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

Figure 4. A) Schematic illustration of the genetic risk prediction model. We ordered SNPs by maximum Bayes Factor in the discovery set and built nested SNP sets starting with the most significant SNP and then adding one SNP at a time from the ordered list. The conditional probabilities of SNP genotypes in centenarians (p(SNP|EL)) and controls (p(SNP|AL)) are used to compute the posterior probability of exceptional longevity (p(EL|S )) i i k using Bayes’ theorem and prior probability p(EL)=0.5. The classification rule is the standard Bayesian classification rule that is optimal under a 0–1 loss function. B) Sensitivity and specificity of 400 nested models. The x-axis reports the number of SNPs in each of the nested models, and the y-axis reports sensitivity (% of centenarians with posterior probability of exceptional longevity.posterior probability of average longevity) and specificity (% of controls with posterior probability of exceptional longevity,posterior probability of average longevity). doi:10.1371/journal.pone.0029848.g004

important pathogenic role in aging and numerous age-related diseases [50]. CDKN2A (rs1063192) performs a key step in the p53 pathway that has been posited to play a key role in inducing cellular senescence [51] and it has been associated with adult onset diabetes [52]. SORCS1 (rs7907713) and SORCS2 (rs6812745) have been linked AD [53]. Gastric inhibitory polypeptide (GIP), commonly referred to as glucose-dependent insulinotropic peptide, encodes a protein that regulates insulin secretion and activates AKT [54]. The association of this gene (rs9899404) supports the potential role of insulin regulation in exceptional longevity [55], and suggests new target genes for human aging beyond FOXO1, FOXO3A and IGF-IR [56,57,58]. There is also growing evidence of GIP playing a protective role in both diabetes and AD and GIP is being investigated as a therapeutic target [59].

amongst centenarians (average age of onset, 93 years) [60]. Genes related to other age related diseases were also significantly represented: 24 genes were linked to coronary artery disease (Figure 5), and several genes were linked to neoplasms.

better understand the role of these 281 SNPs in shaping the genetic susceptibility to exceptional longevity, we generated a genetic risk profile for each subject by plotting the posterior probability of exceptional longevity (p(EL|S ), y axis) against the k number of SNPs in each of the 281 SNP sets S (x-axis) and k examined their patterns. Figure 6 shows, for example, the profiles from 3 centenarians and a control. In each profile, an increasing posterior probability of exceptional longevity shows strong enrichment of longevity associated variants, because the posterior probability of exceptional longevity increases when the profile includes a new SNP genotype that is more frequent in centenarians than in controls (see methods).

We used Genomatix (http://www.genomatix.de) to annotate the list of 130 genes included in the genetic risk model and the analysis showed that the list was enriched for several groups of genes linked to both common and rare diseases (MeSH). Genes related to Alzheimer’s disease, dementia and tauopathies were the most significant: 38 of the 130 genes were linked to AD in the literature (p-value to test the null hypothesis that this happens by chance was 6.17 e-7) and they are displayed in Figure 5; 42 genes were linked to dementia (Figure S6, p-value to test the null hypothesis that this happens by chance was 1.07 e-6) and 38 to tauopathies (p-value 8.47e-7). The fact that so many genes are noted to play a role in dementia is consistent with the epidemiologic finding that dementia is absent or markedly delayed

These examples support the hypothesis that exceptional longevity is determined by varying combinations of longevity associated variants and some number of SNPs may be optimal for classifying some subjects but not others. Consistent with this observation, we choose an ensemble of all 281 genetic risk models to compute the posterior probability of exceptional longevity. This ensemble of 281 genetic risk models provides 89% specificity and sensitivity in the discovery set (Figure 7A). We next evaluated the predictive accuracy of this ensemble of models in the two replication sets, the ELIX set and a recently enrolled sample of NECS centenarians.

Alzheimer’s disease: 38 genes in 130 (p-value 6.17 e -7)receptor\\_ transporterAssociation by co-citationAssociation by expert-curationActivation—>oyinhibitionModulation——camnoion—

Figure 5. Genes in the genetic risk models have been linked to coronary artery disease and Alzheimer’s disease. The two networks display 38 of the 130 genes in the genetic risk model that are linked to Alzheimer’s disease (top) and 24 of the 130 genes that are linked to coronary artery disease (bottom) in the literature, either by functional or genetic association studies. The nodes that are linked by an edge represents either genes that are ‘‘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 AD/CAD in the literature but not together with other genes. The number of genes linked to each disease was compared to what is expected by chance using Fisher exact test, and the p-values show that the gene seta are unluckily the result of chance. (Networks generated with Genomatix). doi:10.1371/journal.pone.0029848.g005