Genomics of 1 million parent lifespans implicates novel pathways and common diseases and distinguishes survival chances

*For correspondence: [email protected] Group author details: eQTLGen Consortium See page 31 Competing interest: See page 33 Funding: See page 33 Received: 05 July 2018 Accepted: 20 November 2018 Published: 15 January 2019 Copyright Timmers et al. 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. Genomics of 1 million parent lifespans implicates novel pathways and common diseases and distinguishes survival chances Paul RHJ Timmers 1 , Ninon Mounier 2,3 , Kristi Lall 4,5 , Krista Fischer 4,5 , Zheng Ning 6 , Xiao Feng 7 , Andrew D Bretherick 8 , David W Clark 1 , eQTLGen Consortium, Xia Shen 1,6,7 , To ˜nu Esko 4,9 , Zolta ´n Kutalik 2,3 , James F Wilson 1,8 , Peter K Joshi 1,2 * 1 Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom; 2 Institute of Social and Preventive Medicine, University Hospital of Lausanne, Lausanne, Switzerland; 3 Swiss Institute of Bioinformatics, Lausanne, Switzerland; 4 Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia; 5 Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia; 6 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; 7 State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China; 8 MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom; 9 Broad Institute of Harvard and MIT, Cambridge, United States Abstract 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 cellspecific 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). DOI: https://doi.org/10.7554/eLife.39856.001 Introduction 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 Timmers et al. eLife 2019;8:e39856. DOI: https://doi.org/10.7554/eLife.39856 1 of 40 RESEARCH COMMUNICATION 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 kincohort 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. eLife digest 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. DOI: https://doi.org/10.7554/eLife.39856.002 Timmers et al. eLife 2019;8:e39856. DOI: https://doi.org/10.7554/eLife.39856 2 of 40 Research Communication Genetics and Genomics 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. Results Genome-wide association analysis 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 d3Timmers et al. eLife 2019;8:e39856. DOI: https://doi.org/10.7554/eLife.39856 3 of 40 Research Communication Genetics and Genomics GHR does not associate with lifespan with any (recessive or additive) effect similar to that originally reported (Figure 2,Figure 2—source data 1). Mortality risk factor-informed GWAS (iGWAS) 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 (r 2 > 0.8) reach nominal replication (p < 0.05, onesided 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– Figure 1. SNP associations with lifespan across both parents under the assumption of common and sex-specific effect sizes. Miami plot of genetic associations with joint parental survival. In purple are the associations under the assumption of common SNP effect sizes across sexes (CES); in green are the associations under the assumption of sex-specific effect sizes (SSE). P refers to the two-sided P values for association of allelic dosage on survival under the residualised Cox model. The red line represents our multiple testing-adjusted genome-wide significance threshold (p = 2.5 10 8 ). Annotated are the gene, set of genes, or cytogenetic band near the index SNP, marked in red. P values have been capped at –log 10 (p) = 15 to better visualise associations close to genome-wide significance. SNPs with P values beyond this cap (near APOE,CHRNA3/5 and LPA) are represented by triangles. DOI: https://doi.org/10.7554/eLife.39856.007 Timmers et al. eLife 2019;8:e39856. DOI: https://doi.org/10.7554/eLife.39856 4 of 40 Research Communication Genetics and Genomics 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). Table 1. Twelve genome-wide significant associations with lifespan using UK Biobank and LifeGen. Parental phenotypes from UK Biobank and LifeGen meta-analysis, described in Table 1—source data 1, were tested for association with subject genotype. See Table 1—source data 2 for LD Score regression intercept of each cohort separately and combined. Displayed here are loci associating with lifespan at genome-wide significance (p < 2.5 10 8 ). At or near – Gene, set of genes, or cytogenetic band nearest to the index SNP; rsID – The index SNP with the lowest P value in the standard or sex-specific effect (SSE) analysis. Chr – Chromosome; Position – Base-pair position on chromosome (GRCh37); A1 – the effect allele, increasing lifespan; Freq1 – Frequency of the A1 allele; Years1 – Years of life gained for carrying one copy of the A1 allele; SE – Standard Error; P – the P value for the Wald test of association between imputed dosage and cox model residual; Disease – Category of disease for known associations with SNP or close proxies (r 2 > 0.6), see Table 1—source data 3 for details and references. Despite the well-known function of the HTT gene in Huntington’s disease, SNPs within the identified locus near this gene have not been associated with the disease at genome-wide significance. At or near rsID Chr Position A1 Freq1 Years1 SE P SSE P Disease MAGI3 rs1230666 1 114173410 G 0.85 0.3224 0.0555 6.4E-09 6.1E-08 Autoimmune KCNK3 rs1275922 2 26932887 G 0.74 0.2579 0.0443 6.0E-09 2.7E-07 Cardiometabolic HTT rs61348208 4 3089564 T 0.39 0.2299 0.0395 5.8E-09 1.2E-07 - HLA-DQA1 rs34967069 6 32591248 T 0.07 0.5613 0.0956 4.3E-09 3.6E-09 Autoimmune LPA rs10455872 6 161010118 A 0.92 0.7639 0.0743 8.5E-25 3.1E-24 Cardiometabolic CDKN2B-AS1 rs1556516 9 22100176 G 0.50 0.2510 0.0386 7.5E-11 6.4E-12 Cardiometabolic ATXN2/BRAP rs11065979 12 112059557 C 0.56 0.2798 0.0393 1.0E-12 6.2E-13 Autoimmune/ Cardiometabolic CHRNA3/5 rs8042849 15 78817929 T 0.65 0.4368 0.0410 1.6E-26 1.9E-30 Smoking-related FURIN/FES rs6224 15 91423543 G 0.52 0.2507 0.0390 1.3E-10 1.8E-09 Cardiometabolic HP rs12924886 16 72075593 A 0.80 0.2798 0.0493 1.4E-08 9.1E-08 Cardiometabolic LDLR rs142158911 19 11190534 A 0.12 0.3550 0.0616 8.1E-09 3.3E-08 Cardiometabolic APOE rs429358 19 45411941 T 0.85 1.0561 0.0546 3.1E-83 1.8E-85 Cardiometabolic/ Neuropsychiatric DOI: https://doi.org/10.7554/eLife.39856.003 The following source data is available for Table 1: Source data 1. Descriptive statistics of the cohorts and lives analysed. Summary statistics for the 1,012,240 parental lifespans passing phenotypic QC (most notably, parent age > 40). In practice, fewer lives than these were analysed for some SNPs, as a SNP may not have passed QC in all cohorts (in particular LifeGen MAF > 1%). Ancestries in UK Biobank are self-declared, except in the case of Gen. British. Gen. British – Participants identified as genomically British by UK Biobank, based on their genomic profile. LifeGen – A consortium of 26 population cohorts of European Ancestry, with UK Biobank lives removed. DOI: https://doi.org/10.7554/eLife.39856.004 Source data 2. LD-score regression intercepts for GWAS results. Regression intercepts (standard error) of the GWAS summary statistics as calculated by LD-score regression, using LD scores from on average 457,407 SNPs from the UK Biobank array. CES – Results under the assumption of common effect sizes across sexes, SSE – Results allowing for sex-specific effects. DOI: https://doi.org/10.7554/eLife.39856.005 Source data 3. Known associations with genome-wide significant lifespan loci. Genome-wide significant associations from the GWAS catalog and PhenoScanner are reported for the lead SNP and proxies (r 2 > 0.6). Similar associations have been grouped, keeping the most significant association and the shortest trait name (Trait). At or near – Gene or cluster of genes in close proximity to lead SNP; A1 – the effect allele, increasing lifespan; A0 – the reference allele. Freq1Frequency of the A1 allele in the original study, or if missing, averaged from all associations; Beta1 – the reported effect on the trait for carrying one copy of the A1 allele; SE – Standard Error; P – P value; Disease – the type of lifespan-shortening diseases linked to the trait, or ‘other’ if the link is unclear or multiple disease links exist. DOI: https://doi.org/10.7554/eLife.39856.006 Timmers et al. eLife 2019;8:e39856. DOI: https://doi.org/10.7554/eLife.39856 5 of 40 Research Communication Genetics and Genomics Figure 2. Validation of SNPs identified in other studies using independent samples of European descent. Discovery – Candidate SNPs or proxies (r 2 > 0.95) associated with lifespan (top panels, stratified by sex) and longevity (bottom panel) by previous studies (Zeng et al., 2016;Pilling et al., 2017;Deelen et al., 2014; Flachsbart et al., 2009;Sebastiani et al., 2017;Ben-Avraham et al., 2017). Effect sizes have been rescaled to years of life to make direct comparisons between studies (see Materials and methods and Figure 2—figure supplement 1). Replication – Independent samples, either the LifeGen meta-analysis to replicate Pilling et al. (2017), or the full dataset including UK Biobank. Gene names are as reported by discovery and have been coloured based on overlap between confidence intervals (CIs) of effect estimates. Dark blue – Nominal replication (p < 0.05, one-sided test). Light blue – CIs overlap (P het > 0.05) and cover zero, but replication estimate is closer to discovery than zero. Yellow – CIs overlap (P het > 0.05) and cover zero, and replication estimate is closer to zero than discovery. Red – CIs do not overlap (P het < 0.05) and replication estimate covers zero. Black – no replication data. DOI: https://doi.org/10.7554/eLife.39856.008 The following source data and figure supplement are available for figure 2: Source data 1. Eight candidate lifespan regions replicate nominally (p < 0.05) in LifeGen or our full sample. DOI: https://doi.org/10.7554/eLife.39856.010 Figure supplement 1. Concordance between inferred effect sizes from Pilling et al. (2017) and our estimated effect sizes in a largely overlapping UK Biobank sample. DOI: https://doi.org/10.7554/eLife.39856.009 Timmers et al. eLife 2019;8:e39856. DOI: https://doi.org/10.7554/eLife.39856 6 of 40 Research Communication Genetics and Genomics Sexand age-specific effects We stratified our UK Biobank sample (for which we had individual level data) by sex and age bands to identify sexand 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 e4 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. Causal genes and methylation sites 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/ Figure 3. SNP associations with lifespan across both parents when taking into account prior information on mortality risk factors. Bayesian iGWAS was performed using observed associations from the lifespan GWAS and priors based on 16 traits selected by an AIC-based stepwise model. As the P values were assigned empirically using a permutation approach, the minimum P value is limited by the number of permutations; SNPs reaching this limit are represented by triangles. Annotated are the gene, cluster of genes, or cytogenetic band in close proximity to the top SNP. The red line represents the genome-wide significance threshold (p = 2.5 10 8 ). The blue