In conclusion, though living to very old age runs strongly in families, it is a complex phenomenon with many different patterns of survival that include disease-free survival but also survival with various age-related diseases. The study developed an approach that uses genetic risk profiles to predict exceptional longevity, and the results obtained were informative and can be used as a basis for further research.in combination, rather than the absence of disease-associated variants. Our study has some limitations, including the use of heuristic rules in our genetic risk modeling approach, and the fact that our sample only includes Caucasian subjects, which limits the generalizability of our findings to other racial groups. However, our analysis provides novel insights into the genetics of exceptional longevity, including the identification of 130 genes implicated in this complex phenotype and the correlation of different genetic signatures with different patterns of survival. We hope that our approach may prove useful in dissecting other complex genetic traits and that future studies will replicate and expand upon our findings.In our analysis, we found specific genetic signatures that counteract the effects of disease-risk alleles and contribute to compressing morbidity and disability towards the end of very long lives [43]. We also observed that certain signatures correlated with the prevalence and age of onset of some age-related diseases, and more research is needed to understand how and why they predispose for exceptional longevity and different patterns of aging. These genetic signatures were created using an ensemble of genetic risk models, which showed high sensitivity in predicting exceptional longevity even without knowledge of other risk factors. This high sensitivity aligns with theoretical results indicating high predictability of rare and highly heritable traits, even when only 50% of the genetic variants that determine the trait are found [36], and with the accuracy of genetic risk models developed to predict complex and highly heritable traits [37-41].

To quantify the amount of genetic variance in liability to exceptional longevity explained by our model, we used an online calculator (http://gump.qimr.edu.au/genroc/) to translate the predictive accuracy measured by the AUC into a proportion of explained genetic variance on the liability scale [36]. Based on previous reports and the latest US 2010 Census (http://www.census.gov/prod/cen2010/briefs/c2010br-03.pdf), we estimated that exceptional longevity (living to 100+) has a prevalence of 1 in every 5,000 people, while the sibling relative risk for exceptional longevity ranges between 8 and 17 [9,10]. Using these numbers, we estimated that a maximum AUC for a genetic model of exceptional longevity ranges between 0.95 to 0.98. Our genetic model reached an AUC of 0.74 in the second replication set (Figure S7), explaining between 12% to 17% of the genetic variance on the liability scale. In the ELIXIR replication set, the AUC of our genetic risk model is 0.58, which would represent 12% of explained genetic variance. Since the ELIXIR set includes more nonagenarians than centenarians, and their prevalence in the population is 0.5% with a sibling relative risk of approximately 2.5, we repeated the calculations in this scenario. The 0.58 AUC translated into approximately 4% of the genetic variance on the liability scale. These results indicate that although we explained a good amount of genetic variability on the liability scale for living to very old ages, there is still more than 80% missing heritability that needs to be explained through more comprehensive genetic studies.

The NECS began in 1994 as a population-based study of all centenarians living within eight towns in the Boston area [81]. Since 2000, the NECS has expanded enrollment to include centenarians from throughout the USA (www.bumc.bu.edu/centenarian). Potential subjects are ascertained via voting records and media alerts. Subjects are sent a demographic data, lifestyle choices, medical history and functional status questionnaire, family pedigree form, and blood kit. A dementia scale test is administered over the telephone. The study is still actively recruiting centenarians with an average of 50 subjects enrolled per year. The NECS controls consist of approximately 450 referent subjects comprised of spouses of centenarian offspring and children of parents who died at the mean age of 73 years with an age at enrollment ranging between 53 and 90 years.

The specificity of our classification rule is 60-61% in the independent sets and is comparable to other genetic studies of complex traits [76-78]. Although better than random, this specificity would not be useful as a diagnostic test. The decreased specificity in this study could be due to the fact that control subjects from the Illumina database are primarily healthy controls used for other genome-wide association studies, and therefore the control data set may be enriched for healthy aging subjects. We identified 3,613 Caucasian healthy controls from the Illumina control database (iControlDB, http://www.illumina.com/downloads/PurposeDocument.pdf). No phenotypic information is available for subjects selected from the Illumina repository except for gender (<60% females) and age at blood draw for some subjects (age range 0-75 years). The Coriell NINDS control sample in the Parkinson's disease (PD) set is described elsewhere [21]. Subjects from these studies were combined to generate a discovery and replication set using genetic matching (see below) and an additional replication set in which subjects were not genetically matched.