notes: We captured 43 of 43 alleles at r2 ≥ .8, ie, we captured 100% of alleles and mean of maximum r2 was .91 for the seven tagSNPs. CI = confidence interval;

Table 5. Frequency of Minor Allele of Each Tagging SNP of RPS6Ka1 on Chromosome 1p36.11 in Long-Lived Case vs Control Participants

Total Number of Minor Allele Frequency of SNP ID Position Cases and Controls Cases and Controls, % (95% CI) χ2 p Value rs12094989 26720973 184/358 27.2 and 30.0 (–0.084 to 0.029) 1.72 .42 rs17162190 26745419 177/346 27.4 and 31.4 (–0.097 to 0.019) 1.70 .42 rs2278978 26745832 183/355 23.0 and 24.9 (–0.073 to 0.034) 3.10 .22 rs4970510 26783341 180/346 13.6 and 14.6 (–0.053 to 0.035) 0.21 .90

notes: The cases and controls used for the RPS6Ka1 study were a subset described previously (5). We captured 24 of 24 alleles at r2 ≥ .8, ie, we captured 100% of SNPs, and mean of maximum r2 was .94 for the four tagSNPs. CI = confidence interval; SNP = single-nucleotide polymorphism.

common genetic variation in these genes in the etiology of achieving extreme old age, at least in the specific population tested. The population used for the present study has proven capable of detecting genetic association with extreme old age in the case of FOXO3 (5), thereby demonstrating it has the capacity to detect an association should one exist, at least for common variants (minor allele frequency ≥10%) as used in the present study. Nevertheless, the possibility

SNPs with odds ratios of 2.0 and greater are expected to be significant at the .05 level in our study, there were actually slightly fewer significant results than expected if genetic variants had no effect.

Because the tagging SNPs we selected provided mal coverage of genetic variability across each gene 5 kb of 5′ and 5 kb of 3′ DNA, taking advantage of disequilibrium, our data appear to exclude a major effect

remains for the existence of one or more polymorphisms not in linkage disequilibrium with the SNPs tested or of population-specific variants that could be associated with longevity not present in the current population.

pathway between their long-lived subjects and controls (38). Although there was no difference in expression of the longevity-associated gene FoXo3, nor of MToR, RicToR, and RPS6Ka1, they did find significantly lower expression of RPToR in both long-lived cases and their offspring. Six other genes also exhibited either higher or lower expres- sion in the long-lived group of subjects. Although it might be possible that some of the expression differences could be caused by polymorphisms in, say, the promoter of these seven genes, they could instead be downstream conse- quences of genetic or other influences on transcription or posttranscriptional mechanisms of the seven genes.

dated for genetic variants and human longevity (34–36). The two genes that have received widespread replication are aPoe and FoXo3, each having been associated with human longevity in more than 10 independent populations and under different study conditions (37). Several other genes have had several replications, and a limited number of others have had one to two replications (37). This paucity of data reflects, in part, the difficulty of obtaining robust populations of suitable study participants who have attained extreme old age and the complexity of human longevity as a phenotype. Therefore, any data, whether positive or nega- tive, is instructive in deciphering the molecular genetic eti- ology of human longevity.

ciated with human longevity, we hypothesized that there may be association with aging-related phenotypes. After correction for multiple testing, no significant association was, however, found between the various genotypes and 40 aging-related biologic, clinical, and functional phenotypes.

from http://biomedgerontology.oxfordjournals.org/ at Selcuk University on January 30, 2015

The only other comprehensive genetic study of MToR, RPToR, RicToR, and RPS6Ka1 in human longev- ity involved a population of 417 elderly subjects in the Netherlands (38). Genotype data for SNPs in these genes failed to find any association with longevity. However, this study cannot be directly compared with ours for the fol- lowing reasons: (i) The tagSNPs selected did not provide complete coverage of every gene. (ii) Both the cases (aged >89 years) and controls (aged 55–60 years) were much younger. The relative “youth” of their elderly cohort may explain why no association of variation in FoXo3 was found with longevity in this cohort (38,39), whereas in our cohort, with participants aged older than 95 years (5), a highly significant association was found. The latter finding has been replicated consistently in geographically diverse populations of long-lived cases (generally nonagenarian and centenarians) worldwide (37). The inability to detect an association may be due to the fact that some longevity-asso- ciated alleles become markedly more enriched as the age of longevity cases becomes greater. Although the difference between age 89 years and age 95 years may seem small, only 32% of typical 89-year-old people achieve the age of 95 years and thus the latter age is a much more extreme phe- notype (40). This has important consequences for studies of human longevity. For example, the minor allele frequency of the longevity-associated allele of FoXo3 increases by 42% from the octogenarian years to nonagenarian years (5). Allele frequency is a major determinant of statistical power in genetic studies. Thus, the Dutch cohort likely has signifi- cantly less power to detect genetic variants responsible for extreme old age than the current study. (iii) Cases and con- trols were from different cities, although the authors claim population substructure differences were not detected. (iv) Participants were genotyped at different times at differ- ent centers using different Illumina platforms (38,39,41), which could introduce artifacts into the findings. (v) The