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Interesting. I took Lumina around December and have noticed no change, though I don't think I've actually been hungover during that time period. I definitely didn't notice any difference in the AMOUNT needed to produce a hangover; I drunk a substantial amount and didn't get hangovers any more easily.
To the extent that transposon sequences affect traits and risks but can't be measured we should expect that be reflected in "missing heritability".
You may very well be right that highly repetitive sequences like transposons do affect traits, but that's accounted for.
Thanks a lot for the comment. I'll amend the post with some of this information in the next week. If your numbers are correct (and I have no current reason to doubt them, that substantially increases my estimate of the effectiveness of whole genome embryo sequencing.
I've been meaning to write a whole post about the different screening companies but a combination of little time due to starting a new company and a lack of clear data have preventing me from doing so thus far. With this information I might reconsider.
One more thing I'd like to ask at some point is whether you're going to publish the AUCs of all the predictors in your panel within some reference population. That would be extremely helpful for patients trying to compare Orchid vs Genomic Prediction or any other company.
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.
- 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.
- 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?
I keep finding examples of decision making that can be explained by shard theory. In particular, here's an example I read in ACX today about how depressed people make decisions that keep them depressed, which sounds an awful lot like "shards guiding behavior in ways that tend to lead to their own future self-reinforcement":
Millgram et al (2015) find that depressed people prefer to listen to sad rather than happy music. This matches personal experience; when I'm feeling down, I also prefer sad music. But why? Try setting aside all your internal human knowledge: wouldn’t it make more sense for sad people to listen to happy music, to cheer themselves up?
A later study asks depressed people why they do this. They say that sad music makes them feel better, because it’s more "relaxing" than happy music. They’re wrong. Other studies have shown that listening to sad music makes depressed people feel worse, just like you’d expect. And listening to happy music makes them feel better; they just won’t do it.
Scott's preferred explanation is one of a kind of "mood setpoint" which the depressed individual's actions are trying to reach:
Depression is often precipitated by some psychosocial event (like loss of a job, or the death of a loved one). It’s natural to feel sad for a little while after this. But instead of correctly activating regulatory processes to get mood back to normal, the body accepts the new level as its new set point, and tries to defend it.
By “defend it”, I mean that healthy people have a variety of mechanisms to stop being sad and get their mood back to a normal level. In depression, the patient appears to fight very hard to prevent mood getting back to a normal level. They stay in a dark room and avoid their friends. They even deliberately listen to sad music!
Self-reinforcing "depression shards" are obviously a mechanism through which depressive states can be maintained. But then the question becomes why are some people more vulnerable to this kind of depression homeostasis than others?
There's certainly a genetic component, but given how polygenic depression risk is (>30k variants involved) the mechanisms are likely multi-causal.
I think it’s plausible we could go higher but I’m fairly certain the linear model will break down at some point. I don’t know exactly where , but somewhere above the current human range is a good guess.
You’ll likely need a “validation generation” to go beyond that, meaning a generation or very high IQ people who can study themselves and each other to better understand how real intelligence has deviated from the linear model at high IQ ranges.
The reason they have lower IQs than humans can be explained entirely by neuron count.
Not true. Humans have an inbuilt propensity for language in a way that gorillas and other non-human primates don’t. There are other examples like this.
In Lex Fridman’s interview with Eliezer Yudkowsky, Eliezer presents no compelling path forward — and paints the future as almost non-existent.
It's worth pointing out that Eliezer's views on the relative hopelessness of the situation do not reflect those of the rest of the field. Nearly everyone else outside of MIRI is more optimistic than he is (though that is of course no guarantee he is wrong).
As an interested observer who has followed the field from a distance for about 6 years at this point, I don't think there has ever been a more interesting time with more things going on than now. When I talk to some of my friends that work in the field, many of their agendas sound kind of obvious to me, which is IMO an indication that there's a lot of low-hanging fruit in the field. I don't think you have to be a supergenius to make progress (unless perhaps you're working on agent foundations).
• The probability of doom given the development of AGI, + the probability of solving aging given AGI, nearly equals 1.
I'm not sure I understand what this means. Do you mean the "and" instead of "+"? Otherwise this statement is a little vague.
If you consider solving aging a high priority and are concerned that delays of AI might delay such a solution, here are a few things to cosider:
- Probably over a hundred billion people have died building the civilization we live in today. It would be pretty disrespectful to their legacy if we threw all that away at the last minute just because we couldn't wait 20 more years to build a machine god we could actually control. Not to mention all the people who will live in the future if we get this thing right. In the grand scheme of the cosmos, one or two generations is nothing.
- If you care deeply about this, you might consider working on cryonics both to make it cheaper for everyone and to increase the odds of personality and memory recovery following the revival process.
I live in Scandinavia and see no major (except for maybe EA dk?) political movements addressing these issues. I’m eager to make an impact but feel unsure about how to do so effectively without dedicating my entire life to AI risk.
One potential answer here is "earn to give". If you have a chance to enter a lucrative career you can use your earnings from that career to help fund work done by others.
If that's not an option or doesn't sound like something you'd enjoy, perhaps you could move? There are programs like SERI MATS you could attempt to enroll in if you're a newcomer to the field of AI safety but have a relevant background in math or computer science (or are willing to teach yourself before the program begins).
A bad enough error in a regulatory region could cause a protein to stop being expressed. But the insertion or deletion of a single base pair is not nearly as devastating.
Let me explain by talking through how a promoter works.
Promoters sit upstream of a region of the genome that codes for a protein. They generally serve as a binding site for a very important enzyme called RNA polymerase, whose job it is to transcribe DNA into mRNA, which can then be exported from the nucleus and turned into proteins.
