So today I was going to just look at mTOR in its own right, encoded by the FRAP1 gene. Wikipedia has a page on it - quite detailed - so I won't reinvent the describtive wheel. There's also a nice, though date, review here (pdf). It is probably at the heart of all this but instead the population genetics of aging is an interesting area to look at.
Someone must have looked at SNP allele frequencies in long lived vs. short lived people or something...? Lo' and behold, a pubmed search of "FRAP1 aging" gives a paper called "Genetic variation in healthy oldest-old", published in PLOS. Great!
Small sample size, but it's a start: 47 > 85-year olds, followed by deep re-sequencing across functionally important regions of 24 candidate genes, as they write, genes implicated in "dietary restriction (PPARG, PPARGC1A, SIRT1, SIRT3, UCP2, UCP3), metabolism (IGF1R, APOB, SCD), autophagy (BECN1, FRAP1), stem cell activation (NOTCH1, DLL1), tumor suppression (TP53, CDKN2A, ING1), DNA methylation (TRDMT1, DNMT3A, DNMT3B) Progeria syndromes (LMNA, ZMPSTE24, KL) and stress response (CRYAB, HSPB2)".
You can see FRAP1 in there, the gene coding for mTOR.
So it seems they have catalogued a fair number of variations across these genes - 848 single nucleotide polymorphisms and 87 insertion/deletion polymorphisms. It's a good start. As they say "As a first step towards investigating the effects of genetic variation in aging-related genes on human lifespan and health, we characterized genetic variation in healthy oldest-old."
Clearly, a number of these variations are likely to have an affect on aging. Perhaps they are protective, or alternativly their absence migh increase susceptibility.
An interesting note is that they also look at HapMap. HapMap (see website) is a widely used reference panel of human genomic variation. What the authors say is that "only 12% of variants (179/1550) are shared between available HapMap SNPs and sequencing data generated from our healthy oldest-old". They don't comment on why. One reason could be that these variants are specific to their "healthy old" population, which are probably therefore a unique population, i.e. in a typical population not nearly as many people would live to be as healthy and as old.
The interactions between SNPs would also be very cool to look at - what combinations of SNPs might increase risk? (a) more samples are needed, (b) a matched "control" (old but with diseases) might also be relevant.
But it's a good start!
2016 Year in Review
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