The smaller size of Fanzors compared to Cas9 is appealing and the potential for lower immunogenicity could end up being very important for multiplex editing (if inflammation in off-target tissues is a big issue, or if an immune response in the brain turns out to be a risk).
The most important things are probably editing efficiency and the ratio of intended to unintended edits. Hard to know how that will shake out until we have Fanzor equivalents of base and prime editors.
ANNs and BNNs operate on the same core principles; the scaling laws apply to both and IQ in either is a mostly function of net effective training compute and data quality.
How do you know this?
Genes determine a brain’s architectural prior just as a small amount of python code determines an ANN’s architectural prior, but the capabilities come only from scaling with compute and data (quantity and quality).
In comparing human brains to DL, training seems more analogous to natural selection than to brain development. Much simpler “architectural prior”, vastly more compute and data.
So you absolutely can not take datasets of gene-IQ correlations and assume those correlations would somehow transfer to gene interventions on adults
We’re really uncertain about how much would transfer! It would probably affect some aspects of intelligence more than others, and I’m afraid it might just not work at all if g is determined by the shape of structures that are ~fixed in adults (e.g. long range white matter connectome). But it’s plausible to me that the more plastic local structures and the properties of individual neurons matter a lot for at least some aspects of intelligence (e.g. see this).
so to the extent this could work at all, it is mostly limited to interventions on children and younger adults who still have significant learning rate reserves
There’s a lot more to intelligence than learning. Combinatorial search, unrolling the consequences of your beliefs, noticing things, forming new abstractions. One might consider forming new abstractions as an important part of learning, which it is, but it seems possible to come up with new abstractions ‘on the spot’ in a way that doesn’t obviously depend on plasticity that much; plasticity would more determine whether the new ideas ‘stick’. I’m bottlenecked by the ability to find new abstractions that usefully simplify reality, not having them stick when I find them.
But it ultimately doesn’t matter, because the brain just learns too slowly. We are now soon past the point at which human learning matters much.
My model is there’s this thing lurking in the distance, I’m not sure how far out: dangerously capable AI (call it DCAI). If our current civilization manages to cough up one of those, we’re all dead, essentially by definition (if DCAI doesn’t kill everyone, it’s because technical alignment was solved, which our current civilization looks very unlikely to accomplish). We look to be on a trajectory to cough one of those up, but It isn’t at all obvious to me that it’s just around the corner: so stuff like this seems worth trying, since humans qualitatively smarter than any current humans might have a shot at thinking of a way out that we didn’t think of (or just having the mental horsepower to quickly get working something we have thought of, e.g. getting mind uploading working).
A working protocol hasn’t been demonstrated yet, but it looks like there’s a decent chance it’s doable with the right stitching together of existing technologies and techniques. You can currently do things like isolating a specific chromosome from a cell line, microinjecting a chromosome into the nucleus of a cell, or deleting a specific chromosome from a cell. The big open questions are around avoiding damage and having the correct epigenetics for development.
Mildly deleterious mutations take a long time to get selected out, so you end up with an equilibrium where a small fraction of organisms have them. Genetic load is a relevant concept.
Promoters (and any non-coding regulatory sequence for that matter) are extremely sensitive to point mutations.
A really important question here is whether the causal SNPs that affect polygenic traits tend to be located in these highly sensitive sequences. One hypothesis would be that regulatory sequences which are generally highly sensitive to mutations permit the occasional variant with a small effect, and these variants are a predominant influence on polygenic traits. This would be bad news for us, since even the best available editors have non-negligible indel rates at target sites.
Another question: there tend to be many enhancers per gene. Is losing one enhancer generally catastrophic for the expression of that gene?
We’d edit the SNPs which have been found to causally influence the trait of interest in an additive manner. The genome would only become “extremely unlikely” if we made enough edits to push the predicted trait value to an extreme value—which you probably wouldn’t want to do for decreasing disease risk. E.g. if someone has +2 SD risk of developing Alzheimer’s, you might want to make enough edits to shift them to −2 SD, which isn’t particularly extreme.
You’re right that this is a risk with ambitious intelligence enhancement, where we’re actually interested in pushing somewhat outside the current human range (especially since we’d probably need to push the predicted trait value even further in order to get a particular effect size in adults) -- the simple additive model will break down at some point.