You can delete a letter from a promoter and RNA polymerase will still be able to bind. The "binding affinity" (meaning the strength of the bond) will be affected by this deletion, but except in rare circumstances it will still work.
You can see this reflected in the distribution of insertion and deletions throughout the genome; there's not that many in coding regions, but there are tons in non-coding regions (on the order of 3-5 million).
You know, I actually looked into this at one point.
At the time I didn't find any obvious reason why it wouldn't work. But I didn't spend that much time digging into the details, so my prior is it will be hard for some reason I haven't discovered yet.
If you could actually find a way to present aribtrary "self-antigens" to T and B cells during the development phase within the thymus, that would be an incredibly powerful technology. It seems plausible to me that we could potentially cure a large percentage of autoimmune conditions with that technology, provided we knew which epitopes were triggering a particular immune response. But I know much less about this area than about gene editing, so it's entirely plausible I'm wrong.
There's already a few therapies that basically take this approach; allergy shots are probably the most basic, though I don't believe they actually do anything with the thymus. The general term for this approach seems to be "Immune Tolerance".
With a short search, I don't see anything about reprogramming the thymus.
This would first require that you find an overlap of test subjects five hundred thousand strong that will not only volunteer to have their entire genome sequenced (a SNP array could be used to cut costs if you're willing to sacrifice the breadth of variants interrogated), but will also sit down for an hours-long professionally-administered IQ test, like the WAIS-IV (again, could use some abridged test to cut costs and increase participation rate at the expense of lower-quality data)
I am more optimistic than you here. I think it is enough to get people who have already gotten their genomes sequenced through 23&Me or some other such consumer genomics service to either take an online IQ test or submit their SAT scores. You could also cross-check this with other data such people submit to validate their answer and determine whether it is plausible.
I think this could potentially be done for a few million dollars rather than 50. In fact companies like GenomeLink.io already have these kind of third party data analysis services today.
Also, we aren't limited to western countries. If China or Taiwan or Japan or any other country creates a good IQ predictor, it can be used for editing purposes. Ancestry doesn't matter much for editing purposes, only for embryo selection.
Would the quality of such tests be lower than those of professionally administered IQ tests?
Of course. But sample size cures many ills.
As far as delivery goes, the current state of these technologies will force you to use lipid nanoparticles because of the dangers of an inflammatory response being induced in the brain by an AAV, not to mention the risk of random cell death induction by AAVs, the causes of which are poorly understood.
I briefly looked into this and found these papers:
Adeno-Associated virus induces apoptosis during coinfection with adenovirus
I asked GPT4 whether adenoviruses enter the brain:
In general, adenoviruses are not commonly known to infect the brain or cause central nervous system diseases. Most adenovirus infections remain localized to the site where they first enter the body, such as the respiratory or gastrointestinal tracts. However, in rare cases, especially in individuals with weakened immune systems, adenoviruses can potentially spread to other organs, including the brain.
I also found this paper indicating much more problematic direct effects observed in mouse studies:
AAV ablates neurogenesis in the adult murine hippocampus
We demonstrate that neural progenitor cells (NPCs) and immature dentate granule cells (DGCs) within the adult murine hippocampus are particularly sensitive to rAAV-induced cell death. Cell loss is dose dependent and nearly complete at experimentally relevant viral titers. rAAV-induced cell death is rapid and persistent, with loss of BrdU-labeled cells within 18 hr post-injection and no evidence of recovery of adult neurogenesis at 3 months post-injection.
Also:
Efficient transduction of the dentategyrus (DG)– without ablating adult neurogenesis– can be achieved by injection of rAAV2-retro serotyped virus into CA3
So it sounds like there are potential solutions here and this isn't necessarily a showstopper, especially if we can derisk using animal testing in cows or pigs.
You will need to use plasmid DNA (as opposed to mRNA, which is where lipid nanoparticles currently shine) if you want to keep them non-immunogenic and avoid the same immunogenicity risks of AAVs, which will significantly reduce your transduction efficiency lest you develop another breakthrough.
This is an update for me. I didn't previously realize that the mRNA for a base or prime editor could itself trigger the innate immune system. I wonder how serious of a concern this would actually be?
If it is serious, we could potentially deliver RNPs directly to the cells in question. I think this would be plausible to do with pretty much any delivery vector except AAVs.
I don't really see how delivering a plasmid with the DNA for the editor will be any better than delivering mRNA. The DNA will be transcribed into the exact same mRNA you would have been delivering anyways, so if the mRNA for CRISPR triggers the innate immune system thanks to CpG motifs or something, putting it in a plasmid won't help much.
Lipid nanoparticles, even though they're generally much safer, still have the potential to be immunogenic or toxic following repeated doses or high enough concentrations, which is another hurdle because you will need to use them repeatedly considering the number of edits you're wanting to make.
Yeah, one other delivery vector I've looked into the last couple of days are extracellular vescicles. They seem to have basically zero problems with toxicity because the body already uses them to shuttle stuff around. And you can stick peptides on their surface similar to what we proposed with lipid nanoparticles.
The downside is they are harder to manufacture. You can make lipid nanoparticles by literally putting 4 ingredients plus mRNA inside a flask together and shaking it. ECVs require manufacturing via human cell colonies and purification.
A simple thought experiment may change your mind about mosaicism in the brain: consider what would happen in the case of editing multiple loci (whether purposeful or accidental) that happen to play a role in a neuron's internal clock. If you have a bunch of neurons releasing substrates that govern one's circadian rhythm in a totally discordant manner, I'd have to imagine the outcome is that the organism's circadian rhythm will be just as discordant. This can be extrapolated to signaling pathways in general among neurons, where again 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.
