Lunetta et al.4 performed GWAS using genetic data on 100K SNPs (Affymetrix 100K GeneChip) collected in partic- ipants of the FHS Original and Offspring cohorts. The Cox proportional hazards model was used to perform the re- gression analysis of survival times from age at study entry to age at death. Models were adjusted for a number of observed covariates, including birth cohorts and behavioral and physiological characteristics. The authors concluded that longevity and aging traits are associated with a number of SNPs; however, none of the associations achieved genome- wide significance.

Newman et al.6 performed a meta-analysis of GWAS of longevity (defined as survival to age 90 years or older) in Caucasians from four prospective cohort studies. The au- thors found 273 SNPs associations with p< 0.0001, but none reached the prespecified significance level of 5· 10-8. The authors concluded that survival studies of larger size or more extreme or specific phenotypes may support or refine their findings.

Note that replicating GWAS findings in a new study with an independent population might be a less reliable and cost- ineffective method of confirming true positive associations compared to testing the roles of the detected genes in known metabolic pathways. Indeed, with heritability in life span about 25%, the environmental factors contribute up to 75% of variability in this trait. Because environmental factors influ- ence life span by activating appropriate genes from meta- bolic pathways involved in the regulation of aging, health, and longevity, distinct conditions might activate different genes from the same or different pathways. Therefore, it is likely that different sets of SNP alleles will be associated with life span in the two independent populations, reducing chances to replicate GWAS findings in independent popu- lations. This conclusion is illustrated by the results of two recent GWAS produced by established research groups, where none of the previously identified longevity genes were found.29,30 Does it mean that earlier genetic studies of lon- gevity produced false-positive results? Not necessarily. It is most likely that the idea of replication using independent population does not fully correspond to the multi-factorial nature of complex traits.

Taking into account the difficulties in detecting individual longevity alleles with significant effects using traditional GWAS, as well as the results of earlier studies evaluating narrow-sense heritability of longevity between 15% and 30%,10,31,32 we recently tested the presence of the additive genetic influence on life span, using the FHS data.10 Such influence was demonstrated for other complex traits, such as height.3 To select respective alleles, Yashin et al.10 used a linear regression model without observed covariates and demonstrated that significant additive effects of common SNPs on life span do exist. The results of our analyses showed that detected sets of genetic variants depend on the statistical model used in allele selection procedure, but also have many genetic variants in common. These analyses also showed that SNP alleles from each of the six sets have sub- stantial joint genetic influence on life span, which was also highly statistically significant. Thus, our study demonstrated that six different methods used in the allele selection proce- dure lead to the same conclusion: A substantial portion of variability in human life span is associated with the additive genetic component. This conclusion is in concert with the results of recent studies of genetic contribution to variability of complex traits.33

It is important to point out that the selected 27 longevity SNPs do not exhaust all SNP variants positively influencing life span in a given dataset. The complexity of life span as a phenotypic trait indicates that many other genes with small effects might be involved in the regulation of life span.

In the pregenomic era, the additive genetic contribution to complex traits was a major focus of genetic studies of such traits. Respective statistical models described phenotypic traits as a sum of independent genetic and environmental

components without interaction. The genetic influence on a trait was characterized by the narrow-sense heritability, i.e., the proportion of phenotypic variance, explained by the additive genetic component. Although, using today’s knowledge about phenotypes (e.g., life span) and factors affecting them, mathematical models used for heritability calculations look oversimplified, many conclusions derived from them have important practical meaning. For example, the estimates of heritability have been used to measure se- lection rate in breeding.34 The presence of additive genetic effects on phenotypic traits was also one of the key as- sumptions in evolutionary models of natural selection.35 However, despite recognition of the importance of additive genetic components in phenotypic variation of complex traits, and a number of publications33,36–38 clearly demon- strating the possibility of estimating this component from available genome-wide SNP data, as well as the benefits of using the estimate in analyses of genetic influence on com- plex traits (e.g., risks of diseases), the potential of this ap- proach for studying life span, ages at disease onset, or other health-related durations remain largely underused.

significance level (rs9664222, 6.77·10-7) is also involved in regulating cell proliferation (Table 4). In a more recent meta- analysis of nine GWA studies by Walter et al.,18 the authors identified 14 SNPs associated with time to death with a conventional level of significance (p <10-5), and also 862 SNPs with p <10-3, and evaluated biological processes as- sociated with genes closest to these SNPs. Similar to our study, cell adhesion and neuronal activities (e.g., involving NCAM1 and CACNA1C genes) were overrepresented among the relevant processes (Table 4).