line represents the 1% FDR threshold. Figure 3—figure supplement 1 shows the associations of each genome-wide significant SNP with the 16 risk factors. DOI: https://doi.org/10.7554/eLife.39856.011 The following figure supplement is available for figure 3: Figure supplement 1. Heat map of the effect of genome-wide significant iGWAS SNPs on the mortality risk factors. DOI: https://doi.org/10.7554/eLife.39856.012 Timmers et al. eLife 2019;8:e39856. DOI: https://doi.org/10.7554/eLife.39856 7 of 40 Research Communication Genetics and Genomics FES, and APOE are amongst the most heterogeneous loci with at least 25 variants per locus showing independent effects (Supplementary file 3). Disease and lifespan We next sought to understand the link between our lifespan variants and disease. We looked up known associations with our top hits and proxies (r 2 > 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. Table 2. Bayesian GWAS using mortality risk factors reveals seven additional genome-wide significant variants. At or near – Gene or set of genes nearest to the index SNP; rsID – The index SNP with the lowest P value in the risk factor-informed analysis. Chr – Chromosome; Position – Base-pair position on chromosome (GRCh37); A1 – the effect allele, increasing lifespan; Freq1 – Frequency of the A1 allele; Years1 – Years of life gained for carrying one copy of the A1 allele; SE – Standard Error; CES P – the P value for the Wald test of association between imputed dosage and cox model residual, under the assumption of common effects between sexes. Risk – mortality risk factors associated with the variant (p < 3.81 10 5 , accounting for 82 independent SNPs and 16 independent factors). BF P – Empirical P value derived from permutating Bayes Factors. See Table 2—source data 1 for the causal estimate of each risk factor. See Table 2—source data 2 for all SNPs significant at FDR < 1%. At or near rsID Chr Position A1 Freq1 Years1 SE CES P Risk BF P CELSR2/PSRC1 rs4970836 1 109821797 G 0.23 0.2234 0.0463 1.4E-06 LDL HDL CAD 1.6E-09 TMEM18 rs6744653 2 628524 A 0.17 0.2772 0.0511 5.8E-08 BMI 7.0E-10 GBX2/ASB18 rs10211471 2 237081854 C 0.80 0.2401 0.0493 1.1E-06 Education 2.3E-08 IGF2R rs111333005 6 160487196 G 0.98 0.8665 0.1577 3.9E-08 LDL CAD 6.6E-09 POM121C rs113160991 7 75094329 G 0.78 0.2541 0.0495 2.8E-07 BMI Insulin 7.5E-09 ZC3HC1 rs56179563 7 129685597 A 0.39 0.2107 0.0406 2.1E-07 CAD 5.6E-09 ABO rs2519093 9 136141870 C 0.81 0.2244 0.0497 6.3E-06 LDL CAD 1.9E-08 DOI: https://doi.org/10.7554/eLife.39856.013 The following source data is available for Table 2: Source data 1. Bayesian GWAS - Multivariate effect estimates for the 16 traits chosen by the AIC based stepwise model selection. The multivariate MR identified 16 traits (58 tested, see McDaid et al., 2017 for an exhaustive list) with significant causal effect on lifespan and used the effect estimates to create the prior assumption of the expected effect size of each variant on lifespan, in the (Bayesian) iGWAS. Effect Estimate – the estimated effect of standardized trait on standardized lifespan, in multivariate model. SE – the standard error of the estimated effect, in multivariate model. P – the P value (two sided) from MR, for testing association between standardized trait and standardized lifespan, in multivariate model. DOI: https://doi.org/10.7554/eLife.39856.014 Source data 2. 82 SNPs significantly associated with lifespan at 1% FDR and the SNP’s associations with risk factors. Bayesian iGWAS was performed using observed association results from CES GWAS and priors from 16 risk factors selected by AIC based stepwise model selection. Bayes Factors were calculated to compare effect estimates observed in the conventional GWAS to the prior effect computed. Empirical P values were assigned using a permutation approach and further corrected for multiple testing using Benjamini-Hochberg correction. Chr – Chromosome, Position – Base-pair position on chromosome (GRCh37), A1 – Effect Allele, Freq1 – Frequency of the A1 allele (from conventional GWAS), Beta1 (from conventional GWAS), SE – Standard Error of Beta1, Years – Years of lifespan gained for carrying one copy of the A1 allele (from conventional GWAS), P – P value (from conventional GWAS), PriorEffect – Prior effect estimate calculated from the summary statistics data for the 16 risk factors identified, PriorSE – Standard Error of the prior effect estimate, LogBF – Log of the observed Bayes Factor, P_BF – Empirical P value from a permutation approach for the log Bayes Factor. Final columns show the P value of each SNP in the studies used to calculate the prior, if the P value is significant after Bonferroni multiple testing correction (p < 3.81 10 5 , 82*16 tests) the cell is shaded green. Counts of these significant associations by SNP/trait are shown in the final column/row. DOI: https://doi.org/10.7554/eLife.39856.015 Timmers et al. eLife 2019;8:e39856. DOI: https://doi.org/10.7554/eLife.39856 8 of 40 Research Communication Genetics and Genomics 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 −1 0 1 2 3 APOE (rs6857) MAGI3 (rs1230666) CHRNA3/5 (rs8031948) HTT (rs2285086) ATXN2/BRAP (rs11065987) FURIN/FES (rs17514846) KCNK3 (rs11126666) HP (rs2000999) CDKN2B−AS1 (rs1556516) LDLR (rs6511720) HLA−DQA1 (rs3129720) LPA (rs10455872) GBX2/ASB18 (rs6757542) TMEM18 (rs6744653) CELSR2/PSRC1 (rs602633) POM121C (rs6944634) IGF2R (rs3903279) ZC3HC1 (rs11556924) ABO (rs651007) Summary Alpha Analysis GWAS iGWAS Figure 4. Collective replication of individual lifespan SNPs using GWAMAs for extreme long-livedness shows directional consistency in all cases. Forest plot of effect size ratios between genome-wide significant lifespan variants from our study and external longevity studies (Broer et al., 2015;Deelen et al., 2014), having converted longevity effect sizes to our scale using APOE as benchmark (see Materials and methods and Figure 4—source data 1). Alpha – ratio of replication to discovery effect sizes on the common scale and 95% CI (reflecting uncertainty in the numerator and denominator; P values are for one-sided test). A true (rather than estimated) ratio of 1 indicates the relationship between SNP effect on lifetime hazard and extreme longevity is the same as that of APOE, while a ratio of zero suggests no effect on longevity. A true ratio between 0 and 1 suggests a stronger effect on lifetime hazard than longevity relative to APOE. SNPs overlapping both 0 and 1 are individually underpowered. The inverse variance meta-analysis of alpha over all SNPs, excluding APOE, is 0.42 (95% 0.23 to 0.61; p = 1.35 10 –5 ) for H 0 alpha = 0. DOI: https://doi.org/10.7554/eLife.39856.016 The following source data is available for figure 4: Source data 1. Replication of lead SNPs associating with lifespan using published longevity GWAS. DOI: https://doi.org/10.7554/eLife.39856.017 Timmers et al. eLife 2019;8:e39856. DOI: https://doi.org/10.7554/eLife.39856 9 of 40 Research Communication Genetics and Genomics 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 parentoffspring 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 sex p: 0.0071 age p: 0.0001 0.0 0.1 0.2 0.3 0.4 40−50 50−60 60−70 70−80 80−90 90−120 Age range Beta Female Male Figure 9. Sex and age specific effects of polygenic survival score (PRS) on parental lifespan in UK Biobank. The effect of out-of-sample PRS on parental lifespan stratified by sex and age was estimated for Scottish and English/ Welsh subsamples individually (see Figure 9—figure supplement 1) and subsequently meta-analysed. The estimate for the PRS on father lifespan in the highest age range has very wide confidence intervals (CI) due to the limited number of fathers surviving past 90 years of age. The beta 95% CI for this estimate is –0.15 to 0.57. Beta – log e (protection ratio) for one standard deviation of PRS for increased lifespan in self in the age band (i.e. 2 x observed due to 50% kinship), bounds shown are 95% CI; Age range – the range of ages over which beta was estimated; sex p – P value for association of effect size with sex; age p – P value for association of effect size with age. DOI: https://doi.org/10.7554/eLife.39856.028 The following source data and figure supplement are available for figure 9: Source data 1. Sex and age-stratified association of polygenic score on lifespan. DOI: https://doi.org/10.7554/eLife.39856.029 Figure supplement 1. Sex and age specific effects of polygenic survival score (PRS) on parental lifespan of Scottish and English/Welsh subsamples of UK Biobank. DOI: https://doi.org/10.7554/eLife.39856.030 Timmers et al. eLife 2019;8:e39856. DOI: https://doi.org/10.7554/eLife.39856 16 of 40 Research Communication Genetics and Genomics 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 Figure 10. Associations between polygenic lifespan score and diseases of UK Biobank subjects and their kin. Logistic regression was performed on standardised polygenic survival score (all variants) and 21 disease traits reported by 24,059 Scottish and 29,815 English/Welsh out-of-sample individuals about themselves and their kin. For grouping of UK Biobank disease codes, see Figure 10—source data 1. Displayed here are inverse-variance meta-analysed estimates of the diseases for which multiple sources of data were available (i.e. parents and/or siblings; see Figure 10—figure supplement 1 for all associations). ‘Cancer’ is only in subjects, whilst the specific subtypes are analysed for kin. The left panel shows disease estimates for each kin separately; the right panel shows the combined estimate, with standard errors adjusted for correlation between family members. Diseases have been ordered by magnitude of effect size (combined estimate). Beta – log odds reduction ratio of disease per standard deviation of polygenic survival score, where a negative beta indicates a deleterious effect of score on disease prevalence (lifetime so far), and positive beta indicates a protective effect on disease. Effect sizes for first degree relatives have been doubled. Cancer – Binary cancer phenotype (any cancer, yes/no). DOI: https://doi.org/10.7554/eLife.39856.031 The following source data and figure supplement are available for figure 10: Source data 1. Grouping of UK Biobank disease codes into diseases and major disease categories. DOI: https://doi.org/10.7554/eLife.39856.032 Source data 2. Associations of polygenic score with diseases in UK Biobank. DOI: https://doi.org/10.7554/eLife.39856.033 Figure supplement 1. Associations between polygenic survival score and diseases of individuals and their kin from Scottish and English/Welsh subsamples of UK Biobank. DOI: https://doi.org/10.7554/eLife.39856.034 Timmers et al. eLife 2019;8:e39856. DOI: https://doi.org/10.7554/eLife.39856 17 of 40 Research Communication Genetics and Genomics 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 finemapping 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 longlivedness, 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 underdeveloped for genome-wide scores, whether for disease or lifespan and this needs to be urgently Timmers et al. eLife 2019;8:e39856. DOI: https://doi.org/10.7554/eLife.39856 18 of 40 Research Communication