Taking into account that about 75% of phenotypic varia- tion in life span could be due to nongenetic factors, and that these factors likely influence traits not directly but through genes involved in various pathways, one should not expect to exactly replicate the effect of a specific SNP allele in other populations. However, the fact that ‘‘longevity’’ or ‘‘aging’’ genes identified in different studies often represent the same molecular function, pathway, cell response, or biological process indicates that it may be reasonable to respectively address the issue of replication of gene-longevity associa- tions,39 i.e., to do it on the level of gene functions, pathways, and their outcomes, rather than on the level of individual SNPs or genes.

The set of I-longevity alleles selected in these analyses differs from that selected in our earlier paper,10 although these sets have nine genetic variants in common. This is likely to be due to the differences in approaches to selecting genetic variants associated with life span.

Conclusions

The results of this study stress the importance of using several statistical approaches for identifying alleles in GWAS of complex traits, as well as the importance of using estab- lished metabolic pathways involved in regulation of these traits for confirming research findings. The latter creates an important link between population-based GWAS and the results of experimental studies focused on verifying molec- ular biological mechanisms, metabolic pathways, and pro- tein interaction networks affecting phenotypic traits.

The longevity alleles selected in this study differ from those selected in the studies of Lunetta et al.4 and Newman et al.6 discussed above. As results of our study show, one possible reason for this could be differences in the allele se- lection procedures. The control for observed covariates was also different. In contrast to Newman et al.,6 we did not control for observed covariates that could be considered as mediators of genetic influence on life span (e.g., physiological variables) in our study. Although controlling for such cov- ariates may reduce effects of nongenetic factors affecting life span through these covariates, it may also reduce the num- ber of selected genetic variants affecting this trait (see also Supplemental Data).

The fact that the majority of detected SNPs were located in genes and were intronic indicates a potentially pivotal role of changes in intronic enhancers and in gene/protein expres- sion (not related to the changes in protein structure) in achieving longevity, as compared to the role of the changes in coding exons. This hypothesis needs further testing.

The SNPs identified in our study do not have exact overlaps with the SNPs found in other recent GWAS of longevity.5,7,18 While recognizing all the possible statistical reasons for such lack of replication (discussed above), it is important to stress that on the level of gene functions, pathways, cell responses, and health effects, in which the identified SNPs or related genes are involved, there are clear overlaps across respective studies, as well as with many other (not genome-wide) genetic association studies of aging and longevity (Table 4). For example, rs2024714 is one of the 27 I-longevity SNPs, which is located in the intronic region of CDH4, a cell adhesion gene involved in brain information processing and tumor suppression. In an earlier GWAS conducted by Framingham researchers using the Affymetrix 100K SNP array, a polymorphism (rs1970546) in the same CDH4 gene (and also located in the intronic region of this gene) was found to be significantly (p =3.7· 10-8) associated with total cerebral brain volume used in that study as en- dophenotype of brain aging.17 Another example: Several of the 27 SNPs from our study are located within or close to genes involved in regulating cell proliferation (e.g., RAC2, TGF-a). Similarly, in Newman et al.,6 a gene (MINPP1) linked to one of the identified longevity SNPs with highest

We also found that longevity genes from the intersecting set are not random, but are largely involved in regulating aging and cancer, as well as brain aging and brain disorders. Discovering the functional relevance of these genes to aging and major diseases supports a causal relationship underlying associations between the identified SNPs and life span, and reduces the chances for these alleles to be false positives.

Although the available information on functional effects of the detected longevity alleles is currently not comprehensive enough to draw conclusion about precise mechanisms of their influence on life span (e.g., it is in most cases unclear if the effects of these alleles result in up- or downregulating respective genes/proteins/pathways), discussing these mechanisms is necessary for formulating priority questions that need to be addressed in further studies, e.g., about rel- ative contribution of genetically slower aging versus lower disease risks for achieving longevity, possibility of pleiotro- pic effects (e.g., trade-offs) of the same genetic factor on aging and diseases, as well as on different diseases.16 It is also crucial to connect the results of GWAS with the results of analyses of expression of respective genes and proteins.

Marked similarities in functions and phenotypic effects of many genes found in our and other genetic associations studies of aging/longevity indicate that replication of the results of genetic association studies can be achieved by us- ing available information about the involvement of respec- tive genes in certain pathways, cell responses, physiological processes, and health disorders.40

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