In stage 1 we conducted a GWAS where a total of 763 unrelated German long- lived individuals (LLI) were studied who were between 94 and 110 years of age (meanage:99.7years;Table1).The1085Germancontrolindividualsinstage1(age range: 45–77 years; mean age: 60.2 years) were obtained from the Popgen (Krawczak et al., 2006) and KORA (Wichmann et al., 2005) biobanks. The regional genetic differences between northern and southern Germany are not significant (Lao et al., 2008; Steffens et al., 2006) and therefore the two populations were pooled.Thesexratiowasapproximately1:1.The754LLIusedforvalidationinstage 2hadanagerangeof95–108yearsandameanageof96.9years.Thestage2control sample of 860 individuals (age range: 60–75 years; mean age: 67.3 years) was matched to the LLI as closely as possible in terms of ancestry, gender and geographical origin. The sex ratio in this sample was 12:1 in favor of females. The lack of men in stage 2 implies that any male-specific effects that were observed in theinitialscreeningmaynotbedetectableinthefollow-upsample.Thedescription for the stage 1 samples is given in detail elsewhere (Krawczak et al., 2006; Nebel etal.,2005;Wichmannet al.,2005).Forstage2itispublishedinNebelet al.(2005). There are no mortality data available for the controls. However, based on current predictions only 1.5% of all 60-year old and 1.8% of all 75-year old German females will become 100 years (to become 95 years old the respective proportions are 9.5% for60-yearold and12.3%for75-yearold women).The probabilityformalesiseven lower (Human Mortality Database: www.demogr.mpg.de). Hence, we can estimate that only a negligible proportion of younger controls will become long-lived. The French sample used to investigate rs12741354 in ASTN1 comprised 536 centenarians (mean age: 103.7 years) and 508 younger controls (mean age: 50.8 years). Cases andcontrols were matchedfor gender and geographicorigin (Blanche´ et al., 2001). The sex ratio was 5:1 in favor of females.

2.5. Testing for association of 33 SNPs from Sebastiani et al. in German samples

To test the association findings of the study by Sebastiani et al. which were obtained from Illumina 370 CNV chips and Illumina 610 arrays, the German Affymetrix GWAS data had to be imputed, since different genotyping technologies hadbeenused.Genotypeimputationwascarriedout usingahiddenMarkovmodel algorithm implemented in the software program BEAGLE v3.1.1 (Browning and Browning, 2009) and the HapMap 3 samples from the CEU, TSI, MEX and GIH populations to infer missing autosomal genotypes in silico. To take imputation uncertainty into account, association analysis between the phenotype and the dosage data (expected allele counts) was performed using PLINK’s logistic regression framework for dosage data (with flag-dosage).

3. Results

DNAwasisolatedfrombloodsamplesofallparticipantsusingstandardmethods. All subjects gave written informed consent prior to participation. The protocols were approved by the respective institutional ethics review boards. The study workflow is given in Fig. 1.

3.1. Stage 1 analyses

We have ranked the SNPs according to their minimum (P ) of min P , P , and P . A total of 58,903 markers (8.9%) showed a CAT CCA CCG nominal P value <0.05. To reduce the rate of false-positive min results, cluster plots of the 1600 most significantly associated SNPs (all with P values (cid:5)9.6  10(cid:3)6) were subjectedto post hoc visual min

2.2. Genotyping

The Affymetrix1 Genome-Wide Human SNP Array 6.0 (San Francisco, CA, USA) was used to genotype the individuals in stage 1 (Supplementary methods). For validation, markers were genotyped in the individuals from stage 2 with

Table 1

Description of LLI and controls genotyped in the two stages of the study.

Stage Sample No. Mean age (years) Median age (years) Age range (years) 1 LLI 763 99.7 101 94–110 1 Controls 1085 60.2 60 45–77 2 LLI 754 96.9 97 95–108 2 Controls 860 67.3 66.5 60–75

LLI: long-lived individuals.

            stage 2: fepl in French stage 1: GWAS | Quality criteria: |  906600 SNPs -_ 664472 SNPs validation of 16SNPs | — 1SNPinASTN1* 763 LLI (median age: 101) = visual inspection of + 754 LLI (median age: 97) + 536 centenarians (median age: 103)+ 1085 controls (median age: 60) scatterplots of 1600 SNPs + 860 controls (median age: 66.5) + 508 controls (median age: 52)   

Fig. 1. Workflow diagram summarizing the different stages of the study.

association did not withstand Bonferroni correction (Bonferroni- adjusted P = 0.091 assuming 13 tests; see Supplementary CCA methods). As SNP rs12741354 had the second lowest P value, it was also investigated in an independent collection of 536 French centenarians (mean age: 103.7 years) and 508 younger controls (mean age: 50.8 years) (power: 65.5%) where it failed to show any association with longevity (P = 0.86); Supplementary CCA Table 1).

(Supplementary methods) and were selected for follow-up (Table 2). Their nominal P values ranged from 1.8 a 10(cid:3)10 to CCA 9.5   10(cid:3)6. Due to multiple testing in GWAS usually low initial P values in stage 1 are required in order to be able to replicate the association. Our follow-up study has not sufficient power to detect SNP-effects in stage 2 with higher P values (>10(cid:3)5). Therefore, additional SNPs were not included in our follow-up. Only the association between longevity and SNP rs4420638 near the APOC1 gene achieved genome-wide significance (Bonferroni-adjusted P = 1.2   10(cid:3)4, based upon 664,472 tests and assuming no CCA correlation between SNPs; see Fig. 2 and Supplementary Fig. 4). The significance of the other 15 SNPs did not withstand Bonferroni correction, thereby highlighting the need for independent valida- tion.

3.4. APOE