A meta-analysis of genome-wide association studies identifies multiple longevity genes

ARTICLE A meta-analysis of genome-wide association studies identifies multiple longevity genes Joris Deelen et al. # Human longevity is heritable, but genome-wide association (GWA) studies have had limited success. Here, we perform two meta-analyses of GWA studies of a rigorous longevity phenotype definition including 11,262/3484 cases surviving at or beyond the age corresponding to the 90th/99th survival percentile, respectively, and 25,483 controls whose age at death or at last contact was at or below the age corresponding to the 60th survival percentile. Consistent with previous reports, rs429358 (apolipoprotein E (ApoE) ε4) is associated with lower odds of surviving to the 90th and 99th percentile age, while rs7412 (ApoE ε2) shows the opposite. Moreover, rs7676745, located near GPR78, associates with lower odds of surviving to the 90th percentile age. Gene-level association analysis reveals a role for tissue-specific expression of multiple genes in longevity. Finally, genetic correlation of the longevity GWA results with that of several disease-related phenotypes points to a shared genetic architecture between health and longevity. https://doi.org/10.1038/s41467-019-11558-2 OPEN Correspondence and requests for materials should be addressed to J.D. (email: [email protected]) or to D.S.E. (email: [email protected]) or to P.E.S. (email: [email protected]) or to J.M.M. (email: [email protected]). # A full list of authors and their affiliations appears at the end of the paper. NATURE COMMUNICATIONS | (2019) 10: 3669 | https://doi.org/10.1038/s41467-019-11558-2 | www.nature.com/naturecommunications 1 1234567890():,; The average human life expectancy has been increasing for centuries1. Based on twin studies, the heritability of human lifespan has been estimated to be ~25%, although this estimate differs among studies2. On the other hand, the heritability of lifespan based on the correlation of the mid-parent (i.e., the average of the father and mother) and offspring difference between age at death and expected lifespan was estimated to be 12%3. A recent study has indicated that the different heritability estimates may be inflated due to assortative mating, leaving a true heritability that is below 10%4. The heritability of lifespan, estimated using the sibling relative risk, increases with age5and is assumed to be enriched in long-lived families, particularly when belonging to the 10% longest-lived of their generation6. To identify genetic associations with human lifespan, several genome-wide association (GWA) studies have been performed7–20. These studies have used a discrete (i.e., older cases versus younger controls) or a continuous phenotype (such as age at death of individuals or their parents). The selection of cases for the studies using a discrete longevity phenotype has been based on the survival to ages above 90 or 100 years or belonging to the top 10% or 1% of survivors in a population. Studies defining cases using a discrete longevity phenotype often need to rely on controls from more contemporary birth cohorts, because all others from the case birth cohorts have died before sample collection. Previous GWA studies have identified several genetic variants, but the only locus that has shown genome-wide significance (P≤5×10 −8) in multiple independent meta-analyses of GWA studies is apolipoprotein E (APOE)21, where the ApoE ε4 variant is associated with lower odds of being a long-lived case. The lack of replication for many reported associations with longevity could be due, at least partly, to the use of different definitions for cases and controls between studies. Furthermore, even within a study, the use of a single age cut-off phenotype for men and women and for individuals belonging to different birth cohorts will give rise to heterogeneity, as survival probabilities differ by sex and birth cohort22, and genetic effects are known to be ageand birth cohort-specific5,23. In an attempt to mitigate the effects of heterogeneous case and control groups, we use country-, sexand birth cohort-specific life tables to identify ages that correspond to different survival percentiles to define cases and controls in our meta-analyses of GWA studies of longevity. Furthermore, most studies in our meta-analyses use controls from the same study population as the cases, which limits the impact of sampling biases that could confound associations. The current meta-analyses include individuals from 20 cohorts from populations of European, East Asian, or African American descent. Two sets of cases are examined: individuals surviving at or beyond the age corresponding to the 90th survival percentile (90th percentile cases) or the 99th survival percentile (99th percentile cases) based on life tables specific to the country where each cohort was based, sex, and birth cohort (i.e., birth year). The same country-, sex-, and birth cohort-specific life tables are used to define the age threshold for controls, corresponding to the 60th percentile of survival. We identify two genome-wide significant loci, of which one is replicated in two independent European cohorts that use de novo genotyping. We also perform a genelevel association analysis based on tissue-specific gene expression and identify additional longevity genes. In addition, using linkage disequilibrium (LD) score regression24, we show that longevity is genetically correlated with multiple diseases and traits. Results Genome-wide association meta-analyses. We performed two meta-analyses in individuals of European ancestry combining cohort-specific genome-wide association data generated using 1000 Genomes imputation: (1) 90th percentile cases versus all