GeneSmith

I'm a software developer by training with an interest in genetics. I am currently doing independent research on gene therapy with an emphasis on intelligence enhancement.

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I'll give a quick TL;DR here since I know the post is long.

There's about 20,000 genes that affect intelligence. We can identify maybe 500 of them right now. With more data (which we could get from government biobanks or consumer genomics companies), we could identify far more.

If you could edit a significant number of iq-decreasing genetic variants to their iq-increasing counterpart, it would have a large impact on intelligence. We know this to be the case for embryos, but it is also probably the case (to a lesser extent) for adults.

So the idea is you inject trillions of these editing proteins into the bloodstream, encapsulated in a delivery capsule like a lipid nanoparticle or adeno-associated virus, they make their way into the brain, then the brain cells, and the make a large number of edits in each one.

This might sound impossible, but in fact we've done something a bit like this in mice already. In this paper, the authors used an adenovirus to deliver an editor to the brain. They were able to make the targeted edit in about 60% of the neurons in the mouse's brain.

There are two gene editing tools created in the last 7 years which are very good candidates for our task, with a low chance of resulting in off-target edits or other errors. Those two tools are called base editors and prime editors. Both are based on CRISPR.

If you could do this, and give the average brain cell 50% of the desired edits, you could probably increase IQ by somewhere between 20 and 100 points.

What makes this difficult

There are two tricky parts of this proposal: getting high editing efficiency, and getting the editors into the brain.

The first (editing efficiency) is what I plan to focus on if I can get a grant. The main issue is getting enough editors inside the cell and ensuring that they have high efficiency at relatively low doses. You can only put so many proteins inside a cell before it starts hurting the cell, so we have to make a large number of edits (at least a few hundred) with a fixed number of editor proteins.

The second challenge (delivery efficiency) is being worked on by several companies right now because they are trying to make effective therapies for monogenic brain diseases. If you plan to go through the bloodstream (likely the best approach), the three best candidates are lipid nanoparticles, engineered virus-like particles and adeno-associated viruses.

There are additional considerations like how to prevent a dangerous immune response, how to avoid off-target edits, how to ensure the gene we're targeting is actually the right one, how to get this past the regulators, how to make sure the genes we target actually do something in adult brains, and others which I address in the post.

What I plan to do

I'm trying to get a grant to do research on multiplex editing. If I can we will try to increase the number of edits that can be done at the same time in cell culture while minimizing off-targets, cytotoxicity, immune response, and other side-effects.

If that works, I'll probably try to start a company to treat polygenic brain disorders like Alzheimers. If we make it through safety trials for such a condition, we can probably start a trial for intelligence enhancement.

If you know someone that might be interested in funding this work, or a biologist with CRISPR editor expertise, please send me a message!

I am just now learning the origin of the quokka meme. The first and only time I ever saw the reference was with no explanation when someone posted this meme on Twitter

Who do you recommend asking to be a reader?

That's a difficult question. I always tell readers that the number one thing I'm interested in is where they got bored and stopped reading. I ask them to be brutally honest and not feel like they need to keep reading to flatter my ego or because they are afraid of being harsh on me.

If they aren't interested in the topic in the first place it's harder. You need to be able to at least find an audience that is interested in sitting down to read it. Can you like join a hobbyist club for this stuff, or find a subreddit for it?

Here's a kind of galaxy-brained idea that might just work for finding your crowd:

  • Go onto reddit and find the subreddit community closest to the thing you're interested in/writing about
  • Go to https://subredditstats.com and enter the name of that subreddit to see which communities it has the most overlap with.
  • Go to meetup.com and see if you can find a local group dedicated to one of those related topics (or better yet, the topic itself)
  • Go to the meetup, pitch your thing, and see if people are into it. Maybe just TALK about what you've written first and if people seem interested offer to send them what you've written.

If you decide to actually give the above a shot, tell me how it goes. I'd be very interested to hear whether my idea works.

https://www.nature.com/articles/s41598-020-69927-7

This is one of the better papers I know of examining sibling validation. To quote from the article:

Sibling comparisons are a powerful method with which to validate genomic prediction in humans. Siblings (i.e., children who share the same mother and father) have typically experienced similar environments while growing up: family social status, exposure to toxins, diet, climate, etc. all tend to be similar3,4. Furthermore, siblings are concordant for ancestry and display negligible differences in population structure.

