Good post. This looks possible, if not feasible.
“crazy, unpredictable, and dangerous” are all “potentially surmountable issues”. It’s just that we need more research into them before they stop being crazy, unpredictable, and dangerous. (except quantum I guess)
I think that most are focusing on single-gene treatments because that’s the first step. If you can make a human-safe, demonstrably effective gene-editing vector for the brain, then jumping to multiplex is a much smaller step (effective as in does the edits properly, not necessarily curing a disease). If this were a research project I’d focus on researching multiplex editing and letting the market sort out vector and delivery.
I am more concerned about the off-target effects; neurons still mostly function with a thousand random mutations, but you are planning to specifically target regions that have a supposed effect. I would assume that most effects in noncoding regions are regulator binding sites (alternately: ncRNA?), which are quite sensitive to small sequence changes. My assumption would be a higher likelihood of catastrophic mutations (than you assume).
Promoters have a few of important binding motifs whose spacing is extremely precise, but most of the binding motifs are a lot more flexible in how far away they are from each other.
Also, given that your target is in nonreplicating cells, buildup of unwanted protein might be an issue if you’re doing multiple rounds of treatment.
The accuracy of your variant data could/should be improved as well; most GWAS-based heritability data assumes random mating which humans probably don’t do. But if you’re planning on redoing/rechecking all the variants that’d be more accurate.
Additionally, I’m guessing a number of edits will have no effect as their effect is during development. If only we had some idea how these variants worked so we can screen them out ahead of time. I’m not sure what percent of variants would only have an effect during development, so you’ll need to do a lot more edits than strictly necessary and/or a harder time detecting any effects of the edits. Luckily, genes that are always off are more likely to be silenced, so they might be harder to edit.
Though I would avoid editing unsilenced genes anyways, because they’re generally off and not being expressed (and therefore less likely to have a current effect) and the act of editing usually unsilences the genes for a bit, which is an additional level of disruption you probably don’t want to deal with.
I don’t know how the Biobank measures “intelligence” but make sure it corresponds with what you’re trying to maximize [insert rehash of IQ test accuracy].
Finally, this all assumes that intelligence is a thing and can be measured. Intelligence is probably one big phase space, and measurements capture a subset of that, confounded by other factors. But that’s getting philosophical, and as long as it doesn’t end up as eugenics (Gattaca or Hitler) it’s probably fine.
Honestly just multiplex editing by itself would be useful and impressive, you don’t have to focus on intelligence. Perhaps something like muscle strength or cardiovascular health would be an easier sell.
Spike proteins. Viral entry. Evolution of multipartite viruses. Capsid assembly and maturation. Receptor specificity and modularity. Various anti-host behaviors such as host DNA sequestration/degradation. Immortalization of host cells. Tracking viral lineages.
There’s literally thousands of things virologists are studying. They’re not studying airborne transmission because airborne transmission is not very virus-specific (and hence probably falling into the domain of epidemiology/physics), it’s expensive to do (you need communities of ferrets or other animals with similar respiratory systems for anything close to real applications), and it was generally assumed to have already been known (on aerosolized droplet sizes).
I’ve done research on virus transmission in ants, and how ant hygiene greatly impacts viral transmission within the colony. Is it applicable to humans? almost definitely not. But ants are cheap, grow fast, and therefore it’s easier to study.
It is also important to acknowledge that science isn’t focused on application, but rather understanding.