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genome-wide screening for longevity-related traits in a commu- nity-based population was published, but independent replication of its results is still pending (Lunetta et al., 2007). A meta-analysis of four prospective cohort studies of individuals reaching 90 years and older observed an association of SNP rs9664222 near the gene MINPP1, which did not reach genome-wide significance, but was replicated independently (Newman et al., 2010). A case–control GWAS, including a large sample of US-American centenarians, reported 33 replicated genome-wide significant SNPs (Sebastiani et al., in press), but the validity of the results is still critically discussed (Editorial Expression of Concern; http://www.science- mag.org/content/330/6006/912.2.full). More recently, a GWAS in Dutch nonagenarians from the Leiden Longevity Study identified a SNP, which tagged the deleterious effects of the APOE e4 allele (Deelen et al., in press).

TaqMan1SNP Genotyping Assays and the SNPlex1 Genotyping System Biosystems, Foster City, USA) on an automated platform (Hampe et al., 2001).

Data storage, quality control, and association analysis were performed using a customized database, R v2.7.1 (R Development Team, 2008) (http://www.R- project.org) and PLINKv1.04 (Purcell et al., 2007). Only autosomal SNPs with high- quality genotypes were included in subsequent analyses: SNPs were included if they had (a) a call rate   95% in cases and controls and (b) a minor allele frequency (MAF)   0.02 in controls or if jMAF(controls)   MAF(cases)j > 0.02 (see Fig. 2 for Manhattan-plot and Supplementary Fig. 1 for Q–Q-plot) and (c) P value of Hardy– Weinberg equilibrium (HWE) in controls >0.01. A total of 664,472 SNPs passed qualitycontrol.Toreducetherateoffalse-positiveresults,thegenotypingqualityof the 1600 most significantly associated SNPs (with P values of up to 9.6   10(cid:3)6) was investigatedbyvisualinspectionofthecorrespondingclusterplots(Supplementary Figs.2and3). MoredetailedqualitycontrolforGWASandfollow-upisdescribedin detail in Supplementary methods. Genotype–phenotype associations were assessed for statistical significance using a Cochran–Armitage trend (CAT) test and Fisher’s exact test, with the latter applied to both an allele- (CCA) and a genotype-based (CCG) case–control comparison. Population stratification was low (genomic inflation (GC) factor (Devlin and Roeder, 1999) for stage 1 l: 1.064 and stage 2 l: 1.06). GC-adjusted P values were calculated according to Devlin and Roeder (1999). Logistic regression analysis of rs4420638 and APOE e4 status as influence variables was conducted with and without interaction using R v2.7.1 (R Development Team, 2008). To this end, genotypes were encoded by the number of the major allele of rs4420638 or the e4 allele (APOE), respectively. All tests were two-sided.

Here, we report upon a GWAS using a case–control study design in which we compared more than 760 centenarians and nonagenarians, who represent an exceptional longevity pheno- type, with more than 1000 younger controls. We replicated and confirmed APOE as the major genetic determinant of survival into old age. In addition, when we tested the 33 SNPs of Sebastiani et al. in our samples, only the APOE-related marker rs2075650 showed a significant association.

2. Materials and methods

2.4. Haplotype analysis

Stage 1 data were also subjected to a haplotype-based association analysis, using a two-marker sliding window. To this end, maximum-likelihood estimates of haplotype frequencies were obtained and posterior probabilities of haplotypic genotypes calculated for each individual. The latter were included in a score test for association with a binary phenotype, assuming an additive risk model for haplotypes. SNPs involved in the 5000 most significantly associated haplotypes were subjected to visual inspection of the corresponding cluster plots (see Supplementary methods). Haplotypes for which both SNPs passed the inspection were genotyped and analyzed in stage 2. The analysis was carried out in R v2.7.1 (R Development Team, 2008), using the haplo.score function of R package haplo.stats v1.3.8 (Schaid et al., 2002), which takes phase uncertainty into account.