About 9,300 individuals from all generations of the FHS were genotyped for about 550,000 SNPs using Affymetrix 500K (genome-wide) and 50K (enriched with SNPs within genes) mapping arrays, overall representing a significant part of human genome variability. On average, 40% of SNPs were located within genes in the combined 550K set. In this study, we used information about 1,471 genotyped participants of the FHS Original cohort who passed quality control (QCsee that all seven sets of longevity alleles have a significant effect on life span (p<0.001). The polygenic score index for each set of alleles is positively associated with life span, indicating that the more longevity alleles an individual has, the longer their life span is likely to be. The effect sizes of the different sets of alleles vary, with the I-longevity set having the largest effect size (β=0.138), followed by the GEE-longevity set (β=0.135) and the N-longevity set (β=0.125). The smallest effect size was observed for the L-longevity set (β=0.045). These results suggest that multiple genetic variants contribute to life span and that their joint effect can be detected using polygenic scores.signal, the enhancer can increase transcription of the gene containing the intron. 

The review of biological functions of genes closest to the 27 I-longevity SNPs revealed that they are involved in a variety of processes related to aging and health. The most common processes were related to metabolism, including energy metabolism, lipid metabolism, and glucose metabolism. Other processes included neuronal function (such as synaptic plasticity and memory formation), 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 cardiovascular diseases and asthma.

Overall, the findings suggest that the 27 I-longevity alleles jointly influence life span and have potential functional significance in physiological aging and age-related diseases. The fact that the majority of these SNPs are located within genes, particularly intronic regions, supports their potential functional relevance. The biological functions of genes closest to these SNPs are involved in a variety of processes related to aging and health, including metabolism, neuronal function, inflammation, detoxification, and replicative senescence. The associations with specific health outcomes, particularly cancer and brain disorders, suggest that these genes may be important targets for future research on aging and age-related diseases.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 intronic enhancers may potentially significantly affect the balance of protein concentrations, without any changes in the protein structures, thus providing a possible mechanism for their influence on lifespan. To see if this is the case, it is necessary to connect the results of SNP genotyping with the results of the analyses of expression of respective genes and proteins in a further study. Currently, there is indirect evidence in support of such a 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 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 longevity SNPs revealed that biological processes most often associated 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 lifespan: A closer look at findings shown in Tables 3 and 4 allows clarification of important features of mechanisms connecting genes and lifespan. The detected genes are typically multifunctional, 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 lifespan. 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 key players in physiological aging and linked to the decline in stress resistance. The pathways regulating growth/proliferation and apoptosis are known to be deeply interacting, so that the collective effects of genes involved in relevant pathways on lifespan could be expected. Also, a substantial number of the genes closest to longevity 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 surprisingly large (22% of all longevity SNPs and 30% of the longevity SNPs located within genes). One of the genes (CDH4) was also identified in an earlier GWAS of brain aging involving Framingham participants. Another one (NCAM2) is a close relative of the NCAM1 neural adhesion gene featured in pathway analysis in the recent GWAS of lifespan by Walter et al.