nested genetic risk models starting with the most significant SNP in the discovery set and incrementally adding one SNP at a time from a pruned set of SNPs that are sorted in order of log10(MBF). Each model is used for prediction, and the accuracy of each model to predict exceptional longevity and average longevity is evaluated by sensitivity and specificity (Figure 4B). The trend of sensitivity and specificity in Figure 4B shows that including more SNPs increases both sensitivity and specificity but the gain of accuracy becomes less and less as SNPs with decreasing statistical significance (lower MBF) are added. Particularly, the sensitivity plateaus between 275–285 SNPs so that including more SNPs does not appear to improve the sensitivity further (Figure 4B). Because the model with 281 gives the closest sensitivity and specificity, we stopped the search for predictive SNPs at 281. We also used a resampling approach (Figure S4A) to validate this choice, and examined the effect of changing the SNP order in our heuristic search (Figure S4C and D), and possible lab-genotyping bias (Figure S4B).

concordance suggests that the data are reliable (Figure S5). Intensity plots of the 281 SNPs are available from www.bumc.bu. edu/centenarian. 137 SNPs of the 281 SNPs occur in 130 genes, some of which have been previously associated with aging such as LMNA (rs915179), WRN (rs1800392), and SOD2 (rs2758331) and several of them are in close proximity of coding SNPs [45]. The LMNA gene, which encodes the nuclear envelope proteins lamin A and lamin C, has been associated with the progeroid (premature aging-like) syndrome, Hutchinson-Gilford syndrome [46]. The WRN gene is a DNA helicase and exonuclease that plays a deterministic role in DNA repair and another progeroid syndrome, Werner’s Syndrome [47]. The WRN gene has been associated with longevity in the Framingham Heart Study (FHS) sample [48]. It is remarkable that the two genes responsible for the best known progeroid syndromes appear in the genetic risk model, and this may reflect the power of the discovery sample which includes such extreme old ages. Another gene, also noted to be associated with longevity in the FHS sample as well as the Jerusalem Study, is SOD2, or superoxide dismutase 2 [49]. SOD2 is a key free radical scavenger and free radical damage likely plays an

Table S1 provides complete details of all of the 281 SNPs, and the probabilities that are used to compute the prediction using the formula in Figure 4A. Reliability of the Illumina genotyping was double-checked by re-genotyping the top 28 SNPs of the model

Table 1. Replication of the association of rs207650 in TOMM40/APOE.

Discovery Set (801, 914) SNP Gene Chrom Alleles LOG10(BF) p-value OR p(A) Discovery Set (801, 914) rs2075650 TOMM40/APOE chr19:50087459 AG/GG v AA 6.31 1.03E-08 0.49 0.15/0.26 Replication Set (Elix 253, 341) 2.04 0.000468 0.47 0.15/0.27 Combined (1054, 1255) 9.30 1.01E-11 0.48 0.15/0.26 Coriell (801, 867) 3.73 3.86E-06 0.55 0.15/0.24

The table shows the replicated associations of the SNP rs207650 in TOMM40/APOE in the replication set 1 and the additional control set from the Parkinson’s Disease study. Column legends: SNP=official dbSNP identifier. Gene=official gene name for SNPs that are within 20 kb from transcribed regions. Chrom=Chromosome and physical position of SNP in hg18. Alleles=the two SNP alleles (allele 1 v allele 2) in the genetic model that reached strongest significance in the Bayesian analysis. LOG10(BF)=the logarithm 10 Bayes Factor for the association relative to the null model of no association. Assuming uniform prior probabilities for the two hypotheses, the BF represents the posterior odds for association. P-value=p-value for 1 degree of freedom test for the dominant model AG/GG versus AA. OR=odds ratio for exceptional longevity in subjects who carry allele 1 relative to allele 2. For example, subjects who carry the allele 1 (AG/GG) of SNP rs2075650 have 0.49 times the odds for exceptional longevity compared to subjects who carry the allele 2 (AG/GG: either the genotype AG or GG). P(A)=prevalence of allele 1 in cases and controls. For example, 15% of centenarians carry the allele AG/GG of SNP rs2075650 compared to 26% of controls. Row 1 shows the results in the discovery set; row 2 in the ELIX set, row 3 the combined discovery and ELIX datasets and row 4 is the set in which the 914 matched controls of the discovery set were replaced with the unmatched Coriel controls.

1) Order SNPs by maximum Bayes Factor,

2) Build nested SNP sets to predict exceptionallongevity (EL) and average longevity (AL). Use Bayes’theorem to update the prior probability p(EL) into theposterior probability of EL, given each SNP set.

1.002 04 06 08B) Accuraicv of nested models    —— specificity~~ sensitivity     0 100200 300 400

3) Compute the posterior probability of test subjects,given their genetic profile. Label the predicted subjects’outcome as ELif p(EL] ©,)>p{AL] =,).