To understand mechanisms of the influence of cell adhesion genes on lifespan, it is important to stress that these genes may favor longevity through aging as well as cancer-related processes, and this influence may potentially involve significant tradeoffs; e.g., Hong et al. found that cell adhesion genes in mice were overactivated by the aging process, whereas calorie restriction (the only proved senescence-reversing intervention) downregulated them. In summary, this study provides evidence for a polygenic influence on lifespan with the involvement of multiple biological pathways and functional gene groups.cannot be explained by genetic factors alone; environmental and lifestyle factors also play a significant role. However, the identified genes provide valuable insights into the biological processes that promote longevity and may serve as potential targets for interventions to delay aging and age-related diseases. It is important to note that the functional effects of these genes on longevity are complex and may involve trade-offs between different health outcomes. Further research is needed to fully understand the mechanisms underlying the relationship between these genes, aging, and disease.identified by comparing the trait values in monozygotic and dizygotic twins or in other relatives. However, the advent of genomics has enabled researchers to identify specific genetic variants that are associated with complex traits like life span. This has led to the discovery of several longevity-associated SNPs that have additive effects on life span.

One interesting finding is that individuals who live to a very old age tend to have lower rates of cancer, as reported by Newman et al.6. Additionally, about one-third of the genes closest to these longevity SNPs are involved in brain activities that are highly relevant to brain aging and disorders like Alzheimer's disease, Parkinson's disease, bipolar disorder, and schizophrenia. The mechanisms underlying the prolongevity effects of these genes may involve slower brain aging and decline, as well as trade-offs between genetic factors influencing aging and diseases.

However, there is a risk of false positives when selecting SNP alleles using different statistical models and quality control procedures. Replicating GWAS findings in new studies may also be less reliable and cost-ineffective than testing the roles of detected genes in known metabolic pathways. The multi-factorial nature of complex traits like life span means that different sets of SNP alleles may be associated with life span in different populations.

Despite these challenges, recent studies have demonstrated that a substantial portion of variability in human life span is associated with the additive genetic component. However, it is important to note that the selected longevity SNPs do not exhaust all SNP variants positively influencing life span in a given dataset, and many other genes with small effects may be involved in regulating life span.

In conclusion, while genomics has enabled us to identify specific genetic variants associated with complex traits like life span, it is important to consider the multi-factorial nature of these traits when interpreting GWAS findings and selecting SNP alleles for further study.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., lifespan) 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 selection rate in breeding. The presence of additive genetic effects on phenotypic traits was also one of the key assumptions in evolutionary models of natural selection. However, despite recognition of the importance of additive genetic components in phenotypic variation of complex traits and a number of publications clearly demonstrating 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 complex traits (e.g., risks of diseases), the potential of this approach for studying lifespan, ages at disease onset, or other health-related durations remains largely underused. The significance level (rs9664222, 6.7710-7) is also involved in regulating cell proliferation (Table 4). In a more recent meta-analysis of nine GWA studies by Walter et al., 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 associated 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). Taking into account that about 75% of phenotypic variation in lifespan 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 associations, 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, 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 lifespan.