Genetics and Genomics 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. Materials and methods Summary GWAS For genetically British ancestry (as identified by UK Biobank using genomic PCA) and each selfreported European ethnicity in UK Biobank (including self-declared British but not genetically British ancestry), independent association analyses were performed between unrelated subjects’ genotypes (MAF > 0.005; HRC imputed SNPs only; ~9 million markers) and parent survival using age and alive/ dead status in residualised Cox models, as described in Joshi et al. (2017). To account for parental genotype imputation, effect sizes were doubled, yielding log hazard ratios for the allele in carriers themselves. These values were negated to obtain a measure of log protection ratio, where higher values indicate longer life. While methods exist to account for related individuals using linear mixed models, such as BOLT-LMM (Loh et al., 2015), these are not accurate when trying to account for relatedness between parents (See Detailed Materials and methods). Mother and father survival information was combined in two separate ways, essentially assuming the effects were the same in men and women, or allowing for sex-specific effect sizes (SSE), with appropriate allowance for the covariance amongst the traits. For the first analysis we summed parental survival residuals; for the second analysis we used MANOVA, implemented in MultiABEL (Shen et al., 2015). Timmers et al. eLife 2019;8:e39856. DOI: https://doi.org/10.7554/eLife.39856 19 of 40 Research Communication Genetics and Genomics For LifeGen, where individual-level data was not available, parent survival summary statistics were combined using conventional fixed-effects meta-analysis, adjusted to account for the correlation between survival traits (estimated from summary-level data). For SSE, the same procedure was followed as for the UK Biobank samples, with correlation between traits again estimated from summary-level data. The GWAS statistics showed acceptable inflation, as measured by their LD-score regression intercept (<1.06, Table 1—source data 2). Candidate SNP replication Effect sizes from longevity studies were converted to our scale using an empirical conversion factor, based on the observed relationships between longevity and hazard ratio at the most significant variant at or near APOE, observed in the candidate SNPs study and our data (Joshi et al., 2017). These studies were then meta-analysed using inverse variance weighting and standard errors were inflated to account for sample overlap (see Detailed Methods) Estimates reported in Pilling et al. (2017) were based on rank-normalized Martingale residuals, unadjusted for the proportion dead, which – for individual parents – could be converted to our scale by multiplying by sqrt(c)/c, where c is the proportion dead in the original study (see Detailed Methods for derivation). Combined parent estimates were converted using the same method as the one used for longevity studies. The deletion reported by Ben-Avraham et al. (2017) is perfectly tagged by a SNP that we used to assess replication. Assuming a recessive effect and parental imputation, we derived the expected additive effect to be ^ bC¼^ bCC q2 q2þ2pq, where ^ bCis the effect we expect to observe under our additive model, ^ bCC is the homozygous effect reported in the original study, qis the C allele frequency, and pis 1q(see Detailed Materials and methods for derivation). iGWAS 58 GWAS on mortality risk factors were used to create Bayesian priors for the SNP effects observed in the CES study, as described in McDaid et al. (2017). Mendelian randomisation was used to estimate causal effects of independent risk factors on lifespan, and these estimates were combined with the risk factor GWAS to calculate priors for each SNP. Priors were multiplied with observed Z statistics and used to generate Bayes factors. Observed Z statistics were then permuted, leading to 7.2 billion null Bayes factors (using the same priors), which were used to assess significance. Sex and age stratified analysis Cox survival models, adjusting for the same covariates as the standard GWAS, were used to test SNP dosage against survival of UK Biobank genomically British fathers and mothers, separately. The analysis was split into age bands, where any parent who died at an age younger than the age band was excluded and any parent who died beyond the age band was treated as alive. Using the R package ‘metafor’, moderator effects of sex and age on hazard ratio could be estimated while taking into account the estimate uncertainty (see Detailed Materials and methods for formula). Causal genes and methylation sites SMR-HEIDI (Zhu et al., 2016) tests were performed on CES statistics to implicate causal genes and methylation sites. Summary-level data from two studies on gene expression in blood (Westra et al., 2013;Lloyd-Jones et al., 2017) and data on gene expression in 48 tissues from the GTEx consortium (Battle et al., 2017) were tested to find causal links between gene expression and lifespan. Similarly, data from a genome-wide methylation study (McRae et al., 2017) was used to find causal links between CpG sites and lifespan. All results from the SMR test passing a 5% FDR threshold where the HEIDI test p>0.05 were reported. Conditional analysis SOJO (Ning et al., 2017) was used to fine-map the genetic signals in 1 Mb regions around lead SNPs reaching genome-wide significance and candidate SNPs reaching nominal significance in our study. The analysis was based on CES statistics from UK Biobank genomically British individuals, using the LifeGen meta-analysis results to optimise the LASSO regression tuning parameters. For Timmers et al. eLife 2019;8:e39856. DOI: https://doi.org/10.7554/eLife.39856 20 of 40 Research Communication Genetics and Genomics each parameter, a polygenic score was built and the proportion of predictable variance from the regional polygenic score in the validation sample was calculated. Disease association analysis The GWAS catalog (MacArthur et al., 2017) and PhenoScanner (Staley et al., 2016) were checked for known genome-wide significant associations with our GWAS hits and proxies (r2 >0.6) in European samples. Associations discovered in UK Biobank by Churchhouse and Neale, 2018 were omitted from the PhenoScanner database as the findings have not been replicated, and the large sample overlap with our own study could result in false positive associations, due to phenotypic correlations between morbidity and mortality. Triallelic SNPs and associations without effect sizes were excluded before near-identical traits were grouped together, discarding all but the strongest association and keeping the shortest trait name. For example, ‘Lung cancer’, ‘Familial lung cancer’, and ‘Small cell lung cancer’ were grouped and renamed to ‘Lung cancer’. The remaining associations were classified into disease categories based on keywords and subsequent manual curation. Lifespan variance explained by disease SNPs The GWAS catalog (MacArthur et al., 2017) was checked for disease associations discovered in European ancestry studies, which were grouped into broad disease categories based on keywords and manual curation (see Figure 7—source data 1 and Detailed Methods). Associations were pruned by distance (500 kb) and LD (r 2 <0.1), keeping the SNP most strongly associated with lifespan in the CES GWAS. Where possible this SNP was tested against diseases in UK Biobank subjects and their family to test for pleiotropy (see Detailed Matrials and methods). Significance of associations with lifespan was determined by setting an FDR threshold that allowed for one false positive among all independent SNPs tested (q 0.022). Lifespan variance explained (LVE) was calculated as 2pqa 2 , where p and q are the frequencies of the effect and reference alleles in our lifespan GWAS, and a is the SNP effect size in years of life (Falconer et al., 1996). Cell type enrichment Stratified LD-score regression (Finucane et al., 2015) was used to test for cell type-specific enrichment in lifespan heritability. As the power of this method depends on SNP heritability, standard LDscore regression (Finucane et al., 2015) was first used to check which of our samples (UK Biobank, LifeGen, or the combined cohort) had the highest SNP heritability. Lifespan summary statistics from UK Biobank genomically British individuals were then analysed using the procedure described in Finucane et al., 2015, and P values were adjusted for multiple testing using the Benjamin-Hochberg procedure. Pathway enrichment VEGAS2 v2.01.17 (Mishra and Macgregor, 2015) was used to calculate gene scores using SNPs genotyped in UK Biobank, based on summary statistics from the full CES cohort and the default software settings. VEGAS2Pathway was then used to check for pathway enrichment using those gene scores and the default list of gene sets (Mishra and MacGregor, 2017). DEPICT (Pers et al., 2015) was also used to map genes to lifespan loci and check for pathway enrichment in the combined cohort CES GWAS. Default analysis was run for regions with genomewide significant (p < 5e-8) variants in the first analysis, and genome-wide suggestive (p < 1e-5) variants in the second analysis, excluding the MHC in both cases. PASCAL (Lamparter et al., 2016) was used with the same summary statistics and gene sets as DEPICT, except the gene probabilities within the sets were dichotomized (Z > 3) as described in Marouli et al., 2017. For each software independently, pathway enrichment was adjusted for multiple testing using the Benjamin-Hochberg procedure. Age-related eQTL enrichment Combined cohort CES lifespan statistics were matched to eQTLs associated with the expression of at least one gene (p < 10 –3 ) in a dataset from the eQTLGen Consortium (31,684 individuals) (Vo ˜sa et al., 2018). Data on age-related expression (Peters et al., 2015) allowed eQTLs to be Timmers et al. eLife 2019;8:e39856. DOI: https://doi.org/10.7554/eLife.39856 21 of 40 Research Communication Genetics and Genomics divided into four categories based on association with age and/or lifespan. Fisher’s exact test was used check if age-related eQTLs were enriched for associations with lifespan. Polygenic score analysis Polygenic risk scores (PRS) for lifespan were calculated for two subsamples of UK Biobank (24,059 Scottish individuals and a random 29,815 English/Welsh individuals), and 36,499 individuals from the Estonian Biobank, using combined cohort CES lifespan summary statistics that excluded these samples. PRSice 2.0.14.beta (Euesden et al., 2015) was used to construct the scores from genotyped SNPs in UK Biobank and imputed data from the Estonian Biobank, pruned by LD (r 2 = 0.1) and distance (250 kb). Polygenic scores were Z standardised. Cox proportional hazard models were used to fit parental survival against polygenic score, adjusted for subject sex; assessment centre; genotyping batch and array; and 10 principal components. Parental hazard ratios were converted into subject years of life as described in the GWAS method section. Logistic regression models were used to fit polygenic score against the same