that includes principal components from genome-wide principal component analysis would not be useful for prediction because the values of the principal components for new subjects would be missing. Our analysis does not show any systematic difference between results in the controls genotyped in our lab compared to healthy controls genotyped elsewhere (Figures 5 and 8). Also, additional analyses using traditional principal-components ap- proaches to control for population stratification suggest that no residual stratification is likely to confound the associations (Figures S9 and S10). However, only replication of these results in independent data from comparably old subjects by independent

domains, they may want to conduct similar to evaluate whether the same heuristics lead to

In our study we included only Caucasian subjects and the to which this analysis applies to other racial groups is an question.

A major challenge we faced with our genome-wide association study was the choice of appropriate controls. Because of the limited number of controls in the NECS, we had to resort to healthy controls from other genome-wide association studies (the Illumina control data set and the NECS controls where genotype data were generated in different labs with different SNP arrays) as other investigators have done [64]. Our stringent quality control approach and the genetic matching minimized the number of false positive associations, likely at the expense of missing some true positive associations. We decided to use genetic matching to reduce the effect of population stratification because our initial genome-wide association study that included all control subjects from the Illumina repository had a genomic control factor .1.3 suggesting substantial population stratification between cases and controls. Simulation studies that we published in [65] showed that matching is a good way to remove the effect of stratification

Novel insights about the genetics of exceptional longevity

The large number of SNPs in our genetic risk model and the variety of genetic signatures confirm that exceptional longevity is influenced by the combined effects of a large number of SNPs. The genetic risk model implicates 130 genes, most of them known to play a role in various disease mechanisms (Figure 5), and our findings suggest that different variants of these genes may have a protective role. The most intriguing examples are LMNA and WRN: while specific variants of these two genes determine progeria and accelerated aging, alternative variants may increase life span. About 50% of the SNPs in the genetic risk model are in intragenic regions and this also suggests that regulatory mecha- nisms play an important role in exceptional longevity. We also found that the sensitivity of the prediction in independent sets

wa ptgwa. ptgwa.atwadgwa.cegwa.cecodcodallall     

Figure 10. Distribution of risk alleles of 1214 SNPs in 1054 centenarians (red) and 4118 controls (blue). Risk alleles were derived from the GWAS catalogue at the NHGRI (downloaded in April 2011) and the Human Genome Mutation Database. The boxplots displays the rate of risk alleles carried by centenarians (red) and controls (blue). The disease described are: lupus, cholesterol level (Chol), macular degeneration (MD), Parkinson’s Disease (PD), Chron’s disease (chr), diabetes (diab), cardiovascular disease (CVD), cance (canc)r, Alzheimer’s (AD), GWAS.pt is the group of alleles related to personality disorders that were found in GWAS, gwas.qt is the group of alleles related to QTL from GWASs and include cholesterol, BMI, obesity etc, and GWAS.cc is the group of risk alleles found from case/control GWASs so include for example cancer, PD, MD etc, cod is for coding variants from the HGMD, and all is the full set of 1214 variants. Table S3 reports the actual rates.

increases with the ages of centenarians, and therefore likely, the genetic contribution to lifespan increases with increasing ages of

The NECS previously showed that centenarians fall into different groups in terms of age of onset of age-related diseases: survivors (onset of aging disease #80 years), delayers (onset of aging disease between 80 and 100 years) and escapers (age of onset $100 years) [67]. This current analysis now shows that some of the centenarians carry genetic signatures that correlate with different ages of survival and suggests that the complexity of aging and the different patterns of survival to the age of 100 and older may be the result of different genetic profiles. Unlike the typical approach of finding individuals with a specific phenotype in common and then performing a genetic association study to discover genetic associations with the trait, our approach tries to dissect a complex phenotype into sub-phenotypes based on the genetic data. Our analysis is preliminary, based on small a sample, and needs to be replicated but we hope that this new approach may prove useful in dissecting other complex genetic traits [68].