Summarizing this part of the Discussion, the fact that many of the identified genes participate in regulating both aging and cancer, or both brain aging and brain disorders, raises a question about distinguishing between the aging and disease-related functional effects of SNP alleles that under- line the increases in longevity. Do these alleles favor lon- gevity mainly because they attenuate physiological aging changes in body (and respective decline in stress resistance), or mainly because they help to reduce risks and postpone onsets of common diseases with high fatality rates, such as cancer and AD? They may do both, but there may also be trade-offs. For example, it is well known that risks of many common diseases, including cancer and AD, typically decline at older ages, when the overall risk of death continues to increase.16,23 This indicates that there may be factors, in- cluding genetic ones, that affect the age-associated increase in vulnerability to death due to physiological aging (and related decline in stress resistance), and risks of major dis- eases, differently or even oppositely. For example, van Heemst et al.24 demonstrated that individuals with Pro/Pro genotype of p53 corresponding to a reduced apoptosis in cells had a significant increase in both overall survival (by 41%) and mortality from cancer. In another study, Ørsted et al.25 found that the overall 12-year survival was increased in p53 Pro/Pro versus Arg/Arg homozygotes despite po- tential cancer-favoring properties of the former genotype. Boquoi et al.26 have recently reported that expression of a key tumor suppressor protein p16 (loss of which is associ- ated with many human cancers) induced premature aging in mice via blocking cell proliferation in normal tissues. This

As Table 3 shows, the health effects of the genes related to longevity SNPs focus mostly on cancer and brain disorders. Roughly, about half of all genes in the table are shown to be involved in cancer development, or at least in regulation of highly relevant cell responses (proliferation, senescence, ap- optosis, and adhesion). Therefore, one reason why the joint contribution of respective genes is important for surviving old age could be that certain alleles of these genes, acting together, reduce cancer risk in individuals. Indeed, a ma- jority of long-living people (at least nowadays) are those who have managed to avoid cancer until very old age; e.g., Newman et al.6 have been reported significantly less cancers among those who survived age 90 compared to those who did not.

Finally, about one-third of the genes closest to I-longevity SNPs are involved in brain activities that are highly relevant to brain aging (speed of information processing, brain re- sponse to damage, and memory formation), and also in brain disorders (Alzheimer disease [AD], Parkinson’s disease [PD], bipolar disorder, and schizophrenia), most often through calcium signaling pathways. As Table 3 shows, the possible mechanisms of prolongevity effects of polymorphisms in these genes may involve: (1) Slower brain aging and decline

account the possibility of trade-offs between genetic factors influencing aging and diseases (as well as between diseases), when trying to explain mechanisms of the observed genetic

Moreover, the same gene may play prolongevity or antil- ongevity roles dependent on the functioning of other genes, meaning that its effect may be opposite in the two individ- uals. They may be opposite in the same individual in dif- ferent periods of his/her life and in different environments. Statistical models used in the allele selection procedures es- timate average effect of genetic variants on life span. That is why the estimate of such an effect may be modified when the number of individuals in a sample changes (e.g., as a result of using different quality control procedure, or by adding a portion of newly genotyped individuals to the initial sam- ple). Similarly, the use of different statistical models in the allele selection procedure provides researchers with different approximations of the real connection between genes and life span resulting in different subsets of selected genetic vari- ants.

False positives and replication issues

The large numbers of SNP alleles selected using N-, GEE-, MM-, and C-methods, as well as substantial difference in these numbers, indicate high chances of having false-positive alleles in selected sets. The numbers of selected false-positive alleles depends on the QC procedure. In our study, we used the call rate‡ 80%, HWE> 10-7, and MAF ‡ 1%, which is likely to be not stringent enough. More stringent QC proce- dures (e.g., Fornage et al.27 and O’Seaghdha et al.28) may substantially reduce the number of false positives. Note that intersection of the six sets of selected SNP alleles may sub- stantially reduce the chances of having false positives in the I-set and increase the chances of selecting true positive al- leles. Table S1 in the Supplemental Data shows that 5 out of 27 SNPs (rs16975963, rs1327533, rs432203, rs41383, rs3120819) have a call rate between 80% and 90%, 2 out of remaining 22 SNPs (rs2370413, rs9616906) have a call rate between 90% and 95%, and 3 out of remaining 20 SNPs (rs12623542, rs13008689, rs1834497) have a call rate between 95% and 97%. The fact that 17 out of 27 selected SNPs passed through the most stringent QC with the call rate of 97% indicates high chances for them to be true positive alleles. It is important to note that the 27 SNPs selected in our paper passed through additional stringent QC procedure (which was not used in the studies discussed above). They have been selected in each of six different procedures based on different statistical models (see Fig. S6 and Fig. S7 in Sup- plemental Data concerning additional properties of selected