validate previously unreplicated findings near CDKN2B-AS1 , ATXN2/BRAP , FURIN/FES , ZW10 , PSORS1C3 , and 13q21 . 31 , and identify and replicate novel findings near ABO , ZC3HC1 , and IGF2R . We also validate previous findings near 5q33 . 3/EBF1 and FOXO3 , whilst finding contradictory evidence at other loci . Gene set and cell-specific analyses show that expression in foetal brain cells and adult dorsolateral prefrontal cortex is enriched for lifespan variation , as are gene pathways involving lipid proteins and homeostasis , vesicle-mediated transport , and synaptic function . Individual genetic variants that increase dementia , cardiovascular disease , and lung cancer – but not other cancers – explain the most variance . Resulting polygenic scores show a mean lifespan difference of around five years of life across the deciles . Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review . The Reviewing Editor's assessment is that all the issues have been addressed ( see decision letter ) .
Ageing happens to us all , and as the cabaret singer Maurice Chevalier pointed out , "old age is not that bad when you consider the alternative" . Yet , the growing ageing population of most developed countries presents challenges to healthcare systems and government finances . For many older people , long periods of ill health are part of the end of life , and so a better understanding of ageing could offer the opportunity to prolong healthy living into old age . Ageing is complex and takes a long time to study – a lifetime in fact . This makes it difficult to discern its causes , among the countless possibilities based on an individual’s genes , behaviour or environment . While thousands of regions in an individual’s genetic makeup are known to influence their risk of different diseases , those that affect how long they will live have proved harder to disentangle . Timmers et al . sought to pinpoint such regions , and then use this information to predict , based on their DNA , whether someone had a better or worse chance of living longer than average . The DNA of over 500 , 000 people was read to reveal the specific ‘genetic fingerprints’ of each participant . Then , after asking each of the participants how long both of their parents had lived , Timmers et al . pinpointed 12 DNA regions that affect lifespan . Five of these regions were new and had not been linked to lifespan before . Across the twelve as a whole several were known to be involved in Alzheimer’s disease , smoking-related cancer or heart disease . Looking at the entire genome , Timmers et al . could then predict a lifespan score for each individual , and when they sorted participants into ten groups based on these scores they found that top group lived five years longer than the bottom , on average . Many factors beside genetics influence how long a person will live and our lifespan cannot be read from our DNA alone . Nevertheless , Timmers et al . had hoped to narrow down their 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 .