most significant. Posterior probability of exceptional longevity (EL) and average longevity (AL) (x axis) in the centenarians (red boxplots, label EL), nonagenarians-centenarians (light blue, label NN), Illumina controls (blue boxplots, label AL), in the replication set 1 (panel 1) and replication set 2 (panel 2). Panels 3 and 4 show the effect of reordering the nested models, and panels 5 and 6 show the effect of selecting a random set of 281 SNPs from the top 1,700 most significant SNPs. Numbers in parentheses denote the accuracy in each boxplot ordered from top to bottom. For example, in panel 1, 58% is the accuracy ( =specificity) in controls, 57% is the accuracy (sensitivity) in subjects of the replication set ages ,103, and 71% is the accuracy (sensitivity) in the centenarians ages .102. Changing the order of the 281 SNPs decreases the difference in posterior probability of EL between centenarians and controls so that the model is less able to discriminate between centenarians and controls. The effect is even greater when the SNPs are randomly chosen from the top most significant. (TIF)

Figure S13 Clusters of profiles of the controls in the

discovery set. Genetic signatures in 845 controls subjects of the discovery set. Numbers in parentheses are the cluster sizes (N), and the average posterior probability of exceptional longevity per cluster. Color coding represents the strength of the genetic risk to predict EL (Blue: P(EL|g 281).0.95, Red: 0.5,P(EL|g 281),0.95; Orange: 0.20,P(EL|g 281),0.5; Green: P(EL|g 281),0.2). (TIF)

trained in the discovery set to compute the genetic profiles of all controls, and clustered the profiles using the same analytic strategy. The cluster analysis grouped subjects in 254 clusters of 7 or more, while the remaining subjects had more sporadic signatures. The pie charts display the distribution of all genetic signatures in the 801 centenarians of the discovery set (left) and the 4118 controls (right). The slices are color coded as in the previous figures (Blue: p(EL|g ).0.95; Red: 0.70,P(EL|g ),0.95; 281 281 Brown: 0.5,P(EL|g ),0.7; Orange: 0.17,P(EL|g ),0.50; 281 281 Green P(EL|g ),0.17). The label P(E) denotes p(EL|g ). 281 281 Note the almost lack of ‘‘blue’’ and the dominance of ‘‘green’’ and ‘‘orange’’ signatures in the control set compared to the centenarian set.

Figure S9 No evidence of residual stratification on individual SNP associations. Plot of the 2log10(p-value) of

the 281 SNPs included in the ensemble of genetic risk models. The x-axis reports the 2log10(p-value) for the unadjusted analysis, and the y-axis reports the 2log10(p-value) for the analysis adjusted by the first 4 principal components. The analysis shows that there is no real change between adjusted and unadjusted analysis (correlation coefficient = 0.98.6, 99.0 and 98.2) and suggests that population stratification does not appear to confound the associations. For both analyses, we fit a logistic regression models using PLINK.

Figure S15 Signatures with random profiles. To compare

Plot of first two principal components (PC1 and PC2) to show the population structure in centenarians. Panels B and C show the principal components (PC1, and PC2, x axis) and probability of exceptional longevity (y-axis). The plot shows that the ranges of values of probability of exceptional longevity do not change in the 3 groups. (TIF)

the results from cluster analysis of genetic risk profiles and derived signatures against random results, we randomly selected 300 SNPs from the list of analyzed SNPs, we generated a set of nested genetic risk models using the procedure described in the manuscript and then we tried to cluster the genetic risk profiles. We repeated this analysis a few times, and consistently showed that sensitivity and specificity in the replication set were 0.5 (pure chance), and when we attempted to cluster the genetic risk profiles the analysis produced many smaller clusters (average size 3 profile per clusters compared to 15 profiles per cluster in the signatures generated in the manuscript), many profiles that could not be clustered at all, and those profiles that could be clustered more effectively were showing random variability around 0.5. (TIF)

Figure S11 26 genetic signatures of exceptional longev-

clustered using CAGED and hierarchical clustering and then ordered by the average genetic risk. In each plot, the x-axis reports the number of SNPs in each genetic risk model (1,…,281 SNPs), and the y-axis reports the posterior probability of exceptional longevity predicted by each model. Together, the boxplots (one for each SNP set on the x axis) display the genetic risk profiles of the centenarians in the same cluster. Numbers in parentheses are the cluster sizes (N), and the average posterior probability. Color coding represents the strength of the genetic risk to predict EL (Blue: P(EL |g 281).0.95; Red: 0.5,P(EL |g 281),0.95; Orange: 0.20,P(EL|g ),0.5; Green: P(EL|g ),0.2). Only clusters 281 281 with 8 or more centenarians are included and describe 90% of all cases in the discovery set. (TIF)

were generated with the R package, and the box displays the ages at death between the 25th and 75th percentile, with median age depicted as the middle bar. The whiskers extend to the most extreme data point which is no more than 1.5 times the interquartile range from the box. The boxplots are ordered by predictive accuracy of the genetic risk models within clusters. (Blue: P(EL|g ).0.95,; Red: 0.5,P(EL|g ),0.95; Orange: 281 281 0.20,P(EL|g ),0.5; Green: P(EL|g ),0.2). The most 281 281 predictive cluster (C1) is associated with the longest median survival, and other genetic signatures are characterized by different survivals as well.

clusters with 8 or more centenarians are included. Several of the signatures discovered in the replication set match signatures in the discovery set: The pattern of R1 matches C1, R2 matches C2, R4