Thanks for this example. I don't think I would be particularly worried about this in the context of off-target edits or indels (provided the distribution is similar to that of naturally occuring mutations), but I can see it potentially being an issue if the intellligence modifying alleles themselves work via regulating something like the neuron's internal clock.
If this turns out to be an issue, one potential solution would be to exclude edits to genes that are problematic when mosaic. But this would probably be pretty difficult to validate in an animal model so that might just kill the project.
Thanks for the comment. This is actually quite helpful, as the effects of off-target edits or indels to promoter and enhancer regions is one of the primary uncertainties we have regarding feasibility of the proposal.
My prior for thinking that a few off-targets targets or indels wouldn't necessarily be catastrophic was a paper I read that looked at the total accumulation of random mutations to neurons over the lifespan. I believe by age 40 the average person has about 1500.
Regulatory regions make up about 2% of the genome, so the average neuron has about 30 mutations in regulatory regions by the age of 40. So if we can keep our de novo mutations from increasing that number very much it will probably be ok.
Now it's possible that the types of errors introduced by random mutations are of a different kind than those introduced by indels and off-targets from base and prime editors. A quick google search reveals that most de novos seem to be single base pair changes rather than insertion or deletion errors. So perhaps it WILL be an issue, at least for some editor variants.
I think the ideal approach to answer this question would be to use (or make) a computational model to predict the distribution of off-target edits and indels from editor variants, another to predict binding affinity as a function of sequence, and see how strongly such errors affect binding affinity. We could then compare those results to the affects of binding affinity from de novo mutations to see whether they were comparable in magnitude.
Perhaps others have already made such models. A quick search didn't turn up anything, but I will continue looking.
The other option is to just test it empirically in cell cultures and then animal models.
If you have any other advice about how to approach this problem, I'd appreciate it.
- The use of biotechnology to enhance intelligence raises ethical questions regarding fairness and equality. It would create an uneven playing field in terms of astronomical cost or not be FDA approved, as only those who can afford the enhancement may have access to it, exacerbating existing social inequalities like jet fuel on a dumpster fire.
This is of course something we've thought about. It's a little hard to think too seriously about this so long as we stay on track to develop AGI, which will the gap between people due to genetic differences seem very small by comparison.
But if I ignore that for a moment, I think the best way to tackle this is to make sure the technology is to ensure the per unit cost is not too high (ideally < $10k), and to perhaps offer some innovative payment plans such as taking a percentage of people's future earnings over the level they currently make for some period of time. So for example, instead of paying for the treatment directly, you might agree that for any money you make in excess of your current income, you pay the company 30% of it for 5 years.
- Modifying genes to enhance intelligence could have unforeseen side effects or unintended consequences. Genetic modifications can have complex and unpredictable effects on various aspects of an individual's physical and mental health, potentially leading to unforeseen negative outcomes.
This is of course somewhat of a concern, but most of the research I've read shows that whatever plieotropy exists between intelligence and other triats mostly works in your favor. In other words, the genes that increase intelligence generally tend to slightly decrease disease risk, violent behavior, and other traits generally considered negative.
The only exception to this I've seen this far is mild aspergers, the risk of which seems to be slightly increased by the same genes that affect intelligence. To the extent that this is a problem, we could simply identify the subset of genes that increase intelligence without increasing aspergers, or in addition to editing genes to increase intelligence, also edit genes to keep the risk of aspbergers constant at the same time.
- Intellectual diversity is valuable for society as a whole. If everyone were genetically enhanced to have significantly higher intelligence, it may lead to a loss of diverse perspectives, creative thinking, and alternative problem-solving approaches that contribute to the richness of human society.
There's 8 billion people on the planet. It's going to take a very long time to edit even a million people. So I don't think this should be a concern for at least another 50 years, by which point the question will probably be moot unless we have a global pause on AI development.
- Individuals who choose not to undergo genetic enhancement for intelligence may face stigmatization or discrimination in a society that places a high value on intelligence. This could create a divide between enhanced and non-enhanced individuals, leading to social tensions and inequality.
Yes. I don't really have a society-wide answer to this yet other than the good old "treat other people well". This is already an issue with just natural variation in abilities, though gene editing would undoubtedly exacerbate it.
- Focusing solely on increasing intelligence may lead to an imbalance in human development. Other important qualities such as emotional intelligence, creativity, empathy, and social skills may be undervalued or neglected, potentially resulting in a society that lacks holistic development. As we can deduce from the late 19th and early 20th centuries, pure application of intelligence even with the urging of now socially disregarded religious mores to empathy for others, there tends to be some really horrific wars that result and that creates incentives for weapons systems to be developed that could lead to the extinction of the species. As has been said by a wise sage of the Levant long ago, "man cannot live by bread alone."
We don't actually plan to solely focus on intelligence. In fact the first targets will be polygenic brain diseases like Alzheimers or treatment resistant depression. I also think it would be good if we could modify other traits such as conscientiousness, tolerance to sleep deprivation and others.
- The long-term effects of genetically enhancing intelligence are currently unknown. It would require extensive research and testing to fully understand the potential consequences, including any negative impacts on individuals and the broader population. This would also be the means the gatekeepers are most likely to hold these things back using, if first they don't use the patent court racket first (see below for thoughts on that specifically intended for the author's consideration).
We have very smart people around right now. They seem to be doing fine. Maybe there are "long term consequences" to modifying people to be outside the human range, but we probably won't push that far outside the limits of what naturally occurs.
It's also plausible that we could reverse some of the effects of editing with another round of edits designed to push in the opposite direction.
Assuming intelligence is primarily IQ, which is how quickly and effectively an individual comes up with a solution for a novel problem, does not fully encapsulate the notion meant by the word intelligence.