Also, due to linkage disequilibrium, there are things that could go wrong with creating “unnatural genomes” even within the current human range. E.g. if you have an SNP with alleles A and B, and there are mutations at nearby loci which are neutral conditional on having allele A and deleterious conditional on having allele B, those mutations will tend to accumulate in genomes which have allele A (due to linkage disequilibrium), while being purged from genomes with allele B. If allele B is better for the trait in question, we might choose it as an edit site in a person with allele A, which could be highly deleterious due to the linked mutations. (That said, I don’t think this situation of large-conditional-effect mutations is particularly likely a priori.)
You acknowledge this but I feel you downplay the risk of cancer—an accidental point mutation in a tumour suppressor gene or regulatory region in a single founder cell could cause a tumour.
For each target the likely off-targets can be predicted, allowing one to avoid particularly risky edits. There may still be issues with sequence-independent off-targets, though I believe these are a much larger problem with base editors than with prime editors (which have lower off-target rates in general). Agree that this might still end up being an issue.
Unless you are using the term “off-target” to refer to any incorrect edit of the target site, and wider unwanted edits—in my community this term referred specifically to ectopic edits elsewhere in the genome away from the target site.
This is exactly it—the term “off-target” was used imprecisely in the post to keep things simple. The thing we’re most worried about here is misedits (mostly indels) at noncoding target sites. We know a target site does something (if the variant there is in fact causal), so we might worry that an indel will cause a big issue (e.g. disabling a promoter binding site). Then again, the causal variant we’re targeting has a very small effect, so maybe the sequence isn’t very sensitive and an indel won’t be a big deal? But it also seems perfectly possible that the sequence could be sensitive to most mutations while permitting a specific variant with a small effect. The effect of an indel will at least probably be less bad than in a coding sequence, where it has a high chance of causing a frameshift mutation and knocking out the coded-for protein.
The important figure of merit for editors with regards to this issue is the ratio of correct edits to misedits at the target site. In the case of prime editors, IIUC, all misedits at the target site are reported as “indels” in the literature (base editors have other possible outcomes such as bystander edits or conversion to the wrong base). Some optimized prime editors have edit:indel ratios of >100:1 (best I’ve seen so far is 500:1, though IIUC this was just at two target sites, and the rates seem to vary a lot by target site). Is this good enough? I don’t know, though I suspect not for the purposes of making a thousand edits. It depends on how large the negative effects of indels are at noncoding target sites: is there a significant risk the neuron gets borked as a result? It might be possible to predict this on a site-by-site basis with a better understanding of the functional genomics of the sequences housing the causal variants which affect polygenic traits (which would also be useful for finding the causal variants in the first place without needing as much data).
Even out of this 10%, slightly less than 10% of that 10% responded to a 98-question survey, so a generous estimate of how many of their customers they got to take this survey is 1%. And this was just a consumer experience survey, which does not have nearly as much emotional and cognitive friction dissuading participants as something like an IQ test.
What if 23&me offered a $20 discount for uploading old SAT scores? I guess someone would set up a site that generates realistically distributed fake SAT scores that everyone would use. Is there a standardized format for results that would be easy to retrieve and upload but hard to fake? Eh, idk, maybe not. Could a company somehow arrange to buy the scores of consenting customers directly from the testing agency? Agree that this seems hard.
Statistical models like those involved in GWASes follow one of many simple rules: crap in, crap out. If you want to find a lot of statistically significant SNPs for intelligence and you try using a shoddy proxy like standardized test score or an incomplete IQ test score as your phenotype, your GWAS is going to end up producing a bunch of shoddy SNPs for “intelligence”. Sample size (which is still an unsolved problem for the reasons aforementioned) has the potential to make up for obtaining a low amount of SNPs that have genome-wide significance, but it won’t get rid of entangled irrelevant SNPs if you’re measuring something other than straight up full-scale IQ.
This seems unduly pessimistic to me. The whole interesting thing about g is that it’s easy to measure and correlates with tons of stuff. I’m not convinced there’s any magic about FSIQ compared to shoddier tests. There might be important stuff that FSIQ doesn’t measure very well that we’d ideally like to select/edit for, but using FSIQ is much better than nothing. Likewise, using a poor man’s IQ proxy seems much better than nothing.
The hope is that local neural function could be altered in a way that improves fluid intelligence, and/or that larger scale structural changes could happen in response to the edits (possibly contingent on inducing a childlike state of increased plasticity).
Showing that many genes can be successfully and accurately edited in a live animal (ideally human). As far as I know, this hasn’t been done before! Only small edits have been demonstrated.
This is more or less our current plan.
