The BeadChps were imaged using a two-color confocal  laser system with 0.8- m resolution. The bead intensities were extracted, and genotypes were calculated using an Il- lumina-supplied cluster file, which is based on a set of ref- erence samples. The normalization algorithm adjusts for nominal offset, cross-talk, and intensity variations observed in the two-color channels. The data for each BeadChip were self-normalized using information contained within the ar- ray. This normalization algorithm removes outliers, adjusts for channel-dependent background and global intensity dif- ferences, and also scales the data. The X and Y color chan- nels undergo an affine coordinate transformation to make the data appear as canonical as possible, with the homozy- gotes lying along the transformed x and y axes. All geno- types were evaluated using a quantitative quality score called GenCall score. A GenCall score ranges from 0 to 1 and reflects the proximity within a cluster plot of the intensities of that genotype to the centroid of the nearest cluster.

Haplotype analyses. We used HaploView 4.030 for blocks definition, haplotypes estimation, and association tests. We defined haplotype blocks using the Four Gamete Rule, a vari- ant on the algorithm described in the Wang study,31 and we estimated haplotypes using the default accelerated expecta- tion-maximization (EM) algorithm.30 Differences in haplo- type frequencies between centenarians and controls were tested by means of (cid:3)2 tests; haplotypes with a frequency “—<0 0.01 were excluded from the analysis.

Statistical analysis

Quality control. Individuals showing a genotyping rate  ‘Oo *<9 93% and SNPs with a genotyping rate 95% were excluded from the dataset. We screened for contaminations and eval- uated relationships among individuals using identity by descent (IBD) estimations. Samples showing extreme het- erozygosis and related individuals were removed. X chro- mosome data were used to check for the discordances in terms of gender assignment. SNPs with a minor allele fre- aey<\ quency (MAF) 0.01 and markers deviating from the Hardy–Weinberg equilibrium (HWE) in the control popula- one< tion (p-HWD 0.001) were excluded from the analysis.

Covariate analyses. Associations between clinical risk factors and cognitive variables with genotypes were tested among nonagenarians by means of logistic and linear re- gression using R v2.7.1 software (http://www.R-project. org). All statistical analyses were performed using PLINK v1.0432 software except when specified.

Results

We recruited and phenotyped 281 male longevity “cases” (mean age 95 years) and 195 male “controls” (mean age 33 years) as part of the SICS. Subsequently, we genotyped these subjects (Table 1) using the Illumina 300k SNPs mapping BeadChip. After a preliminary phase of quality control (see Methods section), we looked for evidence of population het- erogeneity on a set of 258 males centenarians (cases) and 178 males controls by applying PCA as implemented in the EigenSoft 2.0 package.24 The estimation of the over-disper- () sion of trend test statistics ( ) was 1.05, decreasing to 1.027 after correction for the first four significant principal com- ponents. These results indicates a small or null confounding effect of population structure on our association results (Fig. 1)

Population structure. To assess the absence of population stratification, we applied the principal component analysis (PCA) approach, a method that can capture subtle and ex- tensive variations due to ethnical, experimental, and techni- cal features.24 All autosomal SNPs and individuals that passed the quality control filters (299,772 SNPs, 258 cente- narians, 178 controls) were used as input to the EigenSoft 2.024 software using the default parameters, except for the number of outliers removal iterations, which was set to 0 to obtain estimates for all subjects.

To test for associations between SNPs and the longevity phenotype, we compared allele and genotype frequencies be- tween cases and controls by means of allelic and genotypic association tests (Table 2, Supplemental Table 1). Results from all analyses were very similar for significantly associ- ated SNPs whether or not adjustments for population struc- ture were applied (Supplemental Table 2). None of the ana- “eey< ( lyzed SNPs showed deviations from HWE (p-HWD 0.05) or significant differences in missing data fractions between r<( cases and controls (p 0.05). All SNPs showed a genotyp- meee >9 ing call rate 98.8%.

Association tests. We evaluated the association of mark- ers with longevity phenotype by means of allelic association tests (1 degree of freedom [df]) and applied the Pearson (cid:3)2 —<5 test (2 df) and Fisher exact test (if cell counts were 5) to compare genotype frequencies between cases and controls. We also performed logistic regression under an additive model and logistic regression comparing homozygous mi- nor versus homozygous major alleles. Correction for popu- lation stratification was performed using logistic regression under an additive model adjusting along the top four sta- tistical significant principal components. Allele frequencies, genotype counts, odd ratios (OR), and 95% confidence in- tervals (95% CI) were also estimated for each SNP.

We also performed logistic regression analyses assuming an additive model and by comparing homozygous minor versus homozygous major alleles on a set of SNPs located at the FOXO3A locus (Table 2). To confirm the null influence

 Centenarians + Controls  Centenarian   

Δ FIG. 1. Population structure of centenarians (+) and control subjects ( ). Each scatterplot shows the first two components that were estimated using genotype data for more than 300K SNPs in centenarians and control subjects the program EigenSoft 2.0. The two populations are ethnically identical, as shown in the rightmost scatter plot.