self-reported UK Biobank disease categories used for individual SNPs. Effect estimates of first-degree relatives were doubled to account for imputation of genotypes and then meta-analysed using inverse variance weighting, adjusting for trait correlations between family members. Postulation of equivalent sample size in height GWAS The use of parent imputation, low trait heritability, and incomplete proportion dead all reduce the power to detect effect sizes. The equivalent sample size in a hypothetical, completely heritable trait with otherwise identical genetic architecture would be the original sample size, diluted (i.e. multiplied) by the heritability (0.122) (Kaplanis et al., 2018), the r 2 of offspring genotype on parent genotype (0.250) and the proportion dead (0.602). This gives an equivalent sample size of 18,579 from the 1,012,240 parent lifespans. We then calculated sample size for height that would have the same properties, accounting for the heritability of height (0.8) (Wood et al., 2014): 23,224 (i.e. 18,579/ 0.8). We next calculated the P value that would have been reported by Wood et al’s 250,000 sample size height GWAMA (Wood et al., 2014) for a SNP that was just significant in a hypothetical 23,224 sample height GWAMA: p < 1.8 10 72 . Six distinct loci passed this significance threshold in Wood et al’s results. With 17,893 nonagenarians, Deelen et al. (2014) attained a P value of 2.33 10 26 at rs4420638. With 1.012 m parents we attained a P value of 1.75 10 64 . Other things being equal a nonagenarian sample size of 44,500 thus appears to be equally powered to one million parents. Sensitivity of annuity prices to age Market annuity rates for life annuities in January 2018 written to 55, 60, 65, and 70 year olds were obtained from the sharing pensions website http://www.sharingpensions.com/annuity_rates. htm (accessed 22 January 2018) and were £4158, £4680, £5476, £6075, £7105 respectively per year for a £100,000 purchase price. The arithmetic average increase from one quinquennial age to the next is 14 percent. Data availability 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-universityof-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 Vo ˜sa 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. Timmers et al. eLife 2019;8:e39856. DOI: https://doi.org/10.7554/eLife.39856 22 of 40 Research Communication Genetics and Genomics Details Data sources Our UK Biobank dataset consisted of 409,700 British individuals (determined by genomic PCA) and 48,656 European individuals of self-reported (but not genetic) British, Irish, and other White European descent. Details on genotyping marker and sample QC are described in Bycroft et al., 2017. Subjects completed a questionnaire which included questions on adoption status, parental age, and parental deaths. For our analysis, we excluded individuals who were adopted or otherwise unclear about their adoption status (N = 7,279), individuals who did not report their parental ages (N = 2,995), and individuals both of whose parents died before the age of 40 and which were therefore more likely due to accident or injury (N = 4,472). We further excluded one of each pair from related individuals (third degree or closer; N = 88,354) from every relative pair reported by UK Biobank, leaving 443,610 individuals for the final analysis. Although exclusion of relatives reduces sample size, we were concerned that linear mixed modelling to account for relatedness might not be fully appropriate under the kin-cohort model. Consider the parental phenotypic correlation for two full sibling subjects (r 2 = 1) or the maternal genetic covariance amongst two subjects who are the offspring of two brothers (r 2 = 0): the heritability/GRM implied covariance is incorrect for both cases (although in the sibling case, it may be correct on average). Individuals passing QC reported a total of 339,732 paternal and 351,889 maternal lifespans, ranging from 40 to 107 years of life, that is 691,621 lives in total (Table 1—source data 1). Our second dataset was the publicly available summary statistics from LifeGen, a consortium of 26 population cohorts investigating genomic effects on parental lifespans (Joshi et al., 2017). LifeGen had included results from UK Biobank, but the UK Biobank GWAS data were removed here, giving GWAS summary statistics for 160,461 father and 160,158 mother lifespans in the form of log hazard ratios. Combined, our datasets had 1,012,240 lives. UK Biobank Genome-Wide association study In each separate UK Biobank ethnicity, we carried out association analysis between genotype (MAF > 0.005; HRC imputed SNPs only; ~9 million markers) and parent age and alive/dead status, effectively analysing the effect of genotype in offspring on parent survival, given survival to at least age 40, using Cox Proportional Hazards models. The following model was assumed to hold: h xð Þ¼h0xð ÞebXþg1Z1þ...gkZk (1) Where x is (parent) age, h 0 the baseline hazard and X the offspring genotype (coded 0,1,2), beta the log e (hazard ratio) associated with X and Z 1 -Z k the covariates, with corresponding effect sizes y 1 - y k . The covariates were genotyping batch and array, the first forty principal components of relatedness, as provided by UK Biobank, and subject sex (but not age, as we were analysing parent age). To facilitate practical runtimes, the Martingale residuals of the Cox model were calculated for father and mothers separately and divided by the proportion dead to give estimates of the hazard ratio (Therneau et al., 1990) giving a residual trait suitable for GWAS (for more details of the residual method see Joshi et al., 2017). Effect sizes observed under this model, for a SNP in offspring, are half that of the actual effect size in the parent carrying the variant (Joshi et al., 2017). Reported effect sizes (and their SE) have therefore been doubled to give the effect sizes in carriers themselves, giving an estimate of the log hazard ratios (or often, with sign reversed, log protective ratios). These estimates are suitable for meta-analysis and allow direct comparison with the log hazard ratios from LifeGen. Analysis of association between genotype and survival across both parents was made under two contrasting assumptions and associated models, which had to adjust for the covariance amongst parent traits, preventing conventional unadjusted inverse-variance meta-analysis. Firstly, we assumed that the hazard ratio was the same for both sexes, that is a common effect size across sexes (CES). If there were no correlation amongst parents’ traits, this could have been done by straightforward inverse variance meta-analysis of the single parent results. However, to account for the covariance amongst father and mother lifespans, we calculated a total parent residual, the sum of individual parent residuals, for each subject (i.e. offspring). Under the common effect assumption, the combined trait’s effect size is twice that in the single parent, and the variance of the combined trait, automatically and appropriately reflects the parents’ covariance, amongst the two parents, giving a Timmers et al. eLife 2019;8:e39856. DOI: https://doi.org/10.7554/eLife.39856 23 of 40 Research Communication Genetics and Genomics residual trait suitable for GWAS, with an effect size equal to that in a carrier of the variant, and correct standard error. Secondly, we assumed that, there might be sex-specific effect sizes (SSE) in fathers and mothers. Under the SSE assumption, individual parental GWAS were carried out, and the summary statistics results were meta-analysed using MANOVA, accounting for the correlation amongst the parent traits and the sample overlap (broadly complete), but agnostic as to whether the effect size was similar or different in each parent, giving a P value against the null hypothesis that both effect sizes are zero, but, naturally, no estimate of a single common beta. This procedure was carried out using the R package MultiABEL and summary-level data (Shen et al., 2015). The procedure requires an estimate of the correlation amongst the traits (in this case parent residuals), which was measured directly (r = 0.1). The procedure automatically estimates the variance of the traits from summary level data (Mother residuals s 2 = 6.74; Father residuals s 2 = 5.25) For the LifeGen results, the SSE procedure to combine results was identical to UK Biobank (Mother residuals s 2 = 14.12; Father residuals s 2 = 18.75), except the trait correlation was derived from summary level data instead of measured directly (r = 0.1). This was done by taking the correlations in effect estimates from independent SNPs from the summary statistics of the individual parents, which equals the trait correlation, assuming full sample overlap (which is slightly conservative). Similarly, since we did not have access to individual level (residual) data, it was not possible to carry out a single total parent residual GWAS under the CES assumption. Instead we meta-analysed the single parent effect sizes using inverse variance meta-analysis, but adjusted the standard errors to reflect the correlation amongst the traits (r) as follows: SE ^ b  ¼SE0^ b   ffiffiffiffiffiffiffiffiffiffi 1þr p Where SE0^ b   is the usual (uncorrected) inverse-variance weighted meta-analysis standard error, ignoring the correlation amongst the estimates and SE ^ b  is the corrected estimate used. This is slightly conservative as Variance ^ b  ¼Variance0^ b   1þ2rs1s2 s2 1þs2 2  <¼Variance0^ b   1þrð Þ (2) which follows straight forwardly from ^ b¼P1^ b1þP2^ b2 P1þP2. Where s1and s2are the standard error of the individual estimates and P1,P2their associated precisions (i.e. reciprocal of the variance). Equation (2) is always conservative, but exact if s1¼s2. In practice s1and s2were similar, as the sample sizes, allele frequencies and variance in the traits for the two parents were very similar. As we were using unrelated populations and fitting forty principal components to control for population structure, material inflation of test statistics due to structure or relatedness was not to be expected. This was confirmed using the intercept of LD-score regression (Bulik-Sullivan et al., 2015) for the summary statistics as shown in Table 1—source data 2. We have tried to use a consistent approach to the direction of lifespan altering effects: positive implies longer life, consistent with previous studies of long-livedness (Deelen et al., 2014). Our base measure was thus a protection ratio, directly mirroring the cox hazard ratio. Effect sizes (betas) are typically –log e (cox hazard ratio), which we denote the log e (protection ratio). Years of life gained were estimated as 10 * log protection ratio, in accordance with a long-standing actuarial rule of thumb and recently verified (Joshi et al., 2017). Candidate SNP replication We sought to reproduce and replicate genome-wide significant associations reported by Pilling et al. (2017), who recently published a GWAS on the same UK Biobank data, but using a slightly different method. Rather than excluding relatives, Pilling et al. used BOLT-LMM and the genomic relationship matrix in subjects, to approximately account for