controls and (2) 99th percentile cases versus all controls. The numbers of cases and controls in each study are shown in Table 1. For both case definitions, multiple genetic variants at the wellreplicated APOE locus reached genome-wide significance (P≤ 5×10 −8) (Table 2, Fig. 1and Supplementary Fig. 1). Consistent with previous reports, rs429358 (ApoE ε4) was associated with lower odds of surviving to the 90th or 99th percentile age at the genome-wide significance level. In addition, we report a genomewide significant association of rs7412 (ApoE ε2) with higher odds of surviving to the 90th and the 99th percentile age. Conditional analysis in two of the cohorts with individuals of European ancestry, CEPH and LLS (combined with GEHA Dutch) (representing 18% of the 90th percentile cases and 6% of all controls), indicated that the signal at the APOE locus was explained by these two independent variants, i.e., rs429358 (ApoE ε4) and rs7412 (ApoE ε2). There was no evidence of heterogeneity of effect across cohorts for ApoE ε2(P-value for heterogeneity (P het )= 0.619, Table 2). For ApoE ε4, on the other hand, there was evidence of heterogeneity (P het =0.004, Table 2), although the direction of effect of this variant was consistent across cohorts (Fig. 2). Besides ApoE ε4 and ε2, one additional variant, rs7676745, located on chromosome 4 near GPR78, showed a genome-wide significant association in the 90th percentile cases versus all controls analysis (P=4.3 × 10−8, Table 2). The rare allele of this variant (A) was associated with lower odds of surviving to the 90th percentile age and there was no evidence of heterogeneity of effect across cohorts (P het =0.462, Table 2). The regional association and forest plots for this locus are depicted in Figs. 1and 2. Most of the variants reported in Table 2show stronger effects in the 99th percentile as compared to the 90th percentile analysis (Supplementary Fig. 2), indicating that the use of a more extreme phenotype results in stronger effects. Replication. The effects of ApoE ε4 and ε2 were replicated in the two cohorts (i.e., DKLSII and GLS) in which de novo genotyping, using predesigned Taqman SNP Genotyping Assays, was applied (Table 2). However, we were not able to replicate the effect of rs7676745 in these cohorts, since there was no Taqman SNP Genotyping Assay available for this variant. Validation in parental age-based data sets. Given that all available studies with genome-wide genetic data that met our inclusion criteria were included in our genome-wide association meta-analyses, we additionally set out to validate our findings in two UK Biobank parental longevity data sets (Table 1) and the parental lifespan data set recently created by Timmers and colleagues20. Since the genotyped individuals in the UK Biobank were recruited at relatively young ages (40–69 years), these data sets were based on the age reached by the parents of the study participants. Hence, the phenotypes used for validation were different from those used in our meta-analyses, resulting in smaller effect sizes. Moreover, the reference panels used to impute the genetic variants (a merged panel of UK10K, 1000G Phase 3, and Haplotype Reference Consortium (HRC) for parental longevity and HRC alone for parental lifespan)20 were different from the one used in our meta-analyses (1000G Phase 1), which could have influenced the outcome of the analyses. Of the variants that showed a P-value ≤1×10 −6in our meta-analyses (Table 2), only ApoE ε4 and ε2 were significantly associated with both parental longevity and lifespan (P < 0.05) in these data sets (Table 3). Moreover, the rare allele (A) of the second most significant ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-11558-2 2NATURE COMMUNICATIONS | (2019) 10:3669 | https://doi.org/10.1038/s41467-019-11558-2 | www.nature.com/naturecommunications Table 1 Samples included in the different genome-wide association meta-analyses or the replication and validation Study Ancestry 90th percentile cases 99th percentile cases All controls Dead controls Discovery 100-plus/LASA/ADC European 373 301 2271 245 AGES European 300 1001 466 CEPHaEuropean 1234 1112 831 CHS European 905 68 558 539 DKLSaEuropean 960 610 1917 FHS European 332 1444 539 GEHA DanishaEuropean 451 127 900 GEHA French European 271 81 358 GEHA Italy European 182 184 HRS European 361 3312 657 LLFS European 1110 339 552 82 LLS +GEHA Dutch European 1037 377 712 Longevity European 548 271 584 MrOS European 1171 82 386 320 Newcastle 85 +aEuropean 215 5159 RS European 774 79 2965 1731 SOF European 812 37 354 300 Vitality 90 +aEuropean 226 1995 Total 11,262 3484 25,483 4879 Replication DKLSIIaEuropean 944 298 772 GLS European 1613 1613 4215 Total 2557 1911 4987 Validation UK Biobank European 19,742 928 19,698 Trans-ethnic CLHLS East Asian 2178 2178 2299 CHS African American 177 211 Total 13,617 5662 27,993 100-plus 100-plus Study, LASA Longitudinal aging study of Amsterdam, ADC Amsterdam dementia cohort, AGES Age/Gene Environment Susceptibility Study, CEPH CEPH centenarian cohort, CHS Cardiovascular Health Study, DKLS Danish longevity study, FHS Framingham Heart Study, GEHA Genetics of Healthy Aging Study, HRS Health and Retirement Study, LLFS Long Life Family Study, LLS Leiden Longevity Study, Longevity Longevity Gene Project, MrOS Osteoporotic Fractures in Men Study, Newcastle 85 +Newcastle 85 +Study, RS Rotterdam study, SOF Study of Osteoporotic Fracture, Vitality 90 +Vitality 90 +project, GLS German longevity study, CLHLS Chinese Longitudinal Healthy Longevity Survey aFor these studies, controls were provided by a separate cohort. Further details of the cohorts are provided in Supplementary Data 4 Table 2 Results of the European genome-wide association meta-analyses and