Showing that editing embryos can result in increased intelligence. I don’t believe this has even been done in animals, let alone humans.
This has some separate technical challenges, and is also probably more taboo? The only reason that successfully editing embryos wouldn’t increase intelligence is that the variants being targeted weren’t actually causal for intelligence.
Gene editing to make people taller.
This seems harder, you’d need to somehow unfuse the growth plates.
on the other hand, “our patients increased 3 IQ points, we swear” is not as easily verifiable
A nice thing about IQ is that it’s actually really easy to measure. Noisier than measuring height, sure, but not terribly noisy.
They all also will make you rich, and they should all be easier than editing the brain. Why do rationalists always jump to the brain?
More intelligence enables progress on important, difficult problems, such as AI alignment.
(I should clarify, I don’t see modification of polygenic traits just as a last ditch hail mary for solving AI alignment—even in a world where I knew AGI wasn’t going to happen for some reason, the benefits pretty clearly outweigh the risks. The case for moving quickly is reduced, though.)
Of course if you combine gene edits with other interventions to rejuvenate older brains or otherwise restore youthful learning rate more is probably possible
We thought a bit about this, though it didn’t make the post. Agree that it increases the chance of the editing having a big effect.
Repeat administration is a problem for traditional gene therapy too, since the introduced gene will often be eliminated rather than integrated into the host genome.
Non-coding means any sequence that doesn’t directly code for proteins. So regulatory stuff would count as non-coding. There tend to be errors (e.g. indels) at the edit site with some low frequency, so the reason we’re more optimistic about editing non-coding stuff than coding stuff is that we don’t need to worry about frameshift mutations or nonsense mutations which knock-out the gene where they occur. The hope is that an error at the edit site would have a much smaller effect, since the variant we’re editing had a very small effect in the first place (and even if the variant is embedded in e.g. a sensitive binding site sequence, maybe the gene’s functionality can survive losing a binding site, so at least it isn’t catastrophic for the cell). I’m feeling more pessimistic about this than I was previously.
Thanks for leaving such thorough and thoughtful feedback!
You could elect to use proxy measures like educational attainment, SAT/ACT/GRE score, most advanced math class completed, etc., but my intuition is that they are influenced by too many things other than pure g to be useful for the desired purpose. It’s possible that I’m being too cynical about this obstacle and I would be delighted if someone could give me good reasons why I’m wrong.
The SAT is heavily g-loaded: r = .82 according to Wikipedia, so ~2/3 of the variance is coming from g, ~1/3 from other stuff (minus whatever variance is testing noise). So naively, assuming no noise and that the genetic correlations mirror the phenotype correlations, if you did embryo selection on SAT, you’d be getting .82*h_pred/sqrt(2) SDs g and .57*h_pred/sqrt(2) SDs ‘other stuff’ for every SD of selection power you exert on your embryo pool (h_pred^2 is the variance in SAT explained by the predictor, we’re dividing by sqrt(2) because sibling genotypes have ~1/2 the variance as the wider population). Which is maybe not good; maybe you don’t want that much of the ‘other stuff’, e.g. if it includes personality traits.
It looks like the SAT isn’t correlated much with personality at all. The biggest correlation is with openness, which is unsurprising due to the correlation between openness and IQ—I figured conscientiousness might be a bit correlated, but it’s actually slightly anticorrelated, despite being correlated with GPA. So maybe it’s more that you’re measuring specific abilities as well as g (e.g. non-g components of math and verbal ability).
Another thing: if you have a test for which g explains the lion’s share of the heritable variance, but there are also other traits which contribute heritable variance, and the other traits are similarly polygenic as g (similar number of causal variants), then by picking the top-N expected effect size edits, you’ll probably mostly/entirely end up editing variants which affect g. (That said, if the other traits are significantly less polygenic than g then the opposite would happen.)
this would be extremely expensive, as even the cheapest professional IQ tests cost at least $100 to administer
Getting old SAT scores could be much cheaper, I imagine (though doing this would still be very difficult). Also, as GeneSmith pointed out we aren’t necessarily limited to western countries. Assembling a large biobank including IQ scores or a good proxy might be much cheaper and more socially permissible elsewhere.
The barriers involved in engineering the delivery and editing mechanisms are different beasts.
I do basically expect the delivery problem will gated by missing breakthroughs, since otherwise I’d expect the literature to be full of more impressive results than it actually is. (E.g. why has no one used angiopep coated LNPs to deliver editors to mouse brains, as far as I can find? I guess it doesn’t work very well? Has anyone actually tried though?)