  1. If environmental conditions in a specific region, such as, e.g., Northern England, affect disease risk, the predictor trained on UK data might assign nonzero effect sizes to SNPs associated with ancestries found in that region—i.e., the predictor learns to use population structure correlated to environmental conditions. These specific SNPs are correlated to disease risk for environmental reasons, but might not have any connection to genetic mechanisms related to the disease. They likely have little power to differentiate between siblings, who experienced similar family conditions and have have identical ancestry.
  2. It is also possible that some SNP variants affect nurture (the way that parents raise their children). These SNPs could affect the child phenotype via an environmental mechanism under parental control, not a biochemical pathway within the child. This is sometimes referred to as a genetic nurture effect9,10,11,12,13. Note, siblings raised together would both be affected by parental genetic nurture variants, so these effects are weakened in family designs.

Sibling comparisons reduce the impact of factors such as those described above. We expect some reduction in power when predictors trained in a population of non-sibling individuals are tested among sibs. Sibling validation likely yields a better estimate of truly causal genetic effects. A more complicated measure of familial relatedness might lead to even better results14, but we restrict our analyses here to siblings.

There's more in the paper if you care to take a look.

A relevant tweet from Nate Silver on the methodology used to conduct the survey:

This is not a scientific way to do a survey. The biggest issue is that it involved personalized outreach based on a totally arbitrary set of criteria. That's a huge no-no. It also, by design, had very few biosafety or biosecurity experts.

The tweet has some screenshots of relevant parts of the paper

I've landed three jobs thanks to my writing, which is a bit strange to say because I still feel like I have substantial room for improvement. But that's still a pretty good track record, so I'll tell you what has worked for me:

  • Ask people to read your stuff and tell you at what point they get bored or want to stop. Tell them to be brutally honest. The most important part of writing is keeping your audience's attention, and many writers make no effort to do this.
  • Write about something that's actually important, and that interests you. I've done a large amount of high-quality writing about interest rates, banking and crypto. So far as I can tell it was a complete waste because no one cared. 80% of the battle is just picking the right topic.
  • Put the most imortant ideas at the start of whatever you're writing. The drop-off among readers (even on a site like LessWrong) is shockingly high. I received almost 400 upvotes on my post about adult intelligence enhancement, and only four people sent me a DM in response to my request they do so that I placed at the end of the appendix.
  • Write the narrative of a story in the evening and then correct language and facts in the morning. My best, most productive narrative writing often occurs late at night, but when I re-read it in the morning it sounds sloppy and full of mistakes. However, I nearly always need to start with the sloppy, emotional version because good narrative writing is the one thing I can't do well when my brain is functioning at full capacity.
  • Don't be afraid to start a new draft of a post if you feel like you haven't gotten the narrative right.

This reminds me a bit of my own hiring process. I wanted to work for a company doing polygenic embryo screening, but I didn't fit any of the positions they were hiring for on their websites, and when I did apply my applications were ignored.

One day Scott Alexander posted "Welcome Polygenically Screened Babies", profiling the first child to be born using those screening methods. I left a comment doing a long cost-effectiveness analysis of the technology, and it just so happened that the CEO of one of the companies read it and asked me if I'd like to collaborate with them.

The collaboration went well and they offered me a full-time position a month later.

All because a comment I left on a blog.

Gene editing can fix major mutations, to nudge IQ back up to normal levels, but we don't know of any single genes that can boost IQ above the normal range

This is not true. We know of enough IQ variants TODAY to raise it by about 30 points in embryos (and probably much less in adults). But we could fix that by simply collecting more data from people who have already been genotyped.

None of them individually have a huge effect, but that doesn’t matter much. It just means you need to perform more edits.

If we want safe AI, we have to slow AI development.

I agree this would help a lot.

EDIT: added a graph

None of the stuff that you suggested has worked for any animal.

Has anyone done 2500 edits in the brain cells of an animal? No. The graphs are meant to illustrate the potential of editing to affect IQ given a certain set of assumptions. I think there are still significant barriers that must be overcome. But like... the trend here is pretty obvious. Look at how much editors have improved in just the last 5 years. Look at how much better our predictors have gotten. It's fairly clear where we are headed.

Also, to say that none of this stuff has been done in animals seems a bit misleading. Here's a paper where the authors were able to make a desired edit in 60% of mouse brain cells. Granted, they were using AAVs, but for some oligogenic conditions that may be sufficient; you can pack a single AAV with a plasmid holding DNA sufficient to make sgRNA for 31 loci using base editors. There are several conditions for which 30 edits would be sufficient to result in a >50% reduction in disease risk even after taking into account uncertanties about which allele is causal.

Granted, if we can't improve editing efficiency in neurons to above 5% then the effect will be significantly reduced. I guess I am fairly optimistic on this front: if an allele is having an effect in brains, it seems reasonable to assume that some portion of the time it will not be methylated or wrapped around a histone, and thus be amenable to editing.