replication in the de novo genotyped cohorts rsID Chr:Position Candidate/ closest gene Alleles (EA/ OA) EAF OR 95% CI PI2(%) P het 90th percentile cases versus all controls (Discovery) rs116362179 2:53,380,757 −T/C 0.05 1.34 1.20–1.50 4.9 × 10−70 0.457 rs7676745a4:8,565,547 GPR78 A/G 0.04 0.67 0.57–0.77 4.3 × 10−80 0.462 rs7754015 6:127,206,068 −G/T 0.43 0.90 0.86–0.94 6.8 × 10−70 0.670 rs35262860 8:55,478,909 RP1 GCT/G 0.39 1.11 1.07–1.15 3.9 × 10−70 0.941 rs3138136 12:56,117,570 RDH5 T/C 0.10 0.83 0.77–0.89 5.4 × 10−714.5 0.284 rs429358 19:45,411,941 APOE C/T 0.13 0.60 0.56–0.64 1.3 × 10−56 54.3 0.004 rs7412 19:45,412,079 APOE T/C 0.09 1.28 1.19–1.37 2.4 × 10−11 0 0.619 90th percentile cases versus all controls (Replication) rs429358 19:45,411,941 APOE C/T 0.45 0.40–0.51 5.2 × 10−36 85.4 0.009 rs7412 19:45,412,079 APOE T/C 1.32 1.18–1.48 2.4 × 10−616.6 0.274 99th percentile cases versus all controls (Discovery) rs3830412 3:124,397,321 KALRN A/AT 0.22 1.21 1.12–1.30 4.3 × 10−70 0.767 rs138762279 5:173,710,197 −AT/A 0.16 0.79 0.72–0.86 1.2 × 10−70 0.769 rs62502826 8:28,982,295 KIF13B A/G 0.15 1.23 1.13–1.33 5.6 × 10−714.9 0.298 rs7039467 9:22,056,213 CDKN2A/B A/G 0.48 1.20 1.12–1.28 1.1 × 10−70 0.843 rs429358 19:45,411,941 APOE C/T 0.13 0.52 0.47–0.58 3.9 × 10−34 0 0.833 rs7412 19:45,412,079 APOE T/C 0.09 1.47 1.32–1.64 3.2 × 10−12 0 0.639 99th percentile cases versus all controls (Replication) rs429358 19:45,411,941 APOE C/T 0.44 0.38–0.50 4.0 × 10−32 84.0 0.012 rs7412 19:45,412,079 APOE T/C 1.35 1.19–1.53 2.0 × 10−60 0.534 EA effect allele, OA other allele, EAF effect allele frequency, OR odds ratio (i.e., odds to become long-lived when carrying the effect allele); 95% CI 95% confidence interval, I2heterogeneity statistic, P het P-value for heterogeneity aWe were not able to replicate the effect of this genetic variant, since there was no Taqman SNP Genotyping Assay available. We only report the most significant genetic variant for the loci with at least one variant with a P-value ≤1×10 –6. The rsID is based on dbSNP build 150. The Chr:Position is based on Genome Reference Consortium Human Build 37 (GRCh37) NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-11558-2 ARTICLE NATURE COMMUNICATIONS | (2019) 10:3669 | https://doi.org/10.1038/s41467-019-11558-2 | www.nature.com/naturecommunications 3 variant at the CDKN2A/B locus, rs2184061, was associated with increased parental lifespan (P=8.4 × 10−6), but not with parental longevity (P=0.329). However, we had adequate power to validate all of our identified variants, even when the effect sizes were halved in the parental longevity data sets. Trans-ethnic meta-analyses. We subsequently performed two trans-ethnic meta-analyses (90th and 99th percentile cases versus all controls) to see if the increase in sample size would lead to identification of additional longevity loci. In this analysis we included individuals of European (all previously used data sets), East Asian (CLHLS), and African American (CHS) ancestry. However, with the exception of APOE and rs2069837, located in IL6, which has previously been associated with longevity in CLHLS9, this analysis did not identify additional genome-wide significant loci (Table 4, Fig. 3and Supplementary Fig. 3). The observed association of the genetic variant in IL6 in the transethnic meta-analyses was mainly driven by the association in the East Asian population. The other variant previously associated with longevity in CLHLS9, rs2440012, located in ANKRD20A9P, did not pass quality control in the large majority of the included cohorts from populations of European descent and was thus not analysed in the trans-ethnic meta-analyses. Comparison of control definitions. To examine the impact of the definition of controls, we performed a sensitivity analysis in which we compared the results of the meta-analysis using the same case definition (90th percentile) with (1) all controls and (2) dead controls only. For this analysis, only cohorts that contributed results using both control definitions were considered (i.e., 100-plus/LASA/ADC, AGES, CHS, FHS, HRS, LLFS, MrOS, RS, and SOF). The results of the two meta-analyses with different control groups were very similar (Supplementary Fig. 4). Among the three loci with at least one genetic variant with a P-value ≤ 1×10 −6in either meta-analysis (and analysed in the same cohorts in both meta-analyses), the most significant variants had odds ratios (ORs) that differed by <1% (Supplementary Table 1). Replication of previously identified loci for human lifespan.To determine the association of previously identified loci for human lifespan and longevity, we performed a look-up of the reported genetic variants within these loci in our meta-analyses data sets. The only previously identified loci that contained variants that showed a significant (P< 7.8 × 10−4, i.e., Bonferroni adjusted for the number of tested loci (n=64)) and directionally consistent associations in our study were FOXO3 and CDKN2A/B (Supplementary Data 1). As depicted in Supplementary Fig. 5, the ab cd –log10 (p) –log10 (p-value) –log10 (p-value) 14 12 10 8 6 4 2 0 60 10 8 6 4 2 0 50 40 30 20 10 0 123456 789 Chromosome 11 13 15 45.35 8.5 8.55 8.6 8.65 rs4293358 rs7676745 rs7412 0.8 r2 0.6 0.4 0.2 0.8 r2 0.6 0.4 0.2 45.4 45.45 Position on chr19 (Mb) Position on chr4 (Mb) 45.5 18 21 123456789 Chromosome 11 13 15 18 21 14 –log10 (p) 12 10 8 6 4 2 0 Fig. 1 Results of the European genome-wide association meta-analyses. Manhattan plot presenting the –log 10 P-values from the European genome-wide association meta-analysis of the 90th percentile cases versus all controls (a) and 99th percentile cases versus all controls (b). The red line indicates the threshold for genome-wide significance (P≤5×10 −8), while