Prospective follow-up studies with a continuous outcome such as time to death are more powerful than case-control analyses. A study of time to death simultaneously addresses the effects of genetic variation related to life span, the progression toward death, and disease-specific mortality. This design has been successfully applied in animal models (Finch and Ruvkun, 2001; Kenyon, 2010) and also in hu- man genetics research of blood pressure (Levy et al., 2009; Newton-Cheh et al., 2009; van Rijn et al., 2007), a trait with heritability similar to longevity, where examination of a continuous outcome has been more successful in identifying genetic loci than studies that have solely used hypertension

These authors contributed equally to this work.

These authors are joint senior authors on this work. * Corresponding author at: Erasmus Medical Center Rotterdam, Depart- ment of Epidemiology & Biostatistics, P.O. Box

,

DR Rotter- dam, Netherlands. Tel.: (cid:

)

; fax: (cid:

)

(H. Tiemeier).

least 4 out of 6 of the health conditions. Genome-wide information on polymorphisms was available for 16,995 participants free of disease at the beginning of the study. These participants were followed for 8.8 (SD 5.7) years and we registered 7314 major events.

as a dichotomous trait. Frailty and survival free of disease have been suggested as more promising phenotypes for studies of aging because mortality is a very heterogeneous outcome caused by multiple chronic conditions (Vijg and Suh, 2005).

sequential way using data from cohort studies participating in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium. First, we aimed to identify single nucleotide polymorphism (SNPs) associated with all cause mortality (time to death) in a hypothesis-free GWAS in approximately 25,000 unselected persons of Eu- ropean ancestry. Second, we performed GWAS of time to event, defined by major incident events (myocardial infarc- tion, heart failure, stroke, dementia, hip fracture, or cancer) or death, as an alternative phenotype for healthy aging. Last, we analyzed the SNPs along with their respective most likely associated genes identified in the GWAS meta-anal- yses to identify pathways and networks associated with aging and longevity.

2.3. Genotyping and imputation

As different genotyping platforms were used across stud- ies, we imputed to 2.5 million SNPs using the HapMap 22 CEU (Build 36) genotyped samples as a reference. For details on the study-specific quality control procedures for genotyping and imputation please consult Supplementary Table S1.

2.4. Statistical analysis

We used the semiparametric Cox proportional hazard to model time to event for both phenotypes in each study. Follow-up time since baseline was used as time scale. An additive genetic model was used in this analysis. We sub- sequently combined the individual study results in a meta- analysis using a fixed effects model that combined the study-specific regression parameters and standard errors us- ing inverse variance weighting. 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 ((cid:1) ) 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 distri- bution with 1 degree of freedom. SNP associations at p r<! 5  10(cid:3)8 were considered to be genome-wide significant. SNPs with p  5  10(cid:3)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 (Supplementary Table S2).

2. Methods

2.1. 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 princi- pal components in each CHARGE study and outliers were removed. Any remaining within-study structure was ad- justed 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. A brief description of each pop- ulation is given in the Supplementary Information.

2.2. Phenotype

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” ee 8444; mean follow-up time 10.6 [SD 5.4] years) that were older than 55 years at baseline. As research demon- strated 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: myo- cardial infarction, heart failure, stroke, dementia, hip frac- ture, 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 re- quired complete follow-up information on mortality and at