covariance amongst parental phenotype. Pilling et al. also analysed parents separately as well as jointly, using a last survivor phenotype. Despite these factors, reproduction (obtaining the same result from almost the same data) was straightforward and consistent, once effect sizes were placed on the same scale (see below and Figure 2—figure supplement 1), confirming our re-scaling was correct. To try to independently replicate their results, we used the consortium, LifeGen, excluding individuals from UK Biobank. Timmers et al. eLife 2019;8:e39856. DOI: https://doi.org/10.7554/eLife.39856 24 of 40 Research Communication Genetics and Genomics Pilling et al. (2017) performed multiple parental survival GWAS in UK Biobank, identifying 14 loci using combined parent lifespan and 11 loci using individual parent lifespan. Their study design involved rank-normalising Martingale residuals before regressing against genotype, which does not give an estimate of the log e (hazard ratio) (lnHR), nor, we believe, another naturally interpretable scale of effects, as the scale is now dependent on the proportion dead. Simulations (not shown) suggested sd »ffiffiffi c pfor some Martingale residual distributions, where sd is the standard deviation of the distribution and c is the proportion dead. As multiplying the untransformed Martingale residual distribution by 1/c gives an estimate of the hazard ratio (Joshi et al., 2017;Therneau et al., 1990), for individual parents, we could convert Pilling et al.’s effect sizes by multiplying them by sqrt(c) to return them to the Martingale residual scale (which still depends on the study structure) and then by 1/c to place them on the lnHR scale, using the proportion dead from Pilling et al.’s study descriptives. Further multiplication by 2 allows conversion from a subject-parent effect to an effect in self. The cumulative scale conversion allowing for all three of these effects was to multiply Pilling et al.’s effect sizes by 2.5863/2.2869 in mothers/fathers, respectively, placing them on a lnHR scale for effects in male/female subjects. The joint life parent phenotype does not appear to have a straightforward conversion to lnHR in self. Instead, we used an empirical estimate derived from effect sizes comparison of the APOE allele between Pilling et al.’s discovery sample and our own UK Biobank Gen. British sample (both parents combined), which were largely overlapping: to get from Pilling et al.’s effect size to log e HR, we had to multiply their effect sizes by 1.9699 for APOE and used this ratio for other alleles, which should be completely valid under the proportional hazard assumption. Whilst this scheme may appear a little ad hoc (the use of simulation and APOE), it was confirmed empirically: visual inspection indeed showed hazard ratios from our own UK Biobank Gen. British sample calculations and inferred hazard ratios from Pilling were highly concordant (noting the concordance for joint life at APOE, which was pre-defined to be perfectly concordant by our procedure, is not, of itself, evidence) (Figure 2—figure supplement 1). Flachsbart et al. (2009),Deelen et al. (2014), and Broer et al. (2015) tested extreme longevity cases (95–110 years, 85 years, 90 years, respectively) against controls (60–75 years, 65 years, deceased at 55–80 years, respectively), identifying SNPs at or near FOXO3 and 5q33.3/EBF1. As done previously (Joshi et al., 2017), we estimated the relationship between study-specific longevity log odds ratio and log hazard ratio empirically using the most powered APOE variant overlapping with our own study, assuming increased odds of surviving to extreme age is due to a reduction in lifetime mortality risk. For Flachsbart et al. and Deelen et al., we used rs4420638_G (reported log OR –0.33 (Deelen et al., 2014), our lnHR 0.086). Inverting the sign to give log e (protection ratio) estimates, the conversion estimate used was 3.82. For Broer et al., we used rs6857_T (reported log OR –0.20 (Broer et al., 2015), our lnHR 0.087), with a conversion estimate of 0.43, again yielding a log e (- protection ratio). Ben-Avraham et al. (2017) reported a deletion in Growth Hormone Receptor exon 3 (d3-GHR) associated with an increase of 10 years in male lifespan when homozygous. This deletion is tagged by rs6873545_C (McKay et al., 2007), which is present in the UK Biobank and LifeGen population sample at a frequency of 26.9% (q). Considering the association is recessive and we are imputing father genotypes, we converted the reported effect size into expected years of life per allele as follows: If the subject genotype is CT, the parent contributing the C allele has 50% chance of being the father and q2 q2þ2pq chance of being homozygous. If the subject genotype is CC, the father has 100% chance of contributing the C allele and again has q2 q2þ2pq chance of being homozygous. We therefore expect the relationship to be ^ bC¼1 2^ bCC q2 q2þ2pq, where ^ bCis the observed effect per subject allele on father lifespan and ^ bCC is the reported effect of the homozygous deletion in the father. As before, doubling the allele effect gives an estimate of the effect of the allele on subject lifespan, which finally yielded a converted estimate of 0.155. That is, under Ben-Avrahim et al.’s assumptions on inheritance patterns, if their estimate of effect size in minor homozygotes is correct, we should see under the additive model an effect size of 0.155 years, or a log e (hazard ratio) of –0.015, and correspondingly scaled standard errors (note we are assuming that the effect is actually recessive, and estimating how that effect should appear if an additive model were fitted). Timmers et al. eLife 2019;8:e39856. DOI: https://doi.org/10.7554/eLife.39856 25 of 40 Research Communication Genetics and Genomics Rue ¨ger: Lausanne University Hospital, Lausanne, Switzerland; A Saha: Department of Computer Science, Johns Hopkins University, Baltimore, United States; M Scholz: Institut fu ¨r Medizinische InformatiK, Statistik und Epidemiologie, LIFE – Leipzig Research Center for Civilization Diseases, Universita ¨t Leipzig, Leipzig, Germany; K Schramm: Institute of Genetic Epidemiology, Helmholtz Zentrum Mu ¨nchen, German Research Center for Environmental Health, Neuherberg, Germany; I Seppa ¨la ¨: Department of Clinical Chemistry, Fimlab Laboratories and Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland; M Stumvoll: Department of Medicine, Universita ¨t Leipzig, Leipzig, Germany; P Sullivan: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; A Teumer: Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany; J Thiery: Institute for Laboratory Medicine, LIFE – Leipzig Research Center for Civilization Diseases, Universita ¨t Leipzig, Leipzig, Germany; L Tong: Department of Public Health Sciences, University of Chicago, Chicago, United States; A To ¨njes: Department of Medicine, Universita ¨t Leipzig, Leipzig, Germany; J Verlouw: Department of Internal Medicine, Erasmus Medical Centre, Rotterdam, The Netherlands; PM Visscher: Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia; U Vo ˜sa: Department of Genetics, University Medical Centre Groningen, Groningen, The Netherlands; U Vo ¨lker: Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany; H Yaghootkar: Exeter Medical School, University of Exeter, Exeter, United Kingdom; J Yang: Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia; B Zeng: School of Biological Sciences, Georgia Institute of Technology, Atlanta, United States; F Zhang: Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia; M Agbessi: Ontario Institute for Cancer Research, Toronto, Canada; H Ahsan: Department of Public Health Sciences, University of Chicago, Chicago, United States; I Alves: Ontario Institute for Cancer Research, Toronto, Canada; A Andiappan: Singapore Immunology Network, Agency for Science, Technology and Research, Singapore, Singapore; P Awadalla: Ontario Institute for Cancer Research, Toronto, Canada; A Battle: Department of Computer Science, Johns Hopkins University, Baltimore, United States; MJ Bonder: Department of Genetics University, Medical Centre Groningen, Groningen, The Netherlands; D Boomsma: Vrije Universiteit, Amsterdam, The Netherlands; M Christiansen: Cardiovascular Health Research Unit, University of Washington, Seattle, United States; A Claringbould: Department of Genetics University, Medical Centre Groningen, Groningen, The Netherlands; P Deelen: Department of Genetics University, Medical Centre Groningen, Groningen, The Netherlands; J van Dongen: Vrije Universiteit, Amsterdam, The Netherlands; T Esko: Estonian Genome Center, University of Tartu, Tartu, Estonia; M Fave ´: Ontario Institute for Cancer Research, Toronto, Canada; L Franke: Department of Genetics University, Medical Centre Groningen, Groningen, The Netherlands; T Frayling: Exeter Medical School, University of Exeter, Exeter, United Kingdom; SA Gharib: Department of Medicine, University of Washington, Seattle, United States; G Gibson: School of Biological Sciences, Georgia Institute of Technology, Atlanta, United States; G Hemani: MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom; R Jansen: Vrije Universiteit, Amsterdam, The Netherlands; A Kalnapenkis: Estonian Genome Center, University of Tartu, Tartu, Estonia; S Kasela: Estonian Genome Center, University of Tartu, Tartu, Estonia; J Kettunen: University of Helsinki, Helsinki, Finland; Y Kim: Department of Computer Science, Johns Hopkins University, Baltimore, United States; H Kirsten: Institut fu ¨r Medizinische Informatik, Statistik und Epidemiologie, LIFE – Leipzig ResearchCenter for Civilization Diseases, Universita ¨t Leipzig, Leipzig, Germany; P Kovacs: IFB Adiposity Diseases, Universita ¨t Leipzig, Leipzig, Germany; K Krohn: Interdisciplinary Center for Clinical Research, Faculty of Medicine, Universita ¨t Leipzig, Leipzig, Germany; J Kronberg-Guzman: Estonian Genome Center, University of Tartu, Tartu, Estonia; V Kukushkina: Estonian Genome Center, University of Tartu, Tartu, Estonia; Z Kutalik: Lausanne University Hospital, Lausanne, Switzerland; M Ka ¨ho ¨nen: Department of Clinical Physiology and Faculty of Medicine and Life Sciences, Tampere University Hospital and University of Tampere, Tampere, Finland; B Lee: Singapore Immunology Network, Agency for Science, Technology and Research, Singapore, Singapore; T Lehtima ¨ki: Department of Clinical Chemistry, Fimlab Laboratories and Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland; M Loeffler: Institut fu ¨r Medizinische Informatik, Statistik und Epidemiologie, LIFE – Leipzig Research Center for Civilization Diseases, Universita ¨t Leipzig, Leipzig, Germany; U Marigorta: School of Biological Sciences, Georgia Institute of Technology, Atlanta, United States; A Metspalu: Estonian Genome Center, University of Tartu, Tartu, Estonia; J van Meurs: Department of Internal Timmers et al. eLife 2019;8:e39856. DOI: https://doi.org/10.7554/eLife.39856 32 of 40 Research Communication Genetics and Genomics Medicine, Erasmus Medical Centre, Rotterdam, The Netherlands; L Milani: Estonian Genome Center, University of Tartu, Tartu, Estonia; M Mu¨ller-Nurasyid: Institute of Genetic Epidemiology, Helmholtz