Table 1 shows the top 14 SNPs (p-value < 10^-5) for time to death ranked by p-value from meta-analysis of nine cohorts. Only SNPs with a minor allele frequency (MAF) > 3% are presented. p-values are for the inverse variance-weighted for NCBI Build 36. HRs are for each additional coded allele. The number of supporting SNPs is the number of SNPs within 500 kilobase pairs of CEU release 22 (r^2 > 0.10) and have association p-value < 0.05. Key: Chr, chromosome; LD, linkage disequilibrium; MAF, minor allele frequency; SNP, single nucleotide polymorphism. For information on all SNP associations with p-value < 10^-4, see Supplementary Table S2. Study-specific information is presented in the order: RS, CHS, FHS, ARIC, AGES, HABC, BLSA, InCHIANTI, SHIP; coded allele of mortality, not tested. 

Table 2 shows the number of participants and events by cohort in the time-to-event analysis. The total number of participants was 16,995 with 7314 events over an average of 8.8 years of follow-up.

Table 3 presents the overlap between association results for time to death and time to event analysis. The number of SNPs with p < 0.05 in both analyses is shown for each gene regionFinally, we evaluated candidate genes for aging by identification and classification of networks, pathways, biological processes, and molecular functions. The candidate genes were derived from the meta-analyses of GWAS and included the reference genes closest to the SNPs associated with p < 1 x 10^-3 (time to death: 862 genes, time to event: 704 genes). We used PANTHER (Mi et al., 2007; Thomas et al., 2003, 2006) and IPA software (www.ingenuity.com) for these analyses. PANTHER compares these gene lists to the reference list using the binomial test for each molecular function, biological process, or pathway term. IPA builds networks by searching the Ingenuity Pathways Knowledge Base for interactions between the identified genes and all other gene objects stored in the knowledge base.

The biological processes overrepresented in the PANTHER analysis were developmental processes, neuronal activities, signal transduction, neurogenesis, ectoderm development, and cell adhesion. For the analysis of time to incident event, developmental processes and neuronal activities were overrepresented among other biological processes (Table 4). The analyses also highlighted the Wnt signaling pathway. The Wnt signaling pathway is ubiquitous and known to be involved in cancer but also plays an important role in the early stages of the development of the central nervous system, in synaptic formation by axon guidance, and in modulating fibrosis during muscle repair scored high in both traits under study (Brack et al., 2007; Inestrosa and Arenas, 2010; Keeble et al., 2006; Ulloa and Martínez-Martínez, 2010). For extended tables see Supplementary Tables S13 and S14. 

In addition, Ingenuity identified 1 network with p < 1 x 10^-31 containing 26 genes involved in processes related to nervous system development and function for the analysis of time to death (Fig. 2) and 1 network with p < 1 x 10^-40 containing 28 genes involved in cellular function and development for time to event (Supplementary Fig. S3). IPA analysis highlighted the following genes associated with the time to death trait: NTRK2 (neurotrophic tyrosine kinase, receptor, type 2) a member of the neurotrophic tyrosine receptor kinase family. This kinase is a membrane-bound receptor that, upon neurotrophin binding, phosphorylates itself and members of the mitogen-activated protein kinase (MAPK) pathway. Signaling through this kinase leads to cell differentiation. Second in line were NCAM1 (neural cell adhesion molecule 1) a cytoskeletal binding Overlap between the associations of time to death and time to event Top SNPs from time to death (time to event) analysis associated with different p-values in time to event (time to death) analysis Top hit SNP Chr Closest reference Time to death Time to event Top SNPs from time to death (time to event) analysis associated with different p-values in time to event (time to death) analysis p Gene Effect p Gene Effect Total p   0.05 p   0.05 p   0.01 p   0.001 p   0.0001 Time to death 1 rs1425609 3 OTOL1 1.46E-06 0.005704 1119 693 235 132 37 22 2 rs766903 12 BIN2 1.61E-06 0.01315 37 27 4 5 0 1
3 rs12042640 1 ATG4C 1.71E-06 0.03701 93 60 19 4 0 10 4 rs11582903 1 LMO4 3.94E-06 0.7336 133 91 8 12 21 1
5 rs10259086 7 ORC5L 5.16E-06 0.03266 239 154 56 21 4 4
6 rs2769255 1 KCNQ4 5.17E-06 0.01322 287 151 68 56 7 5
7 rs17291546 6 LOC3401567.65E-06,0.01624,29,19,9,1,0
8 rs12606100,18 NETO1,8.72E-06,0.02853,23,16,5,2,0,0
9 rs1274214,11 GRAMD1B,8.87E-06,0.0567,101,39,28,17,17,0 
Time to event 
2013], GRIA1), or have been implicated in neurological disorders (BIN2 [Chapuis et al., 2013], NCAM1 [Kulahin et al., 2011], NTRK2 [Klein et al., 2011]). However, further research is needed to confirm the potential role of these genes in human aging and longevity.

One possible limitation of our study is the focus on common genetic variants, which may not capture the effects of low frequency and rare variants or structural variations. Additionally, phenotypic heterogeneity across different cohorts may have limited our ability to identify true effects. Nevertheless, our study highlights the potential contribution of genes related to neural regulation, cellular and neural development and function, and cell communication to variation in human aging.