Ditto for editors, though I’m somewhat more optimistic there for a handful of reasons:
sequence dependent off-targets can be predicted
so you can maybe avoid edits that risk catastrophic off-targets
unclear how big of a problem errors at noncoding target sites will be (though after reading some replies pointing out that regulatory binding sites are highly sensitive I’m a bit more pessimistic about this than I was)
even if they are a big problem, dCas9-based ABEs have extremely low indel rates and incorrect base conversions, though bystanders are still a concern
though if you restrict yourself to ABEs and are careful to avoid bystanders, your pool of variants to target has shrunk way down
I mean, your basic argument was “you’re trying to do 1000 edits, and the risks will mount with each edit you do”, which yeah, maybe I’m being too optimistic here (e.g. even if not a problem at most target sites, errors will predictably be a big deal at some target sites, and it might be hard to predict which sites with high accuracy).
It’s not clear to me how far out the necessary breakthroughs are “by default” and how much they could be accelerated if we actually tried, in the sense of how electric cars weren’t going anywhere until Musk came along and actually tried (though besides sounding crazy ambitious, maybe this analogy doesn’t really work if breakthroughs are just hard to accelerate with money, and AFAIK electric cars weren’t really held up by any big breakthroughs, just lack of scale). Getting delivery+editors down would have a ton of uses besides intelligence enhancement therapy; you could target any mono/oligo/poly-genic diseases you wanted. It doesn’t seem like the amount of effort currently being put in is concomitant with how much it would be worth, even putting ‘enhancement’ use cases aside.
one could imagine that if every 3rd or 4th or nth neuron is receiving, processing, or releasing ligands in a different way than either the upstream or downstream neurons, the result is some discordance that is more likely to be destructive than beneficial
My impression is neurons are really noisy, and so probably not very sensitive to small perturbations in timing / signalling characteristics. I guess things could be different if the differences are permanent rather than transient—though I also wouldn’t be surprised if there was a lot of ‘spatial’ noise/variation in neural characteristics, which the brain is able to cope with. Maybe this isn’t the sort of variation you mean. I completely agree that its more likely to be detrimental than beneficial, it’s a question of how badly detrimental.
Another thing to consider: do the causal variants additively influence an underlying lower dimensional ‘parameter space’ which then influences g (e.g. degree of expression of various proteins or characteristics downstream of that)? If this is the case, and you have a large number of causal variants per ‘parameter’, then if your cells get each edit with about the same frequency on average, then even if there’s a ton of mosaicism at the variant level there might not be much at the ‘parameter’ level. I suspect the way this would actually work out is that some cells will be easier to transfect than others (e.g. due to the geography of the extracellular space that the delivery vectors need to diffuse through), so you’ll have some cells getting more total edits than others: a mix of cells with better and worse polygenic scores, which might lead to the discordance problems you suggested if the differences are big enough.
For all of the reasons herein and more, it’s my personal prediction that the only ways humanity is going to get vastly smarter by artificial means is through brain machine interfaces or iterative embryo selection.
BMI seems harder than in-vivo editing to me. Wouldn’t you need a massive number of connections (10M+?) to even begin having any hope of making people qualitatively smarter? Wouldn’t you need to find an algorithm that the brain could ‘learn to use’ so well that it essentially becomes integrated as another cortical area or can serve as an ‘expansion card’ for existing cortical areas? Would you just end up bottlenecked by the characteristics of the human neurons (e.g. low information capacity due to noise)?
Really interesting, thanks for commenting.
Are you doing traditional gene therapy or CRISPR-based editing?
If the former, I’d guess you’re using Lentivirus because you want genome integration?
If the latter, why not use Lipofectamine?
How do you use electroporation?
Does this refer to the proportion of the remaining cells which had successful edits / integration of donor gene? Or the number that were transfected at all (in which case how is that measured)?
This study achieved up to 59% base editing efficiency in mouse cortical tissue, while this one achieved up to 42% prime editing efficiency (both using a dual AAV vector). These contributed to our initial optimism that the delivery problem wasn’t completely out of reach. I’m curious what you think of these results, maybe there’s some weird caveat I’m not understanding.
This is my belief as well—though the dearth of results on multiplex editing in the literature is strange. E.g. why has no one tried making 100 simultaneous edits at different target sequences? Maybe it’s obvious to the experts that the efficiency would be to low to bother with?