I agree of course. The post was already over 30 pages long, so I decided not to discuss other forms of intelligence. But in reality those would be of interest as well.
However, the hard part with considering any of that is that we don't currently have the phenotype data to create predictors of JUST reading ability or JUST math ability.
You are clearly very intelligent and apparently learn quickly, so now is the time to learn how to write patent applications for any and all processes you think evidently implied by these ideas or face paying a licensing fee to conduct research along lines you came up, if the patent holder even allows that much and doesn't shovel the idea into the filing cabinet of doom.
I spoke with a few biologists before publishing this post, each of which informed me that I don't have any truly novel ideas here. I have put together several existing ideas in a novel way, but it's doubtful they are patentable.
We WILL file patents after we start doing lab work.
That would be very helpful. Would you mind introducing me to them? You can send me an email here: morewronger@gmail.com
I think you’re basically correct. There’s no more clear illustration of how modern technology negatively affects birth rates than the Amish. I believe they have the highest birth rates in the country.
If we get to AGI in the next 5 years I think you’re right. 5-10 years and I’d give it maybe a 20% chance of having a large impact. 15 years maybe a 40% chance. And if we have 20 years or more then iterated meiotic selection may have time to work.
I think at this point we've probably spoken with about 10 people I consider to have some reasonable level of expertise in the field. And there have been a number of very high quality comments from knowledgeable people in the comments.
We will continue to talk with more and perhaps we will learn something that will definitely not work. I think if that is the case, the polygenic editing path is still worth pursuing because it could potentially be repurposed for many other applications.
Thanks for leaving such a high quality comment. I'm sorry for taking so long to get back to you.
We fully expect bringing this to market to take tens of millions of dollars. My best guess was $20-$40 million.
My biggest concern is your step 1:
"Determine if it is possible to perform a large number of edits in cell culture with reasonable editing efficiency and low rates of off-target edits."
And translating that into step 2:
"Run trials in mice. Try out different delivery vectors. See if you can get any of them to work on an actual animal."
I would like to hear more about your research into approaching this problem, but without more information, I am concerned you may be underestimating the difficulty of successfully delivering genetic material to any significant number of cells.
We expect this to be difficult, but we DON'T expect to have to solve the delivery problem entirely on our own. There are significant incentives for existing companies such as Dyno Therapeutics to solve the problem of delivering genes (or other payloads) to the nucleus of brain cells. In fact, Dyno already has a product, Dyno bCap 1 which successfully delivered genes to between 5% and 20% of brain cells in non-human primates.
Obviously we will need higher efficiencies than that to perform edits for polygenic brain diseases or intelligence, but the ease of delivering payloads to brain cells has been gradually improving over the years and I expect it to continue doing so.
There are of course some issues:
I know from some conversations with a former employee of Dyno that the capsids can be customized to be serologically distinct so that any antibodies formed in response to one round of treatment will not destroy the capsids used in the second round. But I am still waiting to hear back from them regarding the cost and time required to do this sort of customization.
Custom AAVs are also quite expensive per dose, largely due to the cost of reagents and other basic supplies that no one has figured out how to make cheaper yet. So it's a plausible delivery mechanism, but far from ideal.
Still, the fact that there is an existing product on the marketplace which can get custom DNA payloads into the nuclei of brain cells gives me hope that someone else will have made major headway on the delivery problem by the time we are ready for trials in cows or non-human primates.
In the meantime, we simply need a way to get editors into HEK cells and brain cells efficiently in cell cultures to test multiplex editing approaches. I'm sure this will pose its own set of challenges, but given dozens of labs have done this I don't expect that to be infeasible.
Send me an email if you have time to chat about this. If you're willing I'd like to pick your brain more about other aspects of the project.
Regulatory issues are not the main bottleneck at the moment. It's possible we'd consider doing trials down there in a few years when we are ready for actual human testing. But there's still a substantial amount of building and validation to do before we need to think about that.
why don't you start with:
- 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 in fact the plan.
2. Showing that editing embryos can result in increased intelligence. I don't believe this has even been done in animals, let alone humans.
This would be a very big thing in and of itself. Also, it wouldn't give you much useful information about whether adult editing would work, because most of the uncertainty centers around delivery efficiency, the effect size of edits in adult brains, mosaicism and other things you wouldn't be able to validate in embryos.
- Gene editing to make people taller. You'd be an instant billionaire. (I expect this is impossible but you seem to be going by which genes are expressed in adult cells, and a lot of the genes governing stature will be expressed in adult cells.)
To the best of my knowledge, this is not possible in adults. The growth plates fuse at the end of puberty. This is why bodybuilders taking HGH don't get taller.
- Gene editing for transitioning (FtM or MtF).
I don't think gene editing will be able to help with this one. You'd need to swap an X chromosome to a Y or vice-versa. None of the delivery vectors are large enough to fit an entire chromosome. They're not even close.
And even if you could select a subset of the genes most impactful, you'd have to eliminate the existing chromosome, which is not trivial.
And even if you could do that, it wouldn't be able to undo male or female puberty.
And even if you could do that I have no idea how this tech could be used to grow different sexual organs, which is what you would ideally want.
MAYBE this could be used to enable endogenous sex hormone production or something. But apart from that, nothing comes to mind for ways this tech could help people who want to transition.
- Gene editing to cure male pattern baldness.
This one could actually be possible. In fact it would probably be easier than intelligence or brain disorders. But your competition is going to be hair transplants and rogaine, which would be difficult to beat on price.
- [Exercise for the reader: generate 3-5 more examples of this general type, i.e. highly desirable body modifications that involve coveting another human's reasonably common genetic traits, and for which any proposed gene therapy can be easily verified to work just by looking.]