Zentrum Mu ¨nchen, German Research Center for Environmental Health, Neuherberg, Germany; M Nauck: Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany; M Nivard: Vrije Universiteit, Amsterdam, The Netherlands; B Penninx: Vrije Universiteit, Amsterdam, The Netherlands; M Perola: National Institute for Health and Welfare, University of Helsinki, Helsinki, Finland; N Pervjakova: Estonian Genome Center, University of Tartu, Tartu, Estonia; B Pierce: Department of Public Health Sciences, University of Chicago, Chicago, United States; J Powell: Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia; H Prokisch: Institute of Human Genetics, Helmholtz Zentrum Mu ¨nchen, Mu ¨nchen, Germany; BM Psaty: Departments of Epidemiology, Medicine, and Health Services, Cardiovascular Health Research Unit, University of Washington, Seattle, United States; O Raitakari: Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital and University of Turku, Turku, Finland; S Ring: School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom; S Ripatti: University of Helsinki, Helsinki, Finland; O Rotzschke: Singapore Immunology Network, Agency for Science, Technology and Research, Singapore, Singapore; S Rue ¨ger: Lausanne University Hospital, Lausanne, Switzerland; A Saha: Department of Computer Science, Johns Hopkins University, Baltimore, United States; M Scholz: Institut fu ¨r Medizinische InformatiK, Statistik und Epidemiologie, LIFE – Leipzig Research Center for Civilization Diseases, Universita ¨t Leipzig, Leipzig, Germany; K Schramm: Institute of Genetic Epidemiology, Helmholtz Zentrum Mu ¨nchen, German Research Center for Environmental Health, Neuherberg, Germany; I Seppa ¨la ¨: Department of Clinical Chemistry, Fimlab Laboratories and Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland; M Stumvoll: Department of Medicine, Universita ¨t Leipzig, Leipzig, Germany; P Sullivan: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; A Teumer: Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany; J Thiery: Institute for Laboratory Medicine, LIFE – Leipzig Research Center for Civilization Diseases, Universita ¨t Leipzig, Leipzig, Germany; L Tong: Department of Public Health Sciences, University of Chicago, Chicago, United States; A To ¨njes: Department of Medicine, Universita ¨t Leipzig, Leipzig, Germany; J Verlouw: Department of Internal Medicine, Erasmus Medical Centre, Rotterdam, The Netherlands; PM Visscher: Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia; U Vo ˜sa: Department of Genetics, University Medical Centre Groningen, Groningen, The Netherlands; U Vo ¨lker: Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany; H Yaghootkar: Exeter Medical School, University of Exeter, Exeter, United Kingdom; J Yang: Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia; B Zeng: School of Biological Sciences, Georgia Institute of Technology, Atlanta, United States; F Zhang: Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia Competing interests To ˜nu Esko: Reviewing Editor, eLife. The other authors declare that no competing interests exist. Funding Funder Grant reference number Author Medical Research Council DTP in Precision Medicine MR/N013166/1,HGU QTL in health and disease Paul RHJ Timmers Andrew D Bretherick David W ClarkeQTLGen ConsortiumJames F Wilson Estonian Research Competency Council PUT 1665 Kristi Lall Krista Fischer Wellcome Trust PhD Training Fellowship for Clinicians Andrew D Bretherick Edinburgh Clinical Academic Track 204979/Z/16/Z Andrew D Bretherick Svenska Forskningsra˚det Formas 2014-00371 Xia Shen Timmers et al. eLife 2019;8:e39856. DOI: https://doi.org/10.7554/eLife.39856 33 of 40 Research Communication Genetics and Genomics Svenska Forskningsra˚det Formas 2017-02543 Xia Shen Schweizerischer Nationalfonds zur Fo ¨rderung der Wissenschaftlichen Forschung 31003A_169929 Zolta ´n Kutalik SystemsX.ch 51RTP0_151019 Zolta ´n Kutalik AXA Research Fund Peter K Joshi The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. Author contributions Paul RHJ Timmers, Ninon Mounier, Formal analysis, Investigation, Visualization, Writing—original draft; Kristi Lall, Formal analysis, Investigation, Writing—original draft; Krista Fischer, Xia Shen, Conceptualization, Formal analysis, Supervision, Investigation, Visualization, Methodology, Writing— original draft; Zheng Ning, Xiao Feng, Formal analysis, Visualization, Writing—review and editing; Andrew D Bretherick, David W Clark, Software, Formal analysis, Writing—review and editing; To ˜nu Esko, Resources, Supervision, Funding acquisition, Writing—original draft; Zolta´n Kutalik, Conceptualization, Resources, Software, Investigation, Methodology, Writing—original draft; James F Wilson, Conceptualization, Resources, Supervision, Funding acquisition, Writing—original draft; Peter K Joshi, Conceptualization, Supervision, Validation, Methodology, Writing—original draft, Writing— review and editing Author ORCIDs Paul RHJ Timmers http://orcid.org/0000-0002-5197-1267 Xia Shen http://orcid.org/0000-0003-4390-1979 To ˜nu Esko http://orcid.org/0000-0003-1982-6569 James F Wilson http://orcid.org/0000-0001-5751-9178 Peter K Joshi http://orcid.org/0000-0002-6361-5059 Ethics Human subjects: This work used existing datasets, for which ethical approval had been gathered for genetic investigation at the time of collection. Decision letter and Author response Decision letter https://doi.org/10.7554/eLife.39856.047 Author response https://doi.org/10.7554/eLife.39856.048 Additional files Supplementary files .Supplementary file 1. Loci with significantly predicted candidate genes using SMR-HEIDI test and two eQTL datasets (blood tissue). This PDF contains a table and plots of the lifespan GWAS and eQTL signals genes from Westra and CAGE eQTL studies that pass FDR < 5% threshold for the SMR test and p>0.05 threshold for HEIDI test. DOI: https://doi.org/10.7554/eLife.39856.035 .Supplementary file 2. Predicted causal elements by SMR-HEIDI using expression and methylation QTL data. This Excel workbook contains two sheets. The first sheet lists the genes and tissues prioritised by SMR-HEIDI using GTEx expression data, while the second sheet lists the methylation sites prioritised using mQTL data. DOI: https://doi.org/10.7554/eLife.39856.036 .Supplementary file 3. Evidence of allelic heterogeneity of the lifespan loci via identification of secondary associations using SOJO. This Excel workbook contains two sheets. The first sheet is a summary of the number of additional variants identified by SOJO per locus. The second sheet is a Timmers et al. eLife 2019;8:e39856. DOI: https://doi.org/10.7554/eLife.39856 34 of 40 Research Communication Genetics and Genomics detailed list of all variants identified by SOJO per locus, their allele frequencies, and their independent effects. DOI: https://doi.org/10.7554/eLife.39856.037 .Supplementary file 4. Cell types enriched for lifespan heritability identified by stratified LD-score regression. This Excel workbook contains a list of all cell types tested for lifespan heritability enrichment by Stratified LD-score regression and includes a list of coefficients for each cel type. DOI: https://doi.org/10.7554/eLife.39856.038 .Supplementary file 5. Gene sets highlighted by VEGAS2Pathway and corresponding results from DEPICT and PASCAL (FDR < 5%). This Excel workbook contains two sheets. The first sheet lists the gene sets highlighted by VEGAS2Pathway as enriched for lifespan genes at FDR 5%, with corresponding results for DEPICT and PASCAL enrichment analyses. The second sheet lists all VEGAS gene sets significant at FDR < 15%, with a list of genes included in each set. DOI: https://doi.org/10.7554/eLife.39856.039 .Supplementary file 6. eQTL SNPs associated with lifespan for genes whose expression varies with age. This PDF contains a table and figure on the age-related gene expression analysis. DOI: https://doi.org/10.7554/eLife.39856.040 .Transparent reporting form DOI: https://doi.org/10.7554/eLife.39856.041 Data availability 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-universityof-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 Vo ˜sa, U. et al. Unraveling the polygenic architecture of complex traits using blood eQTL meta-analysis. bioRxiv 447367 (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. Source data files for main figures and tables are included as supplementary material, other than GWAS summary statistics. The following dataset was generated: Author(s) Year Dataset title Dataset URL Database and Identifier Timmers PRHJ, La ¨ll K, Fischer K, Ning Z, Feng X, Bretherick A, Clark DW, Shen X, Xia Esko T, Kutalik Z, Wilson JF, Joshi PK 2018 Genomic underpinnings of lifespan allow prediction and reveal basis in modern risks http://dx.doi.org/10. 7488/ds/2463 Edinburgh DataShare, 10.7488/ ds/2463 The following previously published dataset was used: Author(s) Year Dataset title Dataset URL Database and Identifier GTEx Consortium 2017 Single tissue gene expression data used in the SMR-Heidi analysis https://gtexportal.org/ home/datasets Genotype-Tissue Expression, GTEx_ Analysis_v7_eQTL.tar. gz Timmers et al. eLife 2019;8:e39856. DOI: https://doi.org/10.7554/eLife.39856 35 of 40 Research Communication Genetics and Genomics References Association of British Insurers and UK Government. 2014. Concordat and Moratorium on Genetics and Insurance: ABI. Battle A, Brown CD, Engelhardt BE, Montgomery SB, GTEx ConsortiumLaboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working GroupStatistical Methods groups—Analysis Working GroupEnhancing GTEx (eGTEx) groupsNIH Common FundNIH/NCINIH/NHGRINIH/NIMHNIH/NIDABiospecimen Collection Source Site—NDRIBiospecimen Collection Source Site—RPCIBiospecimen Core Resource—VARIBrain Bank Repository—University of Miami Brain Endowment BankLeidos Biomedical—Project ManagementELSI StudyGenome Browser Data Integration &Visualization—EBIGenome Browser Data Integration &Visualization— UCSC Genomics Institute, University of California Santa CruzLead analysts:Laboratory, Data Analysis &Coordinating Center (LDACC):NIH program management:Biospecimen collection:Pathology:eQTL manuscript working group:, Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group, Statistical Methods groups—Analysis Working Group, Enhancing GTEx (eGTEx) groups, NIH Common Fund, NIH/NCI, NIH/NHGRI, NIH/NIMH, NIH/NIDA, Biospecimen Collection Source Site—NDRI, Biospecimen Collection Source Site—RPCI, Biospecimen Core Resource—VARI, Brain Bank Repository—University of Miami Brain Endowment Bank, Leidos Biomedical—Project Management, ELSI Study, Genome Browser Data Integration &Visualization—EBI, Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz, Lead analysts:, Laboratory, Data Analysis &Coordinating Center (LDACC):, NIH program management:, Biospecimen collection:, Pathology:, eQTL manuscript working group:.2017. Genetic effects on gene expression across human tissues. Nature 550:204. DOI: https://doi.org/10.1038/nature24277,PMID: 2 9022597 Beekman M, Blanche´ H, Perola M, Hervonen A, Bezrukov V, Sikora E, Flachsbart F, Christiansen L, De Craen AJ, Kirkwood TB, Rea IM, Poulain M, Robine JM, Valensin S, Stazi MA, Passarino G, Deiana L, Gonos ES, Paternoster L, Sørensen TI, et al. 2013. Genome-wide linkage analysis for human longevity: genetics of healthy aging study. Aging Cell 12:184–193. DOI: https://doi.org/10.1111/acel.12039,PMID: 23286790 Ben-Avraham D, Govindaraju DR, Budagov T, Fradin D, Durda P, Liu B, Ott S, Gutman D, Sharvit L, Kaplan R, Bougne` res P, Reiner A, Shuldiner AR, Cohen P, Barzilai N, Atzmon G. 2017. The GH receptor exon 3 deletion is a marker of male-specific exceptional longevity associated with increased GH sensitivity and taller stature. Science Advances 3:e1602025. DOI: https://doi.org/10.1126/sciadv.1602025,PMID: 28630896 Broer L, Buchman AS, Deelen J, Evans DS, Faul JD, Lunetta KL, Sebastiani P, Smith JA, Smith AV, Tanaka T, Yu L, Arnold AM, Aspelund T, Benjamin EJ, De Jager PL, Eirkisdottir G, Evans DA, Garcia ME, Hofman A, Kaplan RC, et al. 2015. GWAS of longevity in CHARGE consortium confirms APOE and FOXO3 candidacy. The Journals of Gerontology: Series A 70:110–118. DOI: https://doi.org/10.1093/gerona/glu166 Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J, Patterson N, Daly MJ, Price AL, Neale BM, Schizophrenia Working Group of the Psychiatric Genomics Consortium. 