the blue line indicates the threshold for genetic variants that showed a suggestive significant association (P≤1×10 −6). The variants that are reported in Table 2are highlighted in green. For representation purposes, the maximum of the y-axis was set to 14. Regional association plot for the APOE (c) and GPR78 (d) loci based on the results from the 90th percentile cases versus all controls metaanalysis. The colour of the variants is based on the linkage disequilibrium with rs429358 (ApoE ε4) (c) or rs7676745 (d) ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-11558-2 4NATURE COMMUNICATIONS | (2019) 10:3669 | https://doi.org/10.1038/s41467-019-11558-2 | www.nature.com/naturecommunications effects of the most frequently reported variants within these loci (i.e., rs2802292 and rs1556516) fluctuate between cohorts and there seems to be no correlation with the genetic background of the included populations. However, for the reported variants within both loci, the odds of surviving to the 99th percentile age is higher than the odds of surviving to the 90th percentile age, indicating they likely affect both early and late-life mortality. Several of the loci that have been associated with increased parental lifespan in the most recent and largest meta-analysis of GWA studies for this phenotype (i.e., KCNK3,HTT,LPA, ATXN2/BRAP, and LDLR)20 contain genetic variants that show a nominal significant association (P< 0.05) with higher odds of surviving to the 90th and/or 99th percentile age. Since the phenotypes used in our study (i.e., cases surviving at or beyond the age corresponding to the 90th/99th survival percentile) were different from the one used in the previous study (i.e., parental lifespan), we performed an additional look-up of these variants in one of the UK Biobank data sets we created for validation of our findings (i.e., the 90th percentile cases versus all controls data set). With the exception of the variant in HTT, all variants showed a nominal significant association in this data set (Supplementary Table 2), indicating that the lack of significant replication of these loci in our discovery phase data set is not likely to be due to a difference in the used phenotype. Study ab cStudy rs7676745 Odds ratio 95% Cl ApoE e4 Odds ratio 95% Cl ApoE e2 Odds ratio 95% ClStudy European (discovery) European (discovery) 100-plus/LASA/ADC AGES CEPH CHS DKLS FHS GEHA Danish GEHA French GEHA Italy HRS LLS + GEHA Dutch Longevity MrOS Newcastle 85+ RS SOF Vitality90+ 100-plus/LASA/ADC AGES CEPH CHS FHS GEHA Danish HRS MrOS RS SOF Vitality90+ LLFS DKLS II GLS CLHLS CHS 0.25 0.1 0.2 0.5 1 2 5 10 0.5 1 2 4 0.25 0.5 1 2 4 European (replication) East Asian African American European (discovery) 100-plus/LASA/ADC AGES CEPH CHS DKLS FHS* GEHA Danish GEHA French GEHA Italy HRS LLS + GEHA Dutch LLFS Longevity MrOS Newcastle 85+ RS SOF Vitality90+ DKLS II GLS CLHLS CHS European (replication) East Asian African American [0.40; 0.67] [0.59; 1.06] [0.34; 0.57] [0.57; 0.93] [0.48; 0.68] [0.29; 0.67] [0.44; 0.71] [0.39; 0.92] [0.28; 0.98] [0.59; 0.93] [0.36; 0.60] [0.41; 0.74] [0.48; 0.78] [0.64; 1.08] [0.59; 0.85] [0.35; 0.63] [0.41; 0.72] [0.46; 0.70] [0.34; 0.47] [0.58; 0.81] [0.45; 1.05] 0.52 [1.14; 1.84] [0.83; 1.84] [1.11; 1.77] [0.84; 1.47] [0.99; 1.55] [0.75; 1.79] [0.83; 1.58] [0.86; 2.09] [0.65; 1.78] [0.98; 1.74] [1.12; 1.95] [1.08; 1.88] [1.08; 1.96] [0.75; 1.36] [0.78; 1.62] [0.91; 1.38] [1.24; 2.57] [0.67; 1.78] [0.94; 1.49] [1.20; 1.57] [1.05; 1.40] [0.83; 1.86] 1.45 1.24 1.40 1.11 1.24 1.16 1.14 1.34 1.08 1.31 1.48 1.43 1.45 1.01 1.13 1.12 1.79 1.09 1.19 1.37 1.21 1.24 [0.24; 0.79] [0.27; 1.25] [0.58; 1.36] [0.39; 1.31] [0.16; 0.66] [0.29; 1.21] [0.48; 1.01] [0.47; 0.89] [0.60; 1.66] [0.38; 0.92] [0.40; 1.35] [0.35; 1.22] 0.43 0.58 0.89 0.71 0.32 0.60 0.70 0.65 1.00 0.59 0.73 0.66 0.80 0.44 0.73 0.57 0.44 0.56 0.60 0.52 0.74 0.46 0.55 0.61 0.83 0.71 0.47 0.55 0.57 0.40 0.69 0.69 Fig. 2 Study-specific results for the genetic variants in APOE and GPR78. Forest plots for the ApoE ε4(a) and ε2(b) variants and rs7676745 (c) based on the results from the 90th percentile versus all controls analysis. The size of the boxes represents the sample size of the cohort. We had no data available for ApoE ε4 in LLFS and for rs7676745 in DKLS, GEHA Italy, GEHA Danish, LLS (combined with GEHA Dutch), Longevity, and Newcastle 85 +. The data for ApoE ε2 in FHS was based on imputation using the Haplotype Reference Consortium reference panel due to the low-imputation quality of this variant when using the 1000 Genomes reference panel NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-11558-2 ARTICLE NATURE COMMUNICATIONS | (2019) 10:3669 | https://doi.org/10.1038/s41467-019-11558-2 | www.nature.com/naturecommunications 5 Gene-level association analysis. In addition to genetic variant associations, GWA studies can also be used to identify gene-level associations by integrating results from expression quantitative trait locus (eQTL) studies that relate variants to gene expression. In order to identify gene-level associations, we used MetaXcan, an analytic approach that uses tissue-specific eQTL results from the GTEx project to estimate gene-level associations with the trait examined from summary-level GWA study results25. Tissuespecific genetically predicted expression of 14 genes (ANKRD31, BLOC1S1,KANSL1,CRHR1,ARL17A,LRRC37A2,ERCC1,RELB, DMPK,CD3EAP,PVRL2,GEMIN7,BLOC1S3, and APOC2) was significantly associated with survival to the 90th and/or 99th percentile age after adjustment for multiple testing (Table 5). Eight of these genes (ERCC1,RELB,DMPK,CD3EAP,PVRL2, GEMIN7,BLOC1S3, and APOC2) are located near the APOE gene, raising the likely possibility that these associations reflected the influence of