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Like most complex phenotypes, exceptional longevity is thought to reflect a combined influence of environmental (e.g., lifestyle choices, where we live) and genetic factors. To explore the genetic contribution, we undertook a genome-wide association study of exceptional longevity in 801 centenarians (median age at death 104 years) and 914 genetically matched healthy controls. Using these data, we built a genetic model that includes 281 single nucleotide polymorphisms (SNPs) and discriminated between cases and controls of the discovery set with 89% sensitivity and specificity, and with 58% specificity and 60% sensitivity in an independent cohort of 341 controls and 253 genetically matched nonagenarians and centenarians (median age 100 years). Consistent with the hypothesis that the genetic contribution is largest with the oldest ages, the sensitivity of the model increased in the independent cohort with older and older ages (71% to classify subjects with an age at death>102 and 85% to classify subjects with an age at death>105). For further validation, we applied the model to an additional, unmatched 60 centenarians (median age 107 years) resulting in 78% sensitivity, and 2863 unmatched controls with 61% specificity. The 281 SNPs include the SNP rs2075650 in TOMM40/APOE that reached irrefutable genome wide significance (posterior probability of association = 1) and replicated in the independent cohort. Removal of this SNP from the model reduced the accuracy by only 1%. Further in-silico analysis suggests that 90% of centenarians can be grouped into clusters characterized by different ''genetic signatures'' of varying predictive values for exceptional longevity. The correlation between 3 signatures and 3 different life spans was replicated in the combined replication sets. The different signatures may help dissect this complex phenotype into sub-phenotypes of exceptional longevity. [ABSTRACT FROM AUTHOR]

Motivation: Although several studies have used Bayesian classifiers for risk prediction using genome-wide single nucleotide polymorphism (SNP) datasets, no software can efficiently perform these analyses on massive genetic datasets and can accommodate multiple traits.Results: We describe the program PleioGRiP that performs a genome-wide Bayesian model search to identify SNPs associated with a discrete phenotype and uses SNPs ranked by Bayes factor to produce nested Bayesian classifiers. These classifiers can be used for genetic risk prediction, either selecting the classifier with optimal number of features or using an ensemble of classifiers. In addition, PleioGRiP implements an extension to the Bayesian search and classification and can search for pleiotropic relationships in which SNPs are simultaneosly associated with two or more distinct phenotypes. These relationships can be used to generate connected Bayesian classifiers to predict the phenotype of interest either using genetic data alone or in combination with the secondary phenotype(s).Availability: PleioGRiP is implemented in Java, and it is available from http://hdl.handle.net/2144/4367.Contact: stephen.hartley@nih.govSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]