Obviously there are many more examples. That's one of the exciting things about in-vivo editing. But you have limitations:
- The trait must be heritable (true for many things we'd want to change)
- We need good genetic predictors for the trait
All of the above are instantly verifiable (on the other hand, "our patients increased 3 IQ points, we swear" is not as easily verifiable). 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?
We would not even attempt the therapy unless the difference was easily measurable.
The market has very strong incentives to solve the above, by the way, and they don't involve taboos about brain modification or IQ. The reason they haven't been solved via gene editing is that gene editing in adults simply doesn't work nearly as well as you want it to.
Yeah, this is what I used to think before I actually worked at a bunch of startups and realized that the efficient market hypothesis doesn't apply to all markets equally. In reality there are hundred dollar bills lying around everywhere, but most people can only see a few, and some can't see any.
This is particularly true when there are high barriers to entry, hidden information, many steps of inference to reach a conclusion, and cultural taboos that prevent people from looking. Every one of those is getting in the way here.
What is the overall demand? How many customers would be interested? What specific traits are parents most interested in? Funding is the single biggest obstacle I face, and I will be applying for AstralCodexTen funding.
My best guess is that maybe 500 children worldwide have been born with the benefit of polygenic embryo screening. The market is not particularly large right now, though it's pretty obvious that could change. If you actually look rationally at the cost-benefit analysis of doing polygenic screening it's way better than most medical interventions in terms QALYs/$. And that if you JUST look at disease risk and ignore other traits.
From everything I've read so far, the really enthusiastic early adopters are most interested in intelligence. But there's also a healthy demand from people who have a family history of a particular medical condition and want their children to have a reduced risk.
If enough interest is displayed or if I get charitable funding, my gut feeling is that I can offer analysis for <$100 per genome, maybe even <$10.
I'm not sure if you're aware, but a bunch of us are working on an open-source intelligence predictor. We may actually be able to expand the site to include other traits as well without too much of an effort. But the data mostly comes from European-heavy biobanks. If you could get access to high quality chinese data that would be a huge boon.
Would Westerners accept Chinese IQ/educational attainment metrics? The easiest metric to use would be GaoKao scores, but would that be legible to Westerners?
Many of them would, so long as the predictors actually perform well among those with European ancestry. Also, you could likely combine data from European studies with those from Chinese studies to more precisely pinpoint causal variants. Different genetic ancestry groups tend to have different linkage disequilibrium structures, which is very helpful when you're trying to pin down which SNP in a group is actually CAUSING the observed difference.
Would ratings of attended college be acceptable?
Possibly? I guess I would have to know more about the college acceptance criteria. And if I want to know more, most parents probably will as well.
Publications/computer code. Would I need to publish a paper to be considered legitimate?
Maybe? If you plan to go through IVF clinics (which I can tell you from personal experience is very difficult), having publications about your approach and your technology will definitely help.
I actually think something that would help far more would be creating tools to help parents estimate the benefits of embryo selection. LifeView.com used to have a calculator page which showed the expected disease reductions from selecting a certain number of embryos. They have taken it down for no apparent reason, and now there are no tools on the internet where parents can see the expected benefits of PGT-P.
It's so obvious a tool like that is needed. And many others as well.
Reputational concerns. What Western institutions should I be expected to be blacklisted from because of this?
I'm not sure. Many academics would probably view you as a eugenicist (even though many of them aren't quite sure what they mean when they use that term). You'd probably have some pretty loyal fans too, especially if you made a product that a lot of parents really want to use.
I would really appreciate it if anyone can direct me towards a source of European ancestry genomic data with labeled donor information (education, height, ect).
If you were to flip enough variants to push someone far outside the human range then that’s almost certainly correct. But the linear model holds remarkably well within the current human range, and likely to some degree outside of it.
But I am not too concerned about this because we can do multiples rounds of fewer edits and validate between rounds.
Man, if you think the vaccine is scary just wait until you hear about what COVID does.
In all seriousness, you shouldn't find mRNA vaccines any scarier than the J&J vaccine or the Novavax vaccine. J&J puts DNA for the spike protein in a modified adenovirus, which then enters your muscle cells, breaks out into the cytoplasm, and injects the DNA payload into the nucleus. Then RNA transcriptase makes mRNA out of that DNA, which is exported from the nucleus into the cytoplasm where it is turned into the spike protein.
Novavax just skips the entire manufacturing process; they just inject the spike protein directly into your body.
I'm not an expert on COVID vaccines, but from everything I have read, the rare dangerous side-effects like myocarditis seem to come from the spike protein itself triggering a dangerous immune response in some very small percentage of people.
But like... you can't avoid that with any vaccine. And the incident of getting myocarditis or a worse condition from COVID itself (especially if you're unvaccinated) are way, way higher than getting it from the vaccine.
Vaccines are mostly just a cost-benefit analysis; if your odds of damage are higher from the virus (adjusting for the probability of infection) compared to the vaccine, you're better off getting vaccinated. That will be the case for virtually everyone. Maybe there's some case for young children to not get the vaccine because the virus itself is so much less dangerous for them. But that's about it.
I mean this is all kind of a moot point now because everyone has either been infected or vaccinated already, but if your main takeaway from this post was that mRNA vaccines are dangerous I would be pretty disappointed.
Imagine if over 100 dishes of edited neurons you saw a slight (statistically significant) increase in performance.
That's an interesting idea for validation, thanks.
I mean, I explicitly state in the post that I don't think we'll be able to reach IQs far outside the normal human range by just flipping alleles:
I don’t expect such an IQ to actually result from flipping all IQ-decreasing alleles to their IQ-increasing variants for the same reason I don’t expect to reach the moon by climbing a very tall ladder; at some point, the simple linear model will break down.