variants in this well-established longevity-associated locus. The remaining genes are located on chromosome 5, 12, and 17. As depicted in Supplementary Data 2, distinct sets of genetic variants were used by MetaXcan for all significant tissuespecific gene expression associations with survival to the 90th and/or 99th percentile age. Genetic correlation analyses. LD score regression was performed to determine the genetic correlation between the different case definitions used for our meta-analyses (based on the results from the European cohorts only), and between longevity and other traits and diseases24. The genetic correlation (rg) between the 90th and 99th percentile analysis, using all controls for both groups, was 1.01 (SE =0.06, P=3.9 × 10−66). Using LD Hub26, which performs automated LD score regression, we subsequently estimated the genetic correlation of our phenotypes with 246 diseases and traits available in their database. We found a significant genetic correlation of our phenotypes with the father’s age at death phenotype from the UK Biobank. The most significant (negative) genetic correlation of both our phenotypes was with coronary artery disease (CAD) (rg (SE) =−0.40 (0.07) and rg (SE) =−0.29 (0.07), respectively) and several traits involved in type 2 diabetes (T2D) also showed a significant association with one or both phenotypes after Bonferroni adjustment for multiple testing (Table 6and Supplementary Data 3). Discussion We brought together studies from all over the world to perform GWA study meta-analyses in over 13,000 long-lived individuals of diverse ethnic background, including European, East Asian and African American ancestry, to characterise the genetic architecture of human longevity. We used the 1000 Genomes reference panel for imputation to expand the coverage of the genome in comparison to previous GWA studies of longevity. Consistent with previous reports, rs429358, defining ApoE ε4, was associated with decreased odds of becoming long-lived. Moreover, we report a genome-wide significant association of rs7412, defining ApoE ε2, with increased odds of becoming long-lived. We additionally found a genome-wide significant association of a locus near GPR78. Gene-level association analysis revealed association of increased KANSL1,CRHR1,ARL17A, and LRRC37A2 expression and decreased ANKRD31 and BLOC1S1 expression with increased odds of becoming long-lived. Genetic correlation analysis showed that our longevity phenotypes are genetically correlated with father’s age at death, CAD and T2D-related phenotypes. Table 3 Results of the validation in the UK Biobank parental age-based data sets rsID Chr:Position Candidate/ closest gene Alleles (EA/OA) EAF OR 95% CI P 90th percentile cases versus all controls (Parental longevity) rs116362179 2:53,380,757 −T/C 0.04 1.01 0.94–1.08 0.775 rs7676745 4:8,565,547 GPR78 A/G 0.04 0.98 0.92–1.06 0.667 rs7754015 6:127,206,068 −G/T 0.43 1.00 0.97–1.03 0.832 rs35262860 8:55,478,909 RP1 GCT/G 0.39 0.97 0.94–0.99 0.021 rs3138136 12:56,117,570 RDH5 T/C 0.11 1.00 0.95–1.04 0.863 rs429358 19:45,411,941 APOE C/T 0.16 0.85 0.81–0.88 1.1 × 10−16 rs7412 19:45,412,079 APOE T/C 0.08 1.12 1.06–1.18 2.2 × 10−5 90th percentile cases versus all controls (Parental lifespan) rs116362179 2:53,380,757 −T/C 0.04 1.00 0.98–1.02 0.697 rs7676745 4:8,565,547 GPR78 A/G 0.05 1.01 0.99–1.03 0.247 rs3138136 12:56,117,570 RDH5 T/C 0.11 0.99 0.98–1.00 0.135 rs429358 19:45,411,941 APOE C/T 0.15 0.90 0.89–0.91 3.1 × 10−83 rs7412 19:45,412,079 APOE T/C 0.08 1.06 1.05–1.08 7.6 × 10−17 99th percentile cases versus all controls (Parental longevity) rs3830412 3:124,397,321 KALRN A/AT 0.20 1.11 0.99–1.24 0.081 rs138762279 5:173,710,197 −AT/A 0.34 1.05 0.95–1.17 0.299 rs62502826 8:28,982,295 KIF13B A/G 0.14 1.04 0.90–1.19 0.614 rs7039467 9:22,056,213 CDKN2A/B A/G 0.69 0.93 0.83–1.05 0.245 rs2184061 9:22,061,562 CDKN2A/B A/C 0.40 0.95 0.87–1.05 0.329 rs429358 19:45,411,941 APOE C/T 0.16 0.76 0.66–0.87 9.6 × 10−5 rs7412 19:45,412,079 APOE T/C 0.08 1.23 1.05–1.45 0.011 99th percentile cases versus all controls (Parental lifespan) rs62502826 8:28,982,295 KIF13B A/G 0.14 1.00 0.99–1.02 0.376 rs2184061 9:22,061,562 CDKN2A/B A/C 0.40 1.02 1.01–1.03 8.4 × 10−6 rs429358 19:45,411,941 APOE C/T 0.15 0.90 0.89–0.91 3.1 × 10−84 rs7412 19:45,412,079 APOE T/C 0.08 1.06 1.05–1.08 7.6 × 10−17 For the CDKN2A/B locus we have also reported the second most significant variant in this locus (rs2184061), since the allele frequency of the most significant variant (rs7039467) is not comparable between the meta-analyses and UK Biobank data sets due to difference in the reference panel used for imputation. The rsID is based on dbSNP build 150. The Chr:Position is based on Genome Reference Consortium Human Build 37 (GRCh37) EA effect allele, OA other allele, EAF effect allele frequency, OR odds ratio (i.e., odds of parent(s) to become long-lived when carrying the effect allele), 95% CI 95% confidence interval ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-11558-2 6NATURE COMMUNICATIONS | (2019) 10:3669 | https://doi.org/10.1038/s41467-019-11558-2 | www.nature.com/naturecommunications Genetic variation in APOE is well known to be associated with longevity and lifespan, with the first report more than two decades ago in a small candidate gene study27. Since then, there have been numerous candidate gene studies, including individuals of diverse ancestry, which have identified associations of ApoE with longevity28–32. However, thus far, rs7412, the ApoE ε2-defining, genetic variant has not been reported to show a genome-wide significant association in GWA studies of longevity and lifespan. This could be due to the fact that we performed imputation using the 1000 Genomes reference panel, while earlier