2015. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nature Genetics 47:291–295. DOI: https:// doi.org/10.1038/ng.3211,PMID: 25642630 Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, Motyer A, Vukcevic D, Delaneau O, O’Connell J, Cortes A, Welsh S, McVean G, Leslie S, Donnelly P, Marchini J. 2017. Genome-wide genetic data on ~500,000 UK biobank participants. bioRxiv.DOI: https://doi.org/10.1101/166298 Churchhouse C, Neale B. 2018. Rapid GWAS of thousands of phenotypes for 337,000 samples in the UK biobank. http://www.nealelab.is/blog/2017/7/19/rapid-gwas-of-thousands-of-phenotypes-for-337000-samplesin-the-uk-biobank [Accessed November 2, 2018]. Cooper JD, Smyth DJ, Smiles AM, Plagnol V, Walker NM, Allen JE, Downes K, Barrett JC, Healy BC, Mychaleckyj JC, Warram JH, Todd JA. 2008. Meta-analysis of genome-wide association study data identifies additional type 1 diabetes risk loci. Nature Genetics 40:1399–1401. DOI: https://doi.org/10.1038/ng.249,PMID: 18978792 Cox DR. 1972. Regression models and Life-Tables. Journal of the Royal Statistical Society: Series B 34:187–202. D’Agostino RB, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, Kannel WB. 2008. General cardiovascular risk profile for use in primary care: the framingham heart study. Circulation 117:743–753. DOI: https://doi.org/10.1161/CIRCULATIONAHA.107.699579,PMID: 18212285 Deelen J, Beekman M, Uh HW, Helmer Q, Kuningas M, Christiansen L, Kremer D, van der Breggen R, Suchiman HE, Lakenberg N, van den Akker EB, Passtoors WM, Tiemeier H, van Heemst D, de Craen AJ, Rivadeneira F, de Geus EJ, Perola M, van der Ouderaa FJ, Gunn DA, et al. 2011. Genome-wide association study identifies a single major locus contributing to survival into old age; the APOE locus revisited. Aging Cell 10:686–698. DOI: https://doi.org/10.1111/j.1474-9726.2011.00705.x,PMID: 21418511 Deelen J, Beekman M, Capri M, Franceschi C, Slagboom PE. 2013. Identifying the genomic determinants of aging and longevity in human population studies: progress and challenges. BioEssays 35:386–396. DOI: https:// doi.org/10.1002/bies.201200148,PMID: 23423909 Deelen J, Beekman M, Uh HW, Broer L, Ayers KL, Tan Q, Kamatani Y, Bennet AM, Tamm R, Trompet S, Guðbjartsson DF, Flachsbart F, Rose G, Viktorin A, Fischer K, Nygaard M, Cordell HJ, Crocco P, van den Akker EB, Bo ¨hringer S, et al. 2014. Genome-wide association meta-analysis of human longevity identifies a novel locus conferring survival beyond 90 years of age. Human Molecular Genetics 23:4420–4432. DOI: https://doi.org/10. 1093/hmg/ddu139,PMID: 24688116 Timmers et al. eLife 2019;8:e39856. DOI: https://doi.org/10.7554/eLife.39856 36 of 40 Research Communication Genetics and Genomics Donlon TA, Morris BJ, Chen R, Masaki KH, Allsopp RC, Willcox DC, Elliott A, Willcox BJ. 2017. FOXO3 longevity interactome on chromosome 6. Aging Cell 16:1016–1025. DOI: https://doi.org/10.1111/acel.12625,PMID: 2 8722347 Euesden J, Lewis CM, O’Reilly PF. 2015. PRSice: polygenic risk score software. Bioinformatics 31:1466–1468. DOI: https://doi.org/10.1093/bioinformatics/btu848,PMID: 25550326 Falconer DS, Mackay TFC, Frankham R. 1996. Trends in genetics : TIG. In: Introduction to Quantitative Genetics. 12 Cell Press. Finucane HK, Bulik-Sullivan B, Gusev A, Trynka G, Reshef Y, Loh PR, Anttila V, Xu H, Zang C, Farh K, Ripke S, Day FR, Purcell S, Stahl E, Lindstrom S, Perry JR, Okada Y, Raychaudhuri S, Daly MJ, Patterson N, et al. 2015. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nature Genetics 47:1228–1235. DOI: https://doi.org/10.1038/ng.3404,PMID: 26414678 Flachsbart F, Caliebe A, Kleindorp R, Blanche´ H, von Eller-Eberstein H, Nikolaus S, Schreiber S, Nebel A. 2009. Association of FOXO3A variation with human longevity confirmed in german centenarians. Proceedings of the National Academy of Sciences 106:2700–2705. DOI: https://doi.org/10.1073/pnas.0809594106,PMID: 19196 970 Flachsbart F, Dose J, Gentschew L, Geismann C, Caliebe A, Knecht C, Nygaard M, Badarinarayan N, ElSharawy A, May S, Luzius A, Torres GG, Jentzsch M, Forster M, Ha ¨sler R, Pallauf K, Lieb W, Derbois C, Galan P, Drichel D, et al. 2017. Identification and characterization of two functional variants in the human longevity gene FOXO3. Nature Communications 8:2063. DOI: https://doi.org/10.1038/s41467-017-02183-y,PMID: 29234056 Grossi V, Forte G, Sanese P, Peserico A, Tezil T, Lepore Signorile M, Fasano C, Lovaglio R, Bagnulo R, Loconte DC, Susca FC, Resta N, Simone C. 2018. The longevity SNP rs2802292 uncovered: hsf1 activates stressdependent expression of FOXO3 through an intronic enhancer. Nucleic Acids Research 46:5587–5600. DOI: https://doi.org/10.1093/nar/gky331,PMID: 29733381 Haller T, Kals M, Esko T, Ma ¨gi R, Fischer K. 2015. RegScan: a GWAS tool for quick estimation of allele effects on continuous traits and their combinations. Briefings in Bioinformatics 16:39–44. DOI: https://doi.org/10.1093/ bib/bbt066,PMID: 24008273 Hayashi T, Ko JH, Strafella AP, Dagher A. 2013. Dorsolateral prefrontal and orbitofrontal cortex interactions during self-control of cigarette craving. Proceedings of the National Academy of Sciences 110:4422–4427. DOI: https://doi.org/10.1073/pnas.1212185110,PMID: 23359677 Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, Laurin C, Burgess S, Bowden J, Langdon R, Tan VY, Yarmolinsky J, Shihab HA, Timpson NJ, Evans DM, Relton C, Martin RM, Davey Smith G, Gaunt TR, Haycock PC. 2018. The MR-Base platform supports systematic causal inference across the human phenome. eLife 7:e34408. DOI: https://doi.org/10.7554/eLife.34408,PMID: 29846171 Herskind AM, McGue M, Holm NV, Sørensen TI, Harvald B, Vaupel JW. 1996. The heritability of human longevity: a population-based study of 2872 danish twin pairs born 1870-1900. Human Genetics 97:319–323. DOI: https://doi.org/10.1007/BF02185763,PMID: 8786073 Hwangbo DS, Gershman B, Gersham B, Tu MP, Palmer M, Tatar M. 2004. Drosophila dFOXO controls lifespan and regulates insulin signalling in brain and fat body. Nature 429:562–566. DOI: https://doi.org/10.1038/ nature02549,PMID: 15175753 Joshi PK, Fischer K, Schraut KE, Campbell H, Esko T, Wilson JF. 2016. Variants near CHRNA3/5 and APOE have ageand sex-related effects on human lifespan. Nature Communications 7:11174. DOI: https://doi.org/10. 1038/ncomms11174,PMID: 27029810 Joshi PK, Pirastu N, Kentistou KA, Fischer K, Hofer E, Schraut KE, Clark DW, Nutile T, Barnes CLK, Timmers P, Shen X, Gandin I, McDaid AF, Hansen TF, Gordon SD, Giulianini F, Boutin TS, Abdellaoui A, Zhao W, MedinaGomez C, et al. 2017. Genome-wide meta-analysis associates HLA-DQA1/DRB1 and LPA and lifestyle factors with human longevity. Nature Communications 8:910. DOI: https://doi.org/10.1038/s41467-017-00934-5, PMID: 29030599 Kaplanis J, Gordon A, Wahl M, Gershovits M, Markus B, Sheikh M, Gymrek M, Bhatia G, MarArthur DG, Price A, Erlich Y. 2018. Quantitative analysis of population-scale family trees with millions of relatives. bioRxiv. DOI: https://doi.org/10.1101/106427 Kenyon C, Chang J, Gensch E, Rudner A, Tabtiang R. 1993. A C. elegans mutant that lives twice as long as wild type. Nature 366:461–464. DOI: https://doi.org/10.1038/366461a0,PMID: 8247153 Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, Natarajan P, Lander ES, Lubitz SA, Ellinor PT, Kathiresan S. 2018. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nature Genetics 50:1219–1224. DOI: https://doi.org/10.1038/s41588-0180183-z,PMID: 30104762 Lamparter D, Marbach D, Rueedi R, Kutalik Z, Bergmann S. 2016. Fast and rigorous computation of gene and pathway scores from SNP-Based summary statistics. PLOS Computational Biology 12:e1004714. DOI: https:// doi.org/10.1371/journal.pcbi.1004714,PMID: 26808494 Legal General Group PLC. 2017. Interim Management Report: Legal and General Group. Leitsalu L, Haller T, Esko T, Tammesoo ML, Alavere H, Snieder H, Perola M, Ng PC, Ma ¨gi R, Milani L, Fischer K, Metspalu A. 2015. Cohort profile: estonian biobank of the estonian genome center, university of tartu. International Journal of Epidemiology 44:1137–1147. DOI: https://doi.org/10.1093/ije/dyt268,PMID: 24518929 Lin DY, Sullivan PF. 2009. Meta-analysis of genome-wide association studies with overlapping subjects. The American Journal of Human Genetics 85:862–872. DOI: https://doi.org/10.1016/j.ajhg.2009.11.001, PMID: 20004761 Timmers et al. eLife 2019;8:e39856. DOI: https://doi.org/10.7554/eLife.39856 37 of 40 Research Communication Genetics and Genomics Livingstone SJ, Levin D, Looker HC, Lindsay RS, Wild SH, Joss N, Leese G, Leslie P, McCrimmon RJ, Metcalfe W, McKnight JA, Morris AD, Pearson DW, Petrie JR, Philip S, Sattar NA, Traynor JP, Colhoun HM, Scottish Diabetes Research Network epidemiology group, Scottish Renal Registry. 2015. Estimated life expectancy in a scottish cohort with type 1 diabetes, 2008-2010. Jama 313:37–44. DOI: https://doi.org/10.1001/jama.2014. 16425,PMID: 25562264 Ljungquist B, Berg S, Lanke J, McClearn GE, Pedersen NL. 1998. The effect of genetic factors for longevity: a comparison of identical and fraternal twins in the swedish twin registry. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 53A:M441–M446. DOI: https://doi.org/10.1093/gerona/53A.6.M441 Lloyd-Jones LR, Holloway A, McRae A, Yang J, Small K, Zhao J, Zeng B, Bakshi A, Metspalu A, Dermitzakis M, Gibson G, Spector T, Montgomery G, Esko T, Visscher PM, Powell JE. 2017. The genetic architecture of gene expression in peripheral blood. The American Journal of Human Genetics 100:371. DOI: https://doi.org/10. 