We included SNPs that had a minor allele frequency (MAF) of at least 1% and an imputation quality ratio (de Bakker et al., 2008) (equivalent to the MaCH r2 statistic; Li et al., 2009b) of at least 0.3. The study-specific inflation factors were computed using GC the set of chi-square statistics used for the meta-analysis for each study. The inflation factor is computed as the median of all chi-square statistics divided by the expected median of the statistics (approximately 0.456) for a chi-square distribution with 1 degree of freedom. SNP associations at p < 5 x 10^-8 were considered to be genome-wide significant. SNPs with p < 5 x 10^-5 were considered suggestive associations. The combined meta-analysis hazard ratio (HR) can be interpreted as the increase in the risk of dying or having a major event during follow-up per additional copy of the coded allele. Power analysis revealed 80% statistical power to detect SNPs with a minor allele frequency of 5% and relative risk of 1.10 using a nominal significance level of 0.05.

The participants stem from cohorts of the CHARGE Consortium (Psaty et al., 2009). All cohorts are follow-up studies periodically assessing the health and vital status of their participants. Although some of the cohorts included multiple ethnic groups, only data from self-reported Caucasians were used. In addition, population structure was assessed using principal components in each CHARGE study, and outliers were removed. Any remaining within-study structure was adjusted for using appropriate methods (Price et al., 2006). All participants included in this analysis were at least 55 years of age at the time of blood draw for DNA and provided written informed consent.

We conducted a survival analysis, adjusted for age at baseline and sex, to model continuous time to death or end of follow-up in 25,007 participants (deceased cases n=8444; mean follow-up time 10.6 [SD 5.4] years) that were older than 55 years at baseline. As research demonstrated that the likelihood of incident disease is genetically determined, we defined a second phenotype: survival free of major disease or mortality (Atzmon et al., 2004; Lunetta et al., 2007; Vijg and Suh, 2005). The outcome was defined as time to the first of the following adjudicated events: myocardial infarction, heart failure, stroke, dementia, hip fracture, cancer, or death. For this analysis, participants at baseline were older than 55 years of age and free of any of the aforementioned conditions. Inclusion in the study required complete follow-up information on mortality.

In addition, we incorporated gene annotation information, a technique that has successfully been applied in the field of aging research (de Magalhaes et al., 2009a, 2010). Protein Analysis Through Evolutionary Relationships (PANTHER; Mi et al., 2007; Thomas et al., 2003) and Ingenuity Pathway Analysis (IPA) (www.ingenuity.com) were used for identification and classification of networks, pathways, biological processes, and molecular functions of the genes identified in this study. For both phenotypes, we generated lists of candidate genes. These genes were the closest referenceA nominally significant level of p 0.05 was observed across all replication samples (Supplementary Tables S5-S8). In the combined meta-analysis of the discovery and replication studies, only rs1425609 in the vicinity of otolin-1 (OTOL1) showed a stronger association (1.61 x 10^-6). Likewise, no genome-wide significant findings were identified in the time-to-event analysis, which included 16,995 participants free of disease at baseline and registering 7314 events over an average of 8.8 (SD 5.7) years of follow-up (Table 2). Events included incident myocardial infarction, heart failure, stroke, dementia, hip fracture, cancer, or any death. The Q-Q plot (Fig. 1a, 1.019) showed no evidence of inflation of type I error due to genomic control. In total, eight independent SNPs were associated with event-free survival at a threshold of p < 10^-5. The SNP with the strongest association was rs10412199 (chromosome 19, p = 3.02 x 10^-6), which is in close proximity to ataxia, cerebellar, Cayman type (AT-CAY). Additional descriptive information including definitions of each event and association results with p < 10^-4 are provided in Supplementary Figure S2 and Supplementary Tables S9-S12.

Fig. 1 shows quantile-quantile (Q-Q) plots after meta-analysis for time to death and time to event. The overlap between their association results is presented in Table 3. Interpretation of the overlap between the phenotypes requires caution as both phenotypes are correlated; nevertheless, it helps to focus on specific loci and put them into the context of aging. From the 14 loci passing the prespecified suggestive threshold of p < 1 x 10^-5 in the time to death analysis, five had corresponding SNPs within 500 kilobase pairs distance, in linkage disequilibrium (LD; r^2 > 0.1) associated with p < 1 x 10^-4 and the same overall direction of effect in the time to event analysis. These five regions were in the vicinity of the following genes: OTOL1 (3q26.1), bridging integrator 2 (BIN2, 12q13), ATG4 autophagy-related 4 homolog C (ATG4C, 1p31.3), origin recognition complex subunit 5-like (ORC5L, 7q22.1), and potassium voltage-gated channel, KQT-like subfamily, member 4 (KCNQ4, 1p34). Similarly, in the time to event analysis, three of the eight top SNPs showed considerable overlap and the same direction of effect as those in the time to death analysis. These SNPs were close to the following genes: MDS1 and EVI1 complex locus (MECOM, 3q24-q28), succinate-CoA ligase, ADP-forming, beta subunit (SUCLA2, 13q12.2-q13.3), and ST3 beta-galactoside alpha-2,3-sialyltransferase 3 (ST3GAL3, 1p34.1).