NECS centenarians not included in the discovery set (n =21), the sum of which had a median age of 107 years (Figure 7F). The sensitivity was 78% (71.5% in the group of 21 with median age 106 and 82% in the recently enrolled and older group of 39) confirming increasing sensitivity with increasing ages. The boxplot in Figure 7F shows that the specificity in an additional set of 2863 controls of replication set 2 was is 61.2%, and the AUC in this second replication set was 0.74 (Figure S7). Figure S8 shows that classification rules based on randomly ordering the top 281 SNPs (mid panels) or selecting 281 SNPs at random have lower sensitivity and specificity.

sample) comprised of 253 nonagenarians and centenarians and 341 genetically matched controls were 60% and 58% (Figure 7B) and AUC =0.58 (Figure S7). Although the distributions of the predictive scores are significantly different (p-value from t-test comparing the predicted probabilities of exceptional longevity in the two groups was 0.001), the discrimination of the model is less remarkable. Since the ages of subjects in this replication set are younger compared to the centenarians in the discovery set (median age in the ELIX set was 100 years compared to 104 in centenarians of the discovery set) and because we expect that the genetic component of exceptional longevity increases with age, we next examined the distribution of the predictive score and the trend of sensitivity in subsets of subjects with older ages. The median probability of exceptional longevity in subsets of increasing age of survival increases to more than 68% in the 81 subjects with ages .101 (Figure 7C) and, consistently, the sensitivity of the model to correctly classify older subjects increases with older ages and reaches 85% in 20 subjects ages 106 and older (Figure 7D). For example, when the 253 cases of the replication set were divided into two age groups to better match the ages of the substantially older discovery set (204 subjects, age ,103, median age 100 years, and 49 subjects, age $103, median age 105) the sensitivity of the model was 71% (Figure 7E).

Our analysis used genetic matching to remove confounding by population structure. However, since we matched subjects within clusters, residual stratification might still confound the association and possibly affect the classification rule. To test the hypothesis that there is no confounding by residual stratification, we conducted two traditional analyses. In one analysis, we adjusted the associations of the 281 SNPs by the top 4 principal components, and in the second analysis we did not. We then checked whether adjusting the analysis by the principal compo- nents would change the results of the unadjusted analysis. Figure S9 shows that the distributions of p-values for the two analyses in different genetic models are essentially identical (correlation coefficient 0.98 to 0.99). This analysis would indicate that there is no confounding due to residual stratification. We repeated the analysis adjusting for the top 10 principal components. The effect

To further investigate our hypothesis that the genetic contribu- tion to exceptional longevity increases with older ages we evaluated the sensitivity of the classification rule in a second replication set of newly enrolled NECS centenarians (n= 39) plus

Probability of ELProbability of EL‘ ote —Pel aeia ?Age 107oT 70740-2108Number of SNPs  Age 1080770440210 ——~*BONumber of SNPsProbability of ELProbability of EL  fH[0.5Age 11994 70 740-210 ~~—-280Number of SNPs1O5F %%iControl04140 210 280Number of SNPs