I’m going through a bunch of comparative transcriptomics & genomics papers purporting to find genes associated with aging by looking at either which genes are overexpressed in young vs old animals, or highly mutated between long-lived vs short-lived species.
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By contrast, we do know a cluster of genes which, when knocked out, reliably extend life & prevent some age-related dysfunctions in multiple model organisms, namely the insulin/IGF/GH pathway.
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Of course, it would be prohibitively expensive to knock out every possible gene in a mouse and see if it lives longer. So we understandably don’t have many of these examples validated for lifespan in mammals. (There are more examples of “knockout rescues disease model.”)
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Maybe the problem with the transcriptomics is study design? N=16 biopsies, list all genes with p<0.05 difference between old & young transcript levels, is pretty typical, and maybe that’s just too much noise?
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Ok, not “maybe”, it is too much noise. I’ll add sample sizes, p-values, and relative fold changes to my notes; will write up results in a blog post.
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Check out the meta-analysis out of Church's lab ... kinda old though :https://academic.oup.com/bioinformatics/article/25/7/875/210443 …
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Yeah a ton has been done since then, but that’s cool! I didn’t know there was a unified database!
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I think the answer is a lot simpler: bioinf/genetics papers tend to not be reproducable at all. It all boils down to bad methodology & bad documentation.
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