GWA studies used the HapMap reference panel, which has limited coverage of this variant. ApoE mediates cholesterol metabolism in peripheral tissues and is the principal cholesterol carrier in the brain. The ApoE ε2 and ε4 variants have previously been associated with a decreased (ε2) or increased (ε4) risk for several age-related Table 4 Results of the trans-ethnic genome-wide association meta-analyses rsID Chr:Position Candidate/ closest gene Alleles (EA/OA) EAF OR 95% CI PI 2(%) P het 90th percentile cases versus all controls rs12143832 1:21,705,436 ECE1 C/T 0.46 0.90 0.87–0.94 2.0 × 10−70 0.722 rs7676745 4:8,565,547 GPR78 A/G 0.04 0.67 0.58–0.78 1.7 × 10−71.8 0.428 rs1262476 6:126,986,996 −A/G 0.24 1.12 1.07–1.17 9.8 × 10−70 0.574 rs2069837 7:22,768,027 IL6 G/A 0.08 0.90 0.82–0.99 5.2 × 10−850.7 0.005 rs35262860 8:55,478,909 RP1 GCT/G 0.39 1.11 1.07–1.15 5.6 × 10−70 0.955 rs62127362 19:33,458,479 CEP89 C/G 0.13 0.87 0.82–0.93 4.3 × 10−721.4 0.190 rs429358 19:45,411,941 APOE C/T 0.13 0.60 0.55–0.66 1.0 × 10−61 52.1 0.004 rs7412 19:45,412,079 APOE T/C 0.09 1.26 1.19–1.35 1.7 × 10−12 0 0.718 99th percentile cases versus all controls rs2758603 1:156,198,994 PMF1 C/T 0.34 1.12 1.02–1.22 9.8 × 10−757.2 0.005 rs3830412 3:124,397,321 KALRN A/AT 0.22 1.21 1.12–1.30 8.2 × 10−70 0.767 rs138762279 5:173,710,197 −AT/A 0.16 0.79 0.72–0.86 2.2 × 10−70 0.769 rs2069837 7:22,768,027 IL6 G/A 0.09 0.90 0.76–1.08 1.4 × 10−867.7 3.5 × 10−4 rs7039467 9:22,056,213 CDKN2A/B A/G 0.48 1.20 1.12–1.28 2.1 × 10−70 0.843 rs429358 19:45,411,941 APOE C/T 0.13 0.55 0.50–0.61 1.3 × 10−36 20.0 0.247 rs7412 19:45,412,079 APOE T/C 0.09 1.39 1.26–1.53 1.7 × 10−12 10.0 0.347 We only report the most significant genetic variant for the loci with at least one variant with a P-value ≤1×10 −6. The reported Pis the P-value from the Han-Eskin random-effects (RE2) model from METASOFT. The rsID is based on dbSNP build 150. The Chr:Position is based on Genome Reference Consortium Human Build 37 (GRCh37) EA effect allele, OA other allele, EAF effect allele frequency (based on individuals of European ancestry only), OR odds ratio (i.e., odds to become long-lived when carrying the effect allele), 95% CI 95% confidence interval, I2heterogeneity statistic, P het P-value for heterogeneity a b 35 30 25 20 15 10 5 0 –log10 (p) –log10 (p) 60 50 40 30 20 10 0 12345678 Chromosome 91113151821 123456 78 Chromosome 91113151821 Fig. 3 Results of the trans-ethnic genome-wide association meta-analyses. Manhattan plot presenting the –log 10 P-values from the trans-ethnic genome-wide association meta-analysis of the 90th percentile cases versus all controls (a) and 99th percentile cases versus all controls (b). The red line indicates the threshold for genome-wide significance (P≤5×10 −8), while the blue line indicates the threshold for genetic variants that showed a suggestive significant association (P≤1×10 −6) NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-11558-2 ARTICLE NATURE COMMUNICATIONS | (2019) 10:3669 | https://doi.org/10.1038/s41467-019-11558-2 | www.nature.com/naturecommunications 7 diseases, such as cardiovascular disease and Alzheimer’s disease33, which could explain their effect on longevity. The fact that the two variants in ApoE show opposite effects may be attributable to differences in structural and biophysical properties of the protein, since ApoE ε2 shows high stability and ApoE ε4 low stability upon folding34. We also found a genome-wide significant association of rs7676745, located on chromosome 4 near GPR78. We have to note that this locus would benefit from replication in independent cohorts in the future, given that we were not able to replicate this variant in the cohorts in which de novo genotyping was applied. There is no report of association of this locus with other traits according to Phenoscanner (http://www.phenoscanner.medschl. cam.ac.uk/)35, although other genetic variants in this gene have been associated with several diseases and traits in the UK Biobank, including death due to a variety of disorders. The GPR78 protein, belongs to the family of G-protein-coupled receptors, whose main function is to mediate physiological responses to various extracellular signals, including hormones and neurotransmitters36. However, the specific function of GPR78 is still largely unknown, although it has been shown to play a role in lung cancer metastasis37. To maximise power for discovery, we meta-analysed results from all of the studies that contained long-lived individuals that met our 90th and/or 99th percentile case definitions, had genome-wide genetic data, and were able to participate. Hence, we were not able to replicate our findings in an independent cohort with genome-wide genotype data and participants reaching the age of our case definitions. Therefore, we tried to validate our findings using two related phenotypes, parental longevity and lifespan, in the UK Biobank. We applied our case and control definitions to the parental lifespan of genotyped middle-aged UK Biobank participants rather than the participants themselves, as none of the latter fulfilled the age criteria for cases in our study. Although this resulted in relatively large data sets for both the 90th and 99th percentile analysis, the power to replicate our Table 5 Results of the gene-level association analyses Genes Ensembl ID Chromosome band Tissue OR 90 P 90 OR 99 P 99 ANKRD31 ENSG00000145700 5q13.3 Stomach 0.63 1.1 × 10−60.61 9.0 × 10−4 BLOC1S1 ENSG00000135441 12q13.2 Adipose subcutaneous 0.49 4.5 × 10−70.56 0.009 KANSL1 ENSG00000120071 17q21.31 Skin sun exposed lower leg 1.22 1.5 × 10−61.26 1.9 × 10−4 CRHR1 ENSG00000120088 17q21.31 Nerve tibial 1.54 3.4 × 10−71.81 6.2 × 10−6 ARL17A ENSG00000185829 17q21.31 Artery aorta 1.24 8.1 × 10−71.31 