GEE, Generalized estimation equation; normal, normal linear regression; Cox, Cox regression; logistic, logistic regression; MM, model; GF, gene-frequency method; SNP, single-nucleotide polymorphism.

plasticity, and memory formation). Other (less com- mon) processes were inflammation, detoxification, and replicative senescence. Health outcomes predominantly associated with these genes were cancer and brain disorders. Other (less common and mostly suggestive) associations were with CVDs and asthma.

SNPs. For this, we used peer-review publications available through PubMed, as well as online sources of genetic infor- mation, such as NCBI Entrez SNP, Entrez Gene, GeneCards, OMIM, KEGG, PANTHER, Your Favorite Gene (powered by Ingenuity), and some others. The results are summarized in Tables 3 and 4.

This review resulted in a number of important findings:

The majority (

%) of SNPs from the core set of

SNPs were located within genes, compared to only

% of SNPs in the original

K set. This supports poten- tial functional relevance of these SNPs for achieving longevity.

A total of

% (

of

) of those longevity SNPs that were located within genes are intronic. No longevity SNPs were found in exons. This finding is important because it suggests that the identified genetic effects on longevity might be linked to changes in protein con- centrations rather than to the changes in protein struc- ture. Indeed, introns may perform various jobs, such as alternative splicing or regulation of gene transcription levels with the help of an enhancer located in the intron. The latter is the reason why intronic polymorphisms may have functional effects, even when the introns are not translated. When activated by a transcription factor, the enhancer may increase the level of gene expression several-fold, which in turn will increase the yield of a respective protein. Therefore, polymorphisms in in- tronic enhancers may potentially significantly affect the balance of protein concentrations, without any changes in the protein structures, thus providing the possible mechanism of their influence on life span. To see if this is the case, it is necessary to connect the results of SNP genotyping with the results of the analyses of expres- sion of respective genes and proteins in a further study. Currently, there is indirect evidence in support of such mechanism. One of the authors of this paper (Dr. Wu), in an earlier work on nematode worms,

demonstrated a correlation between levels of gene–protein expression and survival, not related to the changes in gene–protein structure (the worms were genetically identical).

A review of functions and biological effects (known, as well as suggested) of genes closest to the

I-longevity SNPs revealed that biological processes most often as- sociated with these genes are cell growth/proliferation, apoptosis, cell adhesion, and neural activities (such as information processing, response to damage, synaptic

Discussion

Potential mechanisms connecting genes and life span

A closer look on findings shown in Tables 3 and 4 allows for clarifying important features of mechanisms connecting genes and life span. The detected genes are typically multi- functional, with overlapping functions, so that each gene can contribute to several cell functions, and each function is supported by several genes. The latter provides support for the polygenic influence with potentially interchangeable and additive effects of the identified SNPs on life span.

A substantial part of the genes closest to the 27 longevity SNPs (26% of all loci, and 35% of the SNPs located within genes) are involved in regulating cell growth/proliferation and apoptosis (Tables 3 and 4). These cell responses and relevant pathways are thought to be the key players in physiological aging and linked to the decline in stress resis- tance. The pathways regulating growth/proliferation and apoptosis are known to be deeply interacting,15,16 so that the collective effects of genes involved in relevant pathways on life span could be expected.

Also a substantial number of the genes closest to I-lon- gevity SNPs are involved in cell–cell and focal adhesion (CDH4, SVEP1, NCAM2, RAC2, PARVG, RUNX3). The portion of the SNPs related to the adhesion genes was sur- prisingly large (22% of all I-longevity SNPs, and 30% of the I- longevity SNPs located within genes). One of the genes (CDH4) was also identified in an earlier GWAS of brain aging involving Framingham participants.17 Another one (NCAM2) is a close relative of the NCAM1 neural adhesion gene featured in pathway analysis in the recent GWAS of life span by Walter et al.18

To understand mechanisms of the influence of cell adhe- sion genes on life span, it is important to stress that these genes may favor longevity through aging as well as cancer- related processes,19–21 and this influence may potentially involve significant tradeoffs; e.g., Hong et al.19 found that cell adhesion genes in mice were overactivated by the aging process, whereas calorie restriction (the only proved