So yes, I agree with you
I'd be worried about the loss of memories and previously learned abilities that would come along with "increasing die-off of old cells".
Also, there isn't really much extra room in the brain for these new neurons to go. So unless they were somehow a lot smaller I think you'd have to basically replace existing brain tissue with them.
It's an interesting idea. It seems likely to be substantially more invasive than what I have in mind for the gene editing treatment, but if it actually worked that wouldn't necessarily be a huge concern.
Might be a more universal solution too if we could come up with a single or small variety of options for a 'super' brain optimising added chromosome.
The thing about large scale interventions like "adding a new chromosome" is that it's going to be much harder to generalize from existing people what the effects will be.
If we got this technology working REALLY welll, like 99% editing efficiency and no immune issues with redosing, then we could probably try out adding new genes in randomized control trials and then slowly assemble a new chromosome out of those new genes. But I don't know when or even if we'll reach that point with this tech.
In the long run digital intelligence will win, and if we miraculously solve alignment and have any agency, we'll probably just be digital uploads.
the 23andme dataset is probably not as useful as you project. They are working from a fixed set of variants, not full genomes or even a complete set of SNPs known to vary. There are certainly many SNPs of interest that just aren't in their data.
It's possible the source I read was misleading, but last I checked they use SNP arrays with 650k variants, which is roughly all loci with minor allele frequency >1%. That's enough to make quite a strong predictor, especially since they have a fair number of non-european participants with different linkage disequilibrium (more helpful for pinpointing the causal variant in a cluster).
in projecting the gains from discovering further variants that affect intelligence, it's not clear whether you've accounted for the low hanging fruit effect. With these statistical approaches, we obviously discover the variants of largest effect first. Adding millions of additional genomes or genotypes will allow us to resolve thousands of additional common variants, but they are going to be the ones that have really tiny effect sizes.
The simulation accounts for that. That's why gain per additional edit is logarithmic.
On the other hand (contradicting point 2 somewhat), quite a substantial fraction of variation in intelligence and other traits is likely due to the genetic load - rare mutations, some likely of substantial effect, all deleterious by definition. Identifying these and their effects is a thorny statistical problem due to their rarity, but if we can, they would actually be very promising edit targets. The advantage being the likely lack of negative side effects, and the fact that the top few for any person would likely be of large effect. Some of them are also probably wide-effect boosts, fixes to fundamental bits of cellular machinery! Downside is that this would be a custom targeting job per person.
We'll get better at identifying rare variants with large causal effects soon. UK Biobank just released 500k whole genomes in late November, so we should see the first studies on that data come out in the next few months.
The simulations we ran assume that the dataset only contains variants with minor allele frequencey >1%. Any vairants with lower frequency than that will increase the average marginal effect per edit but aren't necessary for this tech to work in general.
The use of '800 IQ' is a little grating. The tests only go to 200 or 210 (and are not convincingly normed at that level). Still, fully superhuman, entirely outside the normal human trait range... I guess it's a fair way to gesture at that.
This is why I specifically used language in the post like "don't take this too seriously" and "I don’t expect such an IQ to actually result from flipping all IQ-decreasing alleles to their IQ-increasing variants for the same reason I don’t expect to reach the moon by climbing a very tall ladder"
our predictive models for IQ work significantly better for European or white populations because they were trained on that population. This implies that obtaining a bunch of data for Asian and African populations would allow us to identify additional targets. It surprises me that we don't have some huge dataset from China, but at least we recently developed a 100K+ genotype of Han individuals, which should turn up some additional hits.
There's less difference between genetic ancestry groups when it comes to editing than there is for embryo selection. With embryo selection, you can rely on linkage disequilibrium patterns remaining relatively consistent among Europeans to compensate for your uncertainty about which variant in a cluster is causal. You can't do that with editing.
So getting data from other ancestry groups (particularly Africans, who have the greatest variance in LD structure) will actually editing more efficient for everyone, including Europeans.
The lack of non-European data is slowly being solved, but at the moment I know of no non-European data source that has good IQ phenotype data. There are definitely biobanks and consumer genomics companies who have the data, so they could do it if they want to.
Thanks for the thoughtful comment. I'm glad you enjoyed the post!
Fair. I doubt there would be THAT much value drift due to personality changes just because there's not that much plieotropy in the genome. But it also wouldn't be literally zero.
And you could always opt for a lesser effect size by just targeting fewer genes and testing yourself after some time period to assess values drift before deciding if you want to go forward with a larger intervention.
That is one of the nice things about this proposal; if it works well you could will be able to customize the effect size.
So I agree with your general point that genetic interventions made in adults would have a lesser effect than those same interventions made in embryos, which is why our model assumes that the average genetic change would have just half the normal effect. The exact relative size of edits made in the adult brain vs an embryo IS one of the major unknown factors in this project but like... if brain size were the only thing affecting intelligence we'd expect a near perfect correlation between it and intelligence. But that's not what we see.
Brain size only correlates with intelligence at 0.3-0.4.
So there's obviously a lot more going on.
post training in DL lingo
It's not post-training. Brains are constantly evolving and adapting throughout the lifespan.
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.
If this was actually the case then none of the stuff people are doing in AI safety or anything else would matter. That's clearly not true.
I would be very surprised if an "appropriate set of tools" could replicate the effects of a genetic intervention. At least, I would be surprised if you could do so with anything short of AGI or an advanced brain computer interface.
I haven't spent that much time reading up on non-genetic intelligence interventions (though Gwern has written some pretty good stuff on the topic), but my overall impression is that after you've taken care of the basics like having a good diet, exercising, and avoiding lead poisoning you can't really increase fluid intelligence that much.