5.9 × 10−5 ARL17A ENSG00000185829 17q21.31 Breast mammary tissue 1.18 1.8 × 10−61.22 3.2 × 10−4 ARL17A ENSG00000185829 17q21.31 Colon sigmoid 1.21 2.2 × 10−61.21 0.002 LRRC37A2 ENSG00000238083 17q21.31 Minor salivary gland 1.17 2.2 × 10−61.20 4.4 × 10−4 ERCC1 ENSG00000012061 19q13.32 Ovary 1.19 2.8 × 10−71.24 1.8 × 10−4 RELB ENSG00000104856 19q13.32 Lung 0.57 2.0 × 10−70.44 2.9 × 10−6 DMPK ENSG00000104936 19q13.32 Stomach 1.64 1.7 × 10−62.31 1.8 × 10−6 CD3EAP ENSG00000117877 19q13.32 Brain substantia nigra 0.51 8.0 × 10−17 0.36 3.8 × 10−15 PVRL2 ENSG00000130202 19q13.32 Artery coronary 1.36 5.0 × 10−71.59 1.6 × 10−6 PVRL2 ENSG00000130202 19q13.32 Oesophagus muscularis 1.62 6.6 × 10−72.31 4.4 × 10−8 GEMIN7 ENSG00000142252 19q13.32 Brain nucleus accumbens basal ganglia 0.85 1.5 × 10−40.70 1.4 × 10−7 BLOC1S3 ENSG00000189114 19q13.32 Oesophagus muscularis 2.80 6.4 × 10−16 4.47 1.3 × 10−13 APOC2 ENSG00000234906 19q13.32 Skin not sun exposed suprapubic 0.75 4.2 × 10−70.74 9.3 × 10−4 OR odds ratio (i.e., odds to become long-lived when having an increased tissue-specific gene expression). P-values highlighted in bold are significant after adjustment for multiple testing of 247,999 longevity associations with gene-tissue pairs (Storey q-value < 0.05). OR 90 and P 90 are based on the analysis of the 90th percentile cases versus all controls meta-analysis data set, while OR 99 and P 99 are based on the analysis of the 99th percentile cases versus all controls meta-analysis data set Table 6 Results of the genetic correlation analyses of the 90th and 99th percentile phenotypes with other diseases and traits Disease/trait rg 90 SE 90 P 90 rg 99 SE 99 P 99 Coronary artery disease −0.40 0.07 1.7 × 10−8−0.29 0.07 1.2 × 10−5 Fathers age at death 0.74 0.13 2.5 × 10−80.54 0.13 2.7 × 10−5 HDL cholesterol 0.36 0.07 1.0 × 10−70.22 0.07 0.002 Age of first birth 0.33 0.07 3.8 × 10−70.16 0.07 0.019 Years of schooling 2016 0.26 0.05 9.6 × 10−70.12 0.05 0.017 Waist circumference −0.26 0.05 2.4 × 10−6−0.19 0.06 0.001 Type 2 diabetes −0.44 0.10 4.4 × 10−6−0.42 0.10 2.0 × 10−5 Overweight −0.28 0.06 1.2 × 10−5−0.23 0.07 9.0 × 10−4 Fasting insulin main effect −0.45 0.11 3.0 × 10−5−0.33 0.11 0.002 Urate −0.26 0.07 5.0 × 10−5−0.15 0.06 0.013 Body mass index −0.21 0.05 9.2 × 10−5−0.19 0.07 0.004 Cigarettes smoked per day −0.49 0.13 1.0 × 10−4−0.31 0.13 0.016 Mothers age at death 0.51 0.14 2.0 × 10−40.14 0.13 0.289 Waist-to-hip ratio −0.24 0.07 2.0 × 10−4−0.15 0.07 0.028 P-values highlighted in bold are significant after Bonferroni adjustment for multiple testing (P< 0.05/246). rg 90 ,SE 90 , and P 90 are based on the analysis of the 90th percentile cases versus all controls meta-analysis data set, while rg 99 ,SE 99 , and P 99 are based on the analysis of the 99th percentile cases versus all controls meta-analysis data set rg genetic correlation, SE standard error of the rg estimate, HDL high-density lipoprotein ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-11558-2 8NATURE COMMUNICATIONS | (2019) 10:3669 | https://doi.org/10.1038/s41467-019-11558-2 | www.nature.com/naturecommunications findings using the parental longevity traits was lower in comparison to replication using the traits based on the genotyped individuals themselves, since these individuals share only half of their parental genomes. In addition, many of the genotyped individuals, who were 40–69 years at recruitment, will never reach the age belonging to the 90th, let alone the 99th, percentile of their birth cohort. This may explain why we were unable to validate any of our suggestive associations (P≤1×10 −6), with the exception of the genetic variants at the APOE locus in these data sets. On the other hand, we were able to validate one additional locus, CDKN2A/B, in the parental lifespan data set. This is not surprising, since this locus had already been reported to associate with parental lifespan20. However, it is unclear why our reported variants at this locus, rs7039467 and rs2184061, are not associated with parental longevity, given that the most significant parental lifespan-associated variant at this locus, rs1556516, also shows a nominal significant effect on parental longevity (see Supplementary Table 2). We hypothesise that this may be due to a difference in the LD structure of the reference panels used for imputation. We were able to detect significant genetic associations at two previously identified longevity/lifespan-related loci, FOXO3 and CDKN2A/B. For the other loci, we did not find evidence for replication (P> 7.8 × 10−4), despite having adequate power (≥0.8) for replication of all but one of the examined genetic variants (rs28926173) associated with the discrete longevity phenotypes. We were not able to calculate our power to replicate the variants associated with the continuous lifespan-related phenotypes, although we should have had adequate power to replicate variants with a minor allele frequency (MAF) > 12% and an OR > 1.1 (based on the 90th percentile versus all controls analysis). However, several of the variants associated with parental lifespan show a directionally consistent and nominal significant association with our phenotypes, indicating they may also be relevant for longevity. The failure to replicate previously reported loci could be due to the use of a different longevity phenotype then what was used in previous studies, the small effect size of some of the variants associated with parental lifespan, and the modest power of our study. The fact that we detect significant associations of variants in the FOXO3 locus is not surprising, since this locus was previously reported in the longevity GWA study from the CHARGE consortium7, from which many cohorts are included in these metaanalyses. So far, three functional