Supporting Information

S1 Population structure of centenarians and

controls. Scatter plot of principal components 1 and 2 (PC1 and 2 PC2, top panels), and principal components 3 and 4 (PC3 and PC4, bottom panels) in subjects from the NECS (left) and Illumina database (right) that were estimated using genome wide data. We labeled the clusters by ethnicity using the information about mother tongue and place of birth of NECS subjects and their parents. Note that some of the European ethnic groups in controls (NECS and Illumina) are not represented in NECS cases, N for example Italics (fl green), SaxNon/Scandinavia ( green), Celtics/Alpine (& red), and Franks (N , red).

Figure S5 Correlation between allele frequencies esti- mated with the TaqMan assay and the arrays. The top

panel shows the agreement between the allele frequencies estimated with the TaqMan assay in 688 centenarians (x-axis) and 801 centenarians of the discovery set (y-axis). The bottom panel shows the agreement between the allele frequencies estimated with the TaqMan assay in 221 controls of the NECS included in the discovery set (x-axis) and all 914 controls of the discovery set (y-axis).The difference between allele frequencies in the two groups was at most 0.04 (rs6801173). This particular SNP has substantial variability with ethnicity.

Figure S2 Error rate in log10 scale of the Bayes rule for different thresholds of the MBF. The x axes reports the

estimate of the 2log10(error rate) and 95% credible intervals that were estimated using a Beta distribution in 1,000,000 simulations per threshold on the MBF (y-axis). The MBF is the maximum Bayes Factor computed to test the association of each SNP in 4 genetic models (genotypic, allelic, dominant, recessive). The genotype data were generated with allele frequencies varying uniformly between 0.05 and 0.5 and assuming HWE. The analysis suggests that a MBF.1,400 determines an error rate of approximately 1 to 2 errors per 100,000 tested association (2log10(2/100,000) =4.7)), and a MBF.100 determines an error rate of approximately 4 errors per 100,000 tested association (2log10(4/100,000) =3.4). Note that this analysis includes the additional costs of searching for 4 genetic models.

Figure S6 Genes in the genetic risk models have been

the genetic risk model that are linked to dementia in the literature, either by functional or genetic association studies. 38 of the 42 genes are also linked to Alzheimer’s disease (See Figure 6) and in red are 4 nodes that are specifically linked to dementia but not Alzheimer’s disease. The nodes that are linked by an edge represent genes that are either ‘‘co-cited’’ (dashed lines) or ‘‘associated by expert curation’’ (continuous lines). The arrow head means that the associations are activation (triangle), inhibition (circle), modulation (diamond), conversion (arrow head). The node shape informs about known roles of the genes (see inset). The nodes that are singleton were linked to dementia in the literature but not together with other genes. The number of genes linked to dementia was compared to what is expected by chance using Fisher exact test, and the p-value 1.07e -6 shows that the gene set is unluckily the result of chance. (Network generated with Genomatix).

proach. The Manhattan plot shows the 2log10(p-value) for the 1 degree of freedom test Chi-square test. The QQ-plot displays the observed quantiles of the 1 degree of freedom test Chi-square test versus the expected quantiles.

nested models in 1000 resampled sets. 1000 training and test sets were randomly resampled from the discovery set and each training set was used to estimate the Bayesian classification rule that was tested in the test set. The plot displays the average sensitivity and specificity (y-axis) versus number of SNPs (x-axis). The sensitivity is the proportion of centenarians with posterior probability of exceptional longevity.posterior probability of average longevity and the specificity is the proportion of controls with posterior probability of exceptional longevity,posterior probability of average longevity. The mean number of SNPs in which the absolute difference between sensitivity and specificity was ,0.02

Figure S7 Results of the ROC analysis in the discovery

and replication sets. Top panel: We conducted the ROC analysis using the R package ‘‘validation’’ for the ensemble of 281 nested models. The ensemble of model trained in the discovery set was then used to predict the outcome in the two replication sets and the predictions were assessed using ROC analysis. Bottom panel: ROC analysis of the predictions when the SNP rs2075650in TOMM40 was removed from the predictive SNPs. (TIF)

Figure S8 Effect of rearrangement of the top 281 SNPs and random selection of 281 SNPs from the top 1,700

matches C6, R5 matches C11, R8 matches C19, R15 matches C26. The profiles were generated using the genetic risk models trained in the discovery set. The profiles were then clustered using CAGED and hierarchical clustering and then ranked by the average posterior probability of exceptional longevity per cluster. (TIF)