wide are an unprecedented phenomenon with significant health and social impact. Although environmental factors have led to an increase in life span, there is ample evidence that genetic factors are involved in extreme longevity both in humans (1–7) and in other organisms (8). The protective genetic factors that lead to longevity are likely to involve fundamental processes of aging that may be different from those associated with early mortality or premature onset of age-related diseases in younger individuals. The mecha- nisms of aging in humans are far from understood, but avail- able evidence suggests that several pathways—inflammation, oxidative stress and stress responses, cellular senescence, DNA damage and repair, and the growth hormone or insulin- like growth factor and insulin (GH, IGF, INS) axis—may play key roles (9–12). Model organisms suggest that inhibit- ing the GH, IGF, or INS axis, which is involved in regulat- ing cell proliferation, cell death, wound repair, and metabolism, may promote longevity by reducing oxidative stress and slowing the rate of cell replication and the accu- mulation of somatic-cell DNA mutations (13). There is also evidence for other important pathways such as the heat- shock proteins and heat-shock factors that are highly con- served across species and play a role in prolongevity transcription pathways. Clinical and epidemiological inves- tigations, including candidate gene studies, have suggested that inflammation pathways may affect life span and risk of age-related conditions such as cardiovascular disease (CVD) and its risk factors (14–19). A combination of multiple genetic variants may be required for an individual to achieve excep-

We conducted a meta-analysis of GWAS findings for lon- gevity within an international consortium of four longitudi- nal community-based cohort studies that followed adults over many years. Longevity was defined as survival to age 90 years or older, and a comparison group was drawn from each cohort. Furthermore, we identified two independent cohorts of long-lived individuals, the Leiden Longevity Study cohort and the Danish 1905 cohort, to evaluate initial findings for the strongest allelic associations for longevity in a second discovery stage.

Longevity and Comparison Group Definitions

Methods

In the present study, achievement of longevity was de- fined as reaching age 90 years or older, regardless of whether the participants were still living or had since died. Geno- typed participants from these studies who died between the ages of 55 and 80 years were used as the comparison group. The comparison group was limited to deceased participants to ensure that no one in the comparison group could subse- quently achieve longevity. The minimum age at death was set to match the minimum age at enrollment in the RS to promote age comparability of the comparison group across the four cohorts. The maximal age at death in the compari- son group was set arbitrarily at age 80 years to include the majority of deaths, to maximize the overlap between birth cohorts, and to exclude those persons who survived far be- yond average life expectancy for their respective birth co- hort, that is persons who nearly reached longevity. Because of the timing of recruitment, DNA collection, and death, there was only partial overlap of the birth cohorts included in the comparison groups and the group of persons achiev- ing longevity. Only Caucasian participants were included. Across the four studies, there were 1,836 persons who achieved longevity (144 from AGES-Reykjavik, 557 from CHS, 362 from FHS, and 773 from the RS), and the com- parison group had 1,955 participants (122, 544, 355, and 934 participants from the AGES-Reykjavik, CHS, FHS, and RS, respectively). To facilitate comparison of results across studies, we imputed to 2.5 million SNPs using the HapMap Centre d’Etude du Polymorphisme Humain European Ancestry–genotyped samples as a reference. The effective sample size for all but one of the top SNPs was more than 80% of the full sample size of 3,791, indicating that the SNPs that were not directly genotyped were imputed well in most studies.

The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium was convened to pro- mote the discovery of new genomic loci involved in multiple complex traits in population-based follow-up studies using genome-wide association analysis (30). This meta-analysis used data from the CHARGE Consortium, which includes the Age, Gene/Environment Susceptibility-Reykjavik Study (AG- ES-Reykjavik) (31), the Cardiovascular Health Study (CHS) (32), FHS (33–36), and the Rotterdam Study (RS) (37).