Also, any non-genetic interventions that DO work would just stack with the benefits of genetic interventions. So like... why not both?
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!
How difficult would it be to also apply this to the gamates and thus make any potential offspring also have the same enhanced intelligence (but this time it would go into the gene pool instead of just staying in the brain)?
Not very difficult. In fact it would be easier with fewer things that could go wrong, and a much greater ability to validate.
Does the scientific establishment think this is ethical?
It depends on who you ask, but the general answer is "no", for reasons no one can ever articulate very clearly. In general most scientists are just extremely risk averse to doing anything that involves reproduction. See the section in the post titled "Vague associations with eugenics make some academics shy away"
Also, if you do something like this, you reduce the homogeneity of the gene pool which could make the modified babies very susceptible to some sort of disease.
Brain editing wouldn't really affect the immune system.
Frankly of all the potential things we could edit, the one I am most hesitant about is the immune system. There are unique concerns with immunity that don't apply to other areas: for example, we know there are genetic variants whose allele frequencies increased substantially during the black plague because they conferred significant resistance to the disease.
Some of those variants increas the risk of autoimmune disorders as well!
And given the infrequency of huge pandemics, you can't do as good of a job making tradeoffs between decreased autoimmune risk and pandemic susceptibility.
There is a HUGE amount of variance in human immune systems. There's so much variance in the Major Histocompatibility Complex that SNP arrays have noticeably increased error rates when we try to genotype people.
Would it be worth it to give the GMO babies a random subset of the changes to increase variation?
IMO no, but you actually bring up an interesting point: could we generate more genetic diversity by giving huge numbers of babies random variants and seeing what they do?
I think you probably could but it would take a very, very long time to suss out the full effect and incorporate it into your predictors for the next generation. Unless we have a global ban on AI development for many decades, I don't see any point.
As far as giving different people a different subset of trait-affecting alleles, sure. You could do that in embryos.
With adults though, the bottlenecks due to editing efficiency, no effects from genes that affect development and others mean that you probably don't want to "deselect" that many variants.
Maybe I should change the title of my post to match this one
Suddenly the Monty Python monks make so much more sense
cellular stress if on a large scale?
We already expect that too many editor proteins in the cells could be a problem. But that will show up in cell culture experiments and animal studies and we can modify doses, use more efficient editors, and do multiple rounds of editing to address it.
We also know about liver toxicity from too many lipid nanoparticles, but that's an addressable concern (use fewer nanoparticles, ensure they get into the target organ quickly)
I'm sure there will be others that I don't expect, but that's true with literally every new medical treatment. That's the whole point of running experiments in cell cultures and mice.
might be an exception where pleiotropy does actually matter, which would suck. the table in another comment showing correlations between illnesses is pretty convincing however it's possible there are effects that aren't quantified there (doesn't present as diagnosable disease)
We actually have some pretty good studies on plietropy between intelligence and other traits. The only consistently replicated effect I've seen which could be deemed negative is a correlation with mild aspbergers-like symptoms.
But you can just look at current people already alive to test the plieotropy hypothesis. Do unusually smart people have any serious problems that normal people don't?
The answer is pretty clearly "no". I expect that to continue being the case even if we push intelligence to the extremes of the current human range.
???? not sufficiently enmeshed in the bio space but this entire post gives off the vibe of "most of the components are bleeding edge and there aren't many papers, esp not large scale/long term ones" and I imagine that'll cause more issues than you expect and streeeetch timescales
This is true almost by definition for any new technology.
We have gotten nowhere near as much as we could out of behavioral interventions (on long timescales) and nootropics, and both of those seem like better areas to put research time into. I don't actually think a research project of this scale will be faster (for AI safety research etc) than either of those.
I would be very surprised if a pharmaceutical, or even a bunch of pharmaceuticals could replicate the effects of gene editing. Imagine trying to create a set of compounds that coul replicate the effects of gene editing: you would need thousands of different compounds to individually target the pathways affected by all the variants. And you would need pharmaceuticals that would modulate their activity based on the current state of the cell. After all, that's how a lot of promoters and repressors and enhancers work; their activity depends on the state of the cell!
I still think people should work on nootropics. And you may of course be right that this won't be ready before AGI. I'd put the odds at maybe 20%.
But it COULD actually work! And if it did the impact would be absolutely massive! So like... why not? I might as well try.
From my conversations with people working on delivery vector, I'd guess you can probably get 20-40% with current AAVs and probably less with current lipid nanoparticles.
But that percentage has been increasing over time, and I suspect it will continue to do so. That's why I estimated that brain cells would, on average, receive 50% of the edits we attempt to make; by the time we're ready to do animal trials that is probably roughly where we'll be at.
Are edits somehow targeted at brain cells in particular or do they run throughout the body?
This is addressed in the post, but I understand that many people may not have read the whole thing because it's so long.
The set of cells that will be targeted depends on the delivery vector and on how it is customized. You can add custom peptides to both AAVs and lipid nanoparticles which will result in their uptake by a subset of tissues in the body.
Most of the ones I have looked at are taken up by several tissues among which is the brain. This is probably fine, but as stated in the appendix there's a chance expression of Cas9 proteins in non-target tissues will trigger the adaptive immune system.
If that did turn out to be a big issue, there are potential solutions which I only briefly touched on in the post. One is to just give someone an immunosuppressant for a few days while the editor proteins are floating around in the body. Another is to selectively express the editors in a specific tissue as specified by the mRNA transcribed uniquely in that cell type.
The latter would be a general purpose solution to avoiding any edits in any tissues except the target type, but would reduce efficiency. So not something that would be desirable unless it's necessary.
