Note that this property is independent of the SNPs in the current SNP set, so changing the order of the text does not affect the meaning. Therefore, no correction is needed for this part of the text.Nested models may change the overall pattern of the risk profile, but not the interpretation in terms of enrichment of longevity-associated variants. The ensemble of genetic risk models uses 281 nested SNP sets to compute the risk for EL and AL (average longevity), and the overall risk is estimated as the average of all genetic risks. There were 34 duplicate samples genotyped using TaqMan across the 28 SNPs, generating a total of 952 duplicate genotypes, 950 of which had both samples called. Of these 950 duplicate genotypes, 100% were concordant. For the 28 SNPs successfully genotyped, we observed between 1 and 10 discordant genotypes per SNP between the TaqMan genotype and the microarray genotype, yielding concordance rates between 98.88% and 99.89% between genotyping platforms. Our overall discordance rate across all SNPs was <1%, and accuracy was 85%. Panel B displays the specificity for the two types of controls in the discovery set (NECS referent subjects: continuous line; Illumina controls: dashed lines) and shows that there is no difference between the two control sets. Panel C describes the effect of reordering the 281 SNPs. Patterns of sensitivity and specificity using the discovery set (left) and randomly generated validation sets (right) when the top 281 SNPs were randomly entered into the nested models (continuous lines: SNPs are ordered by MBF; dashed lines: the same 281 SNPs are randomly arranged). Panel D describes the effect of random selection on sensitivity and specificity of the nested models. Patterns of sensitivity and specificity using the discovery set (left) and randomly generated validation sets (right) when 281 SNPs were randomly chosen from the top 1,700 most significant SNPs (continuous lines: SNPs are ordered by MBF; dashed lines: 281 SNPs are randomly selected from the 1,700 most significant). The analysis shows that changing the order affects sensitivity and specificity of the model. Furthermore, selecting SNPs at random from the top most significant SNPs gives models that are consistently less specific and less sensitive. This low rate of discordant genotypes did not affect the results: 23 of the 28 SNPs reached statistical significance in the replicated data, and although 5 SNPs did not reach statistical significance, possibly because of the small sample of controls, the allele frequencies from the microarray data and TaqMan data are virtually indistinguishable (Figure S5), suggesting that a 1% genotyping error rate should have no impact on this analysis. Supporting Information S1 Population structure of centenarians and controls. Scatter plot of principal components 1 and 2 (PC1 and 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, for example Italics (fl green), SaxNon/Scandinavia (green), Celtics/Alpine (& red), and Franks (N, red). Figure S5 Correlation between allele frequencies estimated 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-axis 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 associations (2log10(2/100,000) =4.7Cluster analysis was performed on genetic risk profiles and derived signatures. From the analyzed SNPs, 300 were randomly selected, and a set of nested genetic risk models were generated using the procedure described in the manuscript. This analysis was repeated multiple times, consistently showing sensitivity and specificity in the replication set to be 0.5 (pure chance). The attempt to cluster the genetic risk profiles produced many smaller clusters (average size of 3 profiles per cluster 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. Figure S11 shows 26 genetic signatures clustered using CAGED and hierarchical clustering, ordered by the average genetic risk. The boxplots display 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. Only clusters with 8 or more centenarians are included. Several