longevity-associated variants have been identified at the FOXO3 locus (rs2802292, rs12206094, and rs4946935). For all of them, an allele-specific response to cellular stress was observed. Consistently, the longevity-associated alleles of all three variants were shown to induce FOXO3 expression38,39.TheCDKN2A/B locus has previously been associated with parental lifespan and parents’attained age in the UK Biobank as well as a diversity of age-related diseases13,20,40. The longevity-associated allele of the most significant variant at this locus (rs1556516) has also been associated with lower odds of developing CAD41. Although the molecular mechanism behind this association is still unclear, it is known that genes encoded at the CDKN2A/B locus are involved in cellular senescence42,a known hallmark of ageing in animal models43. The gene-level association analysis identified several associations between increased (KANSL1,CRHR1,ARL17A, and LRRC37A2) or decreased (ANKRD31 and BLOC1S1) genetically driven tissue-specific gene expression with survival to the 90th percentile age. The increased expression of KANSL1,CRHR1, ARL17A, and LRRC37A2 on chromosome 17q21.31 is regulated by different genetic variants, indicating that these associations may be independent. More functional work is needed to determine the exact relationship between the altered genetically driven tissue-specific expression of these genes and longevity in humans. A limitation of MetaXcan is that the underlying GTEx models might not have been adequately adjusted for age, which could be problematic for an age-related phenotype like longevity. However, MetaXcan has successfully been used to identify gene-level associations with age-related diseases and traits, such as Alzheimer’s disease and age-related macular degeneration25. The genetic correlation analyses showed that survival to ages corresponding to the 90th and 99th percentile shared genetic associations with father’s age at death, CAD and T2D-related phenotypes, suggesting that survival to old ages may at least partially be explained by protective influences on the mechanisms underlying these traits. The genetic correlation with CAD and T2D-related phenotypes is expected, since it has previously been reported that individuals from long-lived families show a decreased prevalence of cardiovascular disease and T2D44,45. The higher genetic correlation of our longevity phenotypes with father’s in comparison to mother’s age at death may be explained by the difference in the prevalence of cardiovascular diseases and T2D between men and women in the last century46,47, which may be, at least partially, attributable to a difference in smoking prevalence48. Hence, the correlation of our longevity phenotypes with the parental age at death phenotypes from UK Biobank is likely due to the absence of death from specific diseases (i.e., those with a higher prevalence in men). For longevity-specific loci, on the other hand, one would expect that they will have beneficial effects on multiple diseases simultaneously, since long-lived individuals show a delay in overall morbidity49. Our study design imposed an age gap between cases and controls to reduce outcome misclassification, which we expected could potentially increase power by increasing the genetic effect size. It has been correctly noted that longevity study designs that include an age gap between cases and controls result in an effect estimate that is based on an OR and a relative risk (RR) term, which could lead to the identification of genetic variant associations related to early mortality (OR), rather than survival past the case age threshold (RR) (for more details see Sebastiani et al.)50. However, we have presented evidence that imposing a case–control age gap did not greatly influence our results or prevent our replication of variant associations previously discovered using study designs without a case–control age gap. First, our sensitivity analysis indicated that reducing the age gap between cases and controls had a minimal effect on our results. Our sensitivity analysis compared results using dead controls, where all individuals had died before they reached the 60th percentile age, and all controls, which included dead controls and individuals whose age at last contact was below the 60th percentile age but whose age of death was unknown. There is likely to be some outcome misclassification of the living controls, since a small percentage may survive beyond the age corresponding to the 90th or 99th survival percentile. On the other hand, the age gap between cases and controls was narrower for all controls compared to dead controls. However, despite the narrower age gap, the suggestively significant results in all controls and dead controls comparisons with 90th percentile cases were essentially unchanged, and there was a very high genetic correlation between the results of these two meta-analyses, indicating that the age gap had little or no impact on our results. Second, if we had discovered a large number of genome-wide significant variant associations in our study, it could be argued that the OR, reflecting early mortality, contributed to some or all of them. However, the only genome-wide significant variant associations we detected were in the APOE locus, which have been identified using multiple study designs, including designs with no prespecified age gap between cases and controls14, and the GPR78 locus. Third, it is unlikely that our study design prevented the replication of findings from previous GWA studies of survival to NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-11558-2 ARTICLE NATURE COMMUNICATIONS | (2019) 10:3669 | https://doi.org/10.1038/s41467-019-11558-2 | www.nature.com/naturecommunications 9