I hadn't even heard of Fanzors before you mentioned them. Very interesting.
So it's basically an endonuclease native to eukaryotes! After a brief search I haven't been able to find any papers in which it has been used as an editor.
The best case scenario here would be that the cieling for editing efficiency and specificity for Fanzor-based editors would be higher than for CRISPR-based alternatives. I am unsure at the moment whether that's true, and we likely won't know for a little while.
Minicircles are cool, but they just seem like... small plasmids? Granted I am not an expert on this topic and they definitely have advantages over SOME types of plasmids, such as those that contain CpG sequences, which can trigger TLR9 activation and get the immune system riled up.
I mean they're great in the sense that a plasmid that doesn't contain bacterial genes and can fit in a delivery vector like an AAV are great. I just don't really see them as a separate category of thing we haven't had before.
I am not saying plieotropy doesn't exist. I'm saying it's not as big of a deal as most people in the field assume it is.
Take disease risk for example. Here's a chart showing the genetic correlations between various conditions:
With a few notable exceptions, there is not very much correlation between different diseases.
And to the extent that plieotropy does exist, it mostly works in your favor. That's why most of the boxes are yellowish instead of bluish. Editing or selecting embryos to reduce the risk of one disease usually results in a tiny reduction of others.
Evolution succeeds by tinkering over many generations. It creates as many downsides as upsides. Who's going to volunteer to be tinkered upon?
Evolution cannot simultaneously consider data from millions of people when deciding which genetic variants to give someone. We can.
None of these proposals deal with novel genetic variants. Every target variant we would introduce is already present in tens of thousands of individuals and is known to not cause any monogenic disorder.
as long as there's a reason to think you'll get more upside than downside, which limited theory will provide
I'm not quite sure what you're getting at here. Do you believe it's impossible to make advantageous genetic tradeoffs? Or that there is no way to genetically alter organisms in a way that results in a net benefit?
However, there's no way the FDA is going to approve tinkering. You'd have to do this outside of US jurisdiction.
The FDA routinely approves clinical trials to treat fatal diseases with no effective treatments. There are many lethal brain disorders that satisfy this requirement; Alzheimer's, dementia, ALS, Parkinson's and others.
Would the FDA approve a treatment to enhance intelligence? Probably not, unless US citizens were flying out of the country to get it. But if you can treat a polygenic brain disorder like Alzheimer's with gene therapy, you can quite easily repurpose the platform to target intelligence by simply swapping the guide RNAs.
So there are two separate concerns:
One is a concern for people who are getting a single dose monogenic gene therapy who already have antibodies to an AAV delivery vector due to a natural infection. In these cases, doctors can sometimes switch the therapy to use an AAV with a different serotype that can't be attacked by the patient's existing antibodies. If that's not available, they'll sometimes give patients immunosupressants.
The problem is more relevant in the context of multiplex editing because you may not be able to make all the edits you'd like to in one round of therapy. You can only inject so many AAVs or lipid nanoparticles or what have you at a time. Cells only have a limited capacity to process and break down editor proteins, and other waste products of the editing process. So you may need to do multiple editing rounds to achieve the desired effect.
It will be a lot easier to do this if the delivery vector itself doesn't trigger the immune system. If it does, antibodies formed during the first round of edits will attack and destroy the the delivery vector. Maybe you can just give someone immunosupressants during each round of treatment? Or maybe you can just use a different AAV? There are potential solutions but I don't yet know which are likely to work best.
Yes, I think many in the field would share this viewpoint and that's part of why we haven't seen someone already attempt this.
I disagree for reasons I've shared in my post on "Black Box Biology", but it's worth reiterating my reasons here:
- You don't need to understand the causal mechanism of genes. Evolution has no clue what effects a gene is going to have, yet it can still optimize reproductive fitness. The entire field of machine learning works on black box optimization.
- Most genetic variants (especially those that commonly vary among humans, which are the ones we would be targeting) have linear effects on a single trait. We don't actually need to worry about gene-gene interactions that much.
- To the degree plieotropy does exist and is a concern, you can optimize your edit targeting criteria according to multiple traits. For example, you could try to edit to reduce (or at the very least keep constant) the risk of schizophrenia and other mental disorders.
- (As stated in the post), a delivery vector that doesn't induce an adaptive immune response can be administered in multiple rounds, with a relatively small number of edits made each time, further decreasing the risk of large side-effects.
As far as regulation goes, we've already approved one CRISPR-based gene therapy in the US. I see no reason to expect that you couldn't conduct a clinical trial to treat a polygenic brain disease like Alzheimers or treatment resistant depression. That's why in my roadmap I proposed clinical trails for treating a fatal brain disorder as a first step before we tackle intelligence.
Thanks! I've added a link to the paper by Cory Smith and others to the relevant section and updated the section about CAR T-cell therapy to read "adds a single gene" rather than "modifies a single gene"
I was under the impression that the new gene usually integrated into the cell's genome. But that impression was from a conversation with GPT-4 so perhaps I'm mistaken. Or perhaps new gene insertions are not considered gene editing?
I love LessWrong. I have better discussions here than anywhere else on the web.
I think I may have a slightly different experience with the site than the modal user because I am not very engaged in the alignment discourse.
I've found the discussions on the posts I've written to be of unusually high quality, especially the things I've written about fertility and polygenic embryo screening.
I concur with other comments about the ability to upvote and agree/disagree with a comment to be a great feature which I use all the time.
My number one requested feature continues to be the ability to see a retention graph on the posts I've written, i.e. where do people get bored and stop reading? After technical accuracy my number one goal is to write something interesting and engaging, but I lack any kind of direct feedback mechanism to optimize my writing in that way.