1016/j.ajhg.2017.01.026,PMID: 28157541 Loh PR, Tucker G, Bulik-Sullivan BK, Vilhja´lmsson BJ, Finucane HK, Salem RM, Chasman DI, Ridker PM, Neale BM, Berger B, Patterson N, Price AL. 2015. Efficient bayesian mixed-model analysis increases association power in large cohorts. Nature Genetics 47:284–290. DOI: https://doi.org/10.1038/ng.3190,PMID: 25642633 Lowe CJ, Hall PA, Staines WR. 2014. The effects of continuous theta burst stimulation to the left dorsolateral prefrontal cortex on executive function, food cravings, and snack food consumption. Psychosomatic Medicine 76:503–511. DOI: https://doi.org/10.1097/PSY.0000000000000090,PMID: 25215552 MacArthur J, Bowler E, Cerezo M, Gil L, Hall P, Hastings E, Junkins H, McMahon A, Milano A, Morales J, Pendlington ZM, Welter D, Burdett T, Hindorff L, Flicek P, Cunningham F, Parkinson H. 2017. The new NHGRIEBI catalog of published genome-wide association studies (GWAS catalog). Nucleic Acids Research 45:D896– D901. DOI: https://doi.org/10.1093/nar/gkw1133,PMID: 27899670 Maciejewski H. 2014. Gene set analysis methods: statistical models and methodological differences. Briefings in Bioinformatics 15:504–518. DOI: https://doi.org/10.1093/bib/bbt002,PMID: 23413432 Marouli E, Graff M, Medina-Gomez C, Lo KS, Wood AR, Kjaer TR, Fine RS, Lu Y, Schurmann C, Highland HM, Ru ¨eger S, Thorleifsson G, Justice AE, Lamparter D, Stirrups KE, Turcot V, Young KL, Winkler TW, Esko T, Karaderi T, et al. 2017. Rare and low-frequency coding variants alter human adult height. Nature 542:186–190. DOI: https://doi.org/10.1038/nature21039,PMID: 28146470 Mathers C, Stevens GA, Mahanani WR, Fat DM, Hogan D. 2018. Global Health Estimates 2016: Deaths by Cause Age, Sex, by Country and by Region: WHO. McDaid AF, Joshi PK, Porcu E, Komljenovic A, Li H, Sorrentino V, Litovchenko M, Bevers RPJ, Ru ¨eger S, Reymond A, Bochud M, Deplancke B, Williams RW, Robinson-Rechavi M, Paccaud F, Rousson V, Auwerx J, Wilson JF, Kutalik Z. 2017. Bayesian association scan reveals loci associated with human lifespan and linked biomarkers. Nature Communications 8:15842. DOI: https://doi.org/10.1038/ncomms15842,PMID: 28748955 McGue M, Vaupel JW, Holm N, Harvald B. 1993. Longevity is moderately heritable in a sample of danish twins born 1870-1880. Journal of Gerontology 48:B237–B244. DOI: https://doi.org/10.1093/geronj/48.6.B237, PMID: 8227991 McKay JD, Kaaks R, Johansson M, Biessy C, Wiklund F, Balter K, Adami H-O, Boillot C, Gioia-Patricola L, Canzian F, Stattin P, Gronberg H. 2007. Haplotype-Based analysis of common variation in the growth hormone receptor gene and prostate cancer risk. Cancer Epidemiology Biomarkers & Prevention 16:169–173. DOI: https://doi. org/10.1158/1055-9965.EPI-06-0320 McRae A, Marioni RE, Shah S, Yang J, Powell JE, Harris SE, Gibson J, Henders AK, Bowdler L, Painter JN, Murphy L, Martin NG, Starr JM, Wray NR, Deary IJ, Visscher PM, Montgomery GW. 2017. Identification of 55, 000 replicated DNA methylation QTL. bioRxiv.DOI: https://doi.org/10.1101/166710 Mishra A, Macgregor S. 2015. VEGAS2: software for more flexible Gene-Based testing. Twin Research and Human Genetics 18:86–91. DOI: https://doi.org/10.1017/thg.2014.79,PMID: 25518859 Mishra A, MacGregor S. 2017. A novel approach for pathway analysis of GWAS data highlights role of BMP signaling and muscle cell differentiation in colorectal cancer susceptibility. Twin Research and Human Genetics 20:1–9. DOI: https://doi.org/10.1017/thg.2016.100,PMID: 28105966 Morrison JH, Baxter MG. 2012. The ageing cortical synapse: hallmarks and implications for cognitive decline. Nature Reviews Neuroscience 13:240–250. DOI: https://doi.org/10.1038/nrn3200,PMID: 22395804 Myllykangas-Luosuja ¨rvi R, Aho K, Kautiainen H, Isoma ¨ki H. 1995. Shortening of life span and causes of excess mortality in a population-based series of subjects with rheumatoid arthritis. Clinical and Experimental Rheumatology 13:149–153. PMID: 7656460 Ning Z, Lee Y, Joshi PK, Wilson JF, Pawitan Y, Shen X. 2017. A selection operator for summary association statistics reveals allelic heterogeneity of complex traits. The American Journal of Human Genetics 101:903–912. DOI: https://doi.org/10.1016/j.ajhg.2017.09.027,PMID: 29198721 Pers TH, Karjalainen JM, Chan Y, Westra HJ, Wood AR, Yang J, Lui JC, Vedantam S, Gustafsson S, Esko T, Frayling T, Speliotes EK, Boehnke M, Raychaudhuri S, Fehrmann RS, Hirschhorn JN, Franke L, Genetic Investigation of ANthropometric Traits (GIANT) Consortium. 2015. Biological interpretation of genome-wide association studies using predicted gene functions. Nature Communications 6:5890. DOI: https://doi.org/10. 1038/ncomms6890,PMID: 25597830 Peters MJ, Joehanes R, Pilling LC, Schurmann C, Conneely KN, Powell J, Reinmaa E, Sutphin GL, Zhernakova A, Schramm K, Wilson YA, Kobes S, Tukiainen T, Ramos YF, Go ¨ring HH, Fornage M, Liu Y, Gharib SA, Stranger BE, De Jager PL, et al. 2015. The transcriptional landscape of age in human peripheral blood. Nature Communications 6:8570. DOI: https://doi.org/10.1038/ncomms9570,PMID: 26490707 Timmers et al. eLife 2019;8:e39856. DOI: https://doi.org/10.7554/eLife.39856 38 of 40 Research Communication Genetics and Genomics Pilling LC, Atkins JL, Bowman K, Jones SE, Tyrrell J, Beaumont RN, Ruth KS, Tuke MA, Yaghootkar H, Wood AR, Freathy RM, Murray A, Weedon MN, Xue L, Lunetta K, Murabito JM, Harries LW, Robine JM, Brayne C, Kuchel GA, et al. 2016. Human longevity is influenced by many genetic variants: evidence from 75,000 UK biobank participants. Aging 8:547–560. DOI: https://doi.org/10.18632/aging.100930,PMID: 27015805 Pilling LC, Kuo CL, Sicinski K, Tamosauskaite J, Kuchel GA, Harries LW, Herd P, Wallace R, Ferrucci L, Melzer D. 2017. Human longevity: 25 genetic loci associated in 389,166 UK biobank participants. Aging 9:2504–2520. DOI: https://doi.org/10.18632/aging.101334,PMID: 29227965 Raychaudhuri S, Remmers EF, Lee AT, Hackett R, Guiducci C, Burtt NP, Gianniny L, Korman BD, Padyukov L, Kurreeman FA, Chang M, Catanese JJ, Ding B, Wong S, van der Helm-van Mil AH, Neale BM, Coblyn J, Cui J, Tak PP, Wolbink GJ, et al. 2008. Common variants at CD40 and other loci confer risk of rheumatoid arthritis. Nature Genetics 40:1216–1223. DOI: https://doi.org/10.1038/ng.233,PMID: 18794853 Ruby JG, Wright KM, Rand KA, Kermany A, Noto K, Curtis D, Varner N, Garrigan D, Slinkov D, Dorfman I, Granka JM, Byrnes J, Myres N, Ball C. 2018. Estimates of the heritability of human longevity are substantially inflated due to assortative mating. Genetics 210:1109–1124. DOI: https://doi.org/10.1534/genetics.118. 301613,PMID: 30401766 Sebastiani P, Solovieff N, Dewan AT, Walsh KM, Puca A, Hartley SW, Melista E, Andersen S, Dworkis DA, Wilk JB, Myers RH, Steinberg MH, Montano M, Baldwin CT, Hoh J, Perls TT. 2012. Genetic signatures of exceptional longevity in humans. PLoS ONE 7:e29848. DOI: https://doi.org/10.1371/journal.pone.0029848,PMID: 2227954 8 Sebastiani P, Sun FX, Andersen SL, Lee JH, Wojczynski MK, Sanders JL, Yashin A, Newman AB, Perls TT. 2013. Families enriched for exceptional longevity also have increased Health-Span: findings from the long life family study. Frontiers in Public Health 1:38. DOI: https://doi.org/10.3389/fpubh.2013.00038,PMID: 24350207 Sebastiani P, Nussbaum L, Andersen SL, Black MJ, Perls TT. 2016. Increasing sibling relative risk of survival to older and older ages and the importance of precise definitions of “Aging,” “Life Span,” and “Longevity”. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 71:340–346. DOI: https://doi.org/ 10.1093/gerona/glv020 Sebastiani P, Gurinovich A, Bae H, Andersen S, Malovini A, Atzmon G, Villa F, Kraja AT, Ben-Avraham D, Barzilai N, Puca A, Perls TT. 2017. Four Genome-Wide association studies identify new extreme longevity variants. The Journals of Gerontology: Series A 72:1453–1464. DOI: https://doi.org/10.1093/gerona/glx027 Shen X, Wang X, Ning Z, Tsepilov Y, Shirali M, Smith BH, Hocking LJ, Padmanabhan S, Hayward C, Porteous DJ, Pawitan Y, Haley CS, Aulchenko YS, Generation Scotland. 2015. Simple multi-trait analysis identifies novel loci associated with growth and obesity measures. bioRXiv.DOI: https://doi.org/10.1101/022269 Staley JR, Blackshaw J, Kamat MA, Ellis S, Surendran P, Sun BB, Paul DS, Freitag D, Burgess S, Danesh J, Young R, Butterworth AS. 2016. PhenoScanner: a database of human genotype-phenotype associations. Bioinformatics 32:3207–3209. DOI: https://doi.org/10.1093/bioinformatics/btw373,PMID: 27318201 Therneau TM, Grambsch PM, Fleming TR. 1990. Martingale-based residuals for survival models. Biometrika 77: 147–160. DOI: https://doi.org/10.1093/biomet/77.1.147 van den Berg N, Beekman M, Smith KR, Janssens A, Slagboom PE. 2017. Historical demography and longevity genetics: back to the future. Ageing Research Reviews 38:28–39. DOI: https://doi.org/10.1016/j.arr.2017.06. 005,PMID: 28689042 Vo ˜sa U, Claringbould A, Westra HJ, Bonder MJ, Deelen P, Zeng B, Kirsten H, Saha A, Kreuzhuber R, Kasela S, Pervjakova N, Alvaes I, Fave MJ, Agbessi M, Christiansen M, Jansen R, Seppa ¨la ¨I, Tong L, Teumer A, Schramm K, et al. 2018. Unraveling the polygenic architecture of complex traits using blood eQTL meta-analysis. bioRxiv. DOI: https://doi.org/10.1101/447367 Wacholder S, Hartge P, Struewing JP, Pee D, McAdams M, Brody L, Tucker M. 1998. The kin-cohort study for estimating penetrance. American Journal of Epidemiology 148:623–630. DOI: https://doi.org/10.1093/aje/148. 7.623,PMID: 9778168 Westra HJ, Peters MJ, Esko T, Yaghootkar H, Schurmann C, Kettunen J, Christiansen MW, Fairfax BP, Schramm K, Powell JE, Zhernakova A, Zhernakova DV, Veldink JH, Van den Berg LH, Karjalainen J, Withoff S, Uitterlinden AG, Hofman A, Rivadeneira F, Hoen PAC’, et al. 2013. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nature Genetics 45:1238–1243. DOI: https://doi.org/10.1038/ng.2756, PMID: 24013639 Williams GC. 1957. Pleiotropy, natural selection, and the evolution of senescence. Evolution 11:398–411. Wood AR, Esko T, Yang J, Vedantam S, Pers TH, Gustafsson S, Chu AY, Estrada K, Luan J, Kutalik Z, Amin N, Buchkovich ML, Croteau-Chonka DC, Day FR, Duan Y, Fall T, Fehrmann R, Ferreira T, Jackson AU, Karjalainen J, et al. 2014. Defining the role of common variation in the genomic and biological architecture of adult human height. Nature Genetics 46:1173–1186. DOI: https://doi.org/10.1038/ng.3097,PMID: 25282103 Young AI, Frigge ML, Gudbjartsson DF, Thorleifsson G, Bjornsdottir G, Sulem P, Masson G, Thorsteinsdottir U, Stefansson K, Kong A. 2018. Relatedness disequilibrium regression estimates heritability without environmental bias. Nature Genetics 50:1304–1310. DOI: https://doi.org/10.1038/s41588-018-0178-9,PMID: 30104764 Zarrouk A, Vejux A, Mackrill J, O’Callaghan Y, Hammami M, O’Brien N, Lizard G. 2014. Involvement of oxysterols in age-related diseases and ageing processes. Ageing Research Reviews 18:148–162. DOI: https:// doi.org/10.1016/j.arr.2014.09.006,PMID: 25305550 Zeng Y, Nie C, Min J, Liu X, Li M, Chen H, Xu H, Wang M, Ni T, Li Y, Yan H, Zhang JP, Song C, Chi LQ, Wang HM, Dong J, Zheng GY, Lin L, Qian F, Qi Y, et al. 2016. Novel loci and pathways significantly associated with longevity. Scientific Reports 6:21243. DOI: https://doi.org/10.1038/srep21243,PMID: 26912274 Timmers et al. eLife 2019;8:e39856. DOI: https://doi.org/10.7554/eLife.39856 39 of 40 Research Communication Genetics and Genomics Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, Montgomery GW, Goddard ME, Wray NR, Visscher PM, Yang J. 2016. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nature Genetics 48:481–487. DOI: https://doi.org/10.1038/ng.3538,PMID: 27019110 Timmers et al. eLife 2019;8:e39856. DOI: https://doi.org/10.7554/eLife.39856 40 of 40 Research Communication Genetics and Genomics