Regarding lipid nanoparticles as a delivery vehicle for editors: Verve-101 is a clinical trial underway right now evaluating safety and efficacy of lipid nanoparticles with a base editor to target PCSK9 mutations causing familial hypercholesterolemia.

There are other links in the post such as one showing transcytosis of BBB endothelial cells using angiopep conjugated LNPs. And here's a study showing about 50% transfection efficiency of LNPs to brain cells following intracranial injection in mice.

it's technically challenging if not impossible

Technically challenging? Yes.

Impossible?

Obviously not. You can get payloads into the brain. You can make edits in cells. And though there are issues with editing efficiency and delivery, both continue to improve every year. Eventually we will be able to do this.

if we want to achieve a true revolution in cognition, we need to target brain development not already developed brain!

If your contention is that it is easier to get a large effect by editing embryos vs the adult brain, I would of course agree! But consider all the conditions that are modulated by the timing and level of protein expression. It would be quite surprising to me if intelligence were not to modulated in a similar manner.

Furthermore, given what is happening in AI right now, we probably don't have 25 years left for the technology for embryo editing to mature and for the children born with its benefits to grow up.

Imagine a monkey thinking of enhancing its abilities by injecting virus in its brain - will it ever reach a human level cognition? Sounds laughable. Who cares about +5 points to IQ

I have doubts we can enhance chimpanzee intelligence. We don't have enough chimpanzees or enough intelligence phenotypes to create GWAS for chimp intelligence (or any other mental trait for that matter).

We could try porting human predictors but well... we already see substantial dropoff in variance explained when predictors are ported from one genetic ancestry group to another. Imagine how large the dropoff would be between species.

Granted, a lot of the dropoff seems to be due to differences in allele frequencies and LD structure. So maybe there's some chance that a decent percentage of the variants would cause similar effects across species. But my current guess is few of the variants will have effects in both species.

Also, if I expected +5 IQ points to be the ceiling of in-vivo editing I wouldn't care about this either. I do not expect that to be the ceiling, which is reflected in some of the later graphs in the post.

For >40 years, way before the discovery of CRISPRs and base editors, we've been successfully genetically engineering mice, but not other species. Why only mice? Because we can culture mouse embryonic stem cells that can give rise to complete animals. We did not understand why mouse cells were so developmentally potent, and why this didn't work for other species. Now we do (I'm the last author): Highly cooperative chimeric super-SOX induces naive pluripotency across species - ScienceDirect

I've spent the better part of the afternoon reading and trying to understand this paper.

First, it's worth saying just how impressive this work is. The improvement of success rates over existing embryogenesis techniques like SCNT. I have a few questions I wasn't able to find answers to in the paper:

  • Do the rates of full-term and adult survival rates in iPSC mice match that which could be achieved by normal IVF, or do they indicate that there is still some suboptimality in culturing of tetraploid aggregated iPSC embryos? I'm not familiar with the normal rates of survival for mice so I wasn't able to tell from the graph whether there is still room for improvement.
  • How epigenetically different are embryos produced with Sox2-17 compared to those produced through the normal IVF process?
  • If this process or an improved one in the future were capable of inducing embryo-viable iPSC's, would you be able to tell this was the case in humans with the current data available? If not, what data are you missing? I'm particularly wondering about whether you feel that there is sufficient data available regarding the epigenetic state of normal embryonic cells at the blastocyst stage.

When you engineer stem cells rather than adult animals, all of those concerns you listed are gone: low efficiency, off-target mutations, delivery, etc. Pluripotent stem cells are immortal and clonogenic, which means that even if you get 1 in 1000 cells with correct edits and no off-target mutations, you can expand it indefinitely, verify by sequencing, introduce more edits, and create as many animals as you want. The pluripotent stem cells can either be derived from the embryos or induced artificially from skin or blood cells. The engineered pluripotent stem cells can either be used directly to create embryos or can be used to derive sperm and eggs; both ways work well for mice.

You are of course correct about everything here. And if we had unlimited time I think the germline editing approach would be better. But AGI appears to be getting quite near. If we haven't alignment by the point that AI can recursively self-improve, then I think this technology becomes pretty much irrelevant. Meat-based brains, even genetically enhanced ones, are going to be irrelevant in a post-AGI world.

One would need to start with animals. I propose starting with rats, which are a great model of cognitive studies

How exactly do you propose to do this given we don't have cognitive ability GWASes for rats, don't have a feasible technique for getting them without hundreds of thousands of phenotypes, and given the poor track record of candidate gene studies in establishing causal variants?

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