I’m a software developer by training with an interest in genetics. I currently run a startup working on multiplex gene editing technology.
GeneSmith
Karma: 5,125
This is one of my favorite articles I’ve read on this website in months. Since I’m guessing most people won’t read the whole thing, I’ll just quote a few of the highlights here:
Measles is an unremarkable disease based solely on its clinical progression: fever, malaise, coughing, and a relatively low death rate of 0.2%~. What is astonishing about the disease is its capacity to infect cells of the adaptive immune system (memory B‑ and T-cells). This means that if you do end up surviving measles, you are left with an immune system not dissimilar to one of a just-born infant, entirely naive to polio, diphtheria, pertussis, and every single other infection you received protection against either via vaccines or natural infection. It can take up to 3 years for one’s ‘immune memory’ to return, prior to which you are entirely immunocompromised.
I had literally no idea Measles did this. As if I needed another reason to get vaccinated.
On the highest end, Alzheimer’s received $3538M in funding in 2023, and caused 451 DALYs per 100k people worldwide. So, 3538:451, or 7.8.
Then Crohn’s Disease, which has the ratio 92:20.97 (4.3).
Slightly lower is diabetes, 1187:801.5 (1.4).
Close to it is epilepsy, 245:177.84 (1.6).
Finally, near the bottom of the list is endometriosis, 29:56.61, or .5.
It’s kind of shocking there is such a big difference between diseases when it comes to funding. Literally a 16x discrepancy between Alzheimer’s funding and endometriosis (and a 52x difference between Alzheimer’s and COPD!) I so wish that DOGE had been functional because it’s exactly situations like this that pose the biggest opportunity for improved government operations.
I think the most interesting part of this disease is how it’s kind of sort of not really a form of cancer. It’s basically cancer that causes a lot of issues but very rarely grows in the aggressive way that other cancers do.
The fact that you literally find endometrial lesions with some of the mutations that are hallmarks of cancer implies that there’s probably a lot of endometriosis cases that are cleared up by the immune system naturally which no one ever finds out about. These mutations show up because they provide a survival advantage to the endometrial cells.
Minor nitpick about heritability
Lastly I have a very minor nitpick. The Nature paper you linked ostensibly showing very high heritability doesn’t actually mention heritability in the abstract. The paper made a genetic predictor for endometriosis which explained 5% of the variance (not particularly high, especially given the sample size they were working with).
It does cite a paper about heritability, but that paper doesn’t showing endometriosis as being unusually heritable; it shows 47% of the variance can be explained by additive genetic factors. That’s pretty middle-of-the-pack as far as heritability goes. Conditions like Alzheimer’s and Schizophrenia are significantly more heritable; roughly 70% and 80% respectively.
Actual heritability of Endometriosis is likely somewhat higher than that because most conditions have some non-additive genetic variance. This paper (somewhat questionably) attributes the entire remainder of the variance to “unique environmental factors”.
The actual genetics of the disease itself are almost shockingly polygenic. The study had 61k cases, yet only identified 42 genome-wide significant hits. I can’t look at the rest of the paper due to a paywall (SciHub has stopped archiving new articles :() but it seems there aren’t any especially common alleles with large effect sizes.
This actually strongly the supports the “multiple causes of endometriosis” narrative you explore in your post: if there are that many genetic variants with small effects, there are probably many different ways the disease can manifest (or at least many influences on when and how it shows up).
Sounds like we should talk
I agree this is a worry. Apart from this stuff just not mattering because AI takes over first, dramatic acceleration of inequality is my biggest worry.
This tech almost certainly WILL accelerate inequality at the start. But in the long run I think there’s no reason we can’t make gene editing available for almost everyone.
Editing reagents are cheap. We’re working with at most a few microliters of editing agents (more realistically a few nanoliters).
It costs a lot of money to collect the data and put it into biobanks, but once that is done you’ve got the data forever.
And at SCALE the cost of absolutely everything comes down.
Maybe we’ll get there someday. I think for the next decade at least it’s going to be hard to beat lead paint elimination or animal welfare initiatives that get a hundred million chickens out of battery cages.
Care to explain how you think it’s being misused?
I find this argument fairly compelling. I also appreciate the fact that you’ve listed out some ways it could be wrong.
Your argument matches fairly closely with my own views as to why we exist, namely that we are computationally irreducible
It’s hard to know what to do with such a conclusion. On the one hand it’s somewhat comforting because it suggests even if we fuck up, there are other simulations or base realities out there that will continue. On the other hand, the thought that our universe will be terminated once sufficient data has been gathered is pretty sad.
DE-FACTO UPLOADING
Imagine for a moment you have a powerful AI that is aligned with your particular interests.
In areas where the AI is uncertain of your wants, it may query you as to your preferences in a given situation. But these queries will be “expensive” in the sense that you are a meat computer that runs slowly, and making copies of you is difficult.
So in order to carry out your interests at any kind of scale with speed, it will need to develop an increasingly robust model of your preferences.
Human values are context-dependent (see shard theory and other posts on this topic), so accurately modeling one’s preferences across a broad range of environments will require capturing a large portion of one’s memories and experiences, since those things affect how one responds to certain stimuli.
In the limit, this internal “model” in the AI will be an upload. So my current model is that we just get brain uploading by default if we create aligned AGI.
I’m not sure I buy that they will be more cautious in the context of an “arms race” with a foreign power. The Soviet Union took a lot of risks their bioweapons program during the cold war.
My impression is the CCP’s number one objective is preserving their own power over China. If they think creating ASI will help them with that, I fully expect them to pursue it (and in fact to make it their number one objective)
So in theory I think we could probably validate IQ scores of up to 150-170 at most. I had a conversation with the guys from Riot IQ and they think that with larger sample sizes the tests can probably extrapolate out that far.
We do have at least one example of a guy with a height +7 standard deviations above the mean actually showing up as a really extreme outlier due to additive genetic effects.
The outlier here is Shawn Bradley, a former NBA player. Study here
Granted, Shawn Bradley was chosen for this study because he is a very tall person who does not suffer from pituitary gland dysfunction that affects many of the tallest players. But that’s actually more analogous to what we’re trying to do with gene editing; increasing additive genetic variance to get outlier predispositions.
I agree this is not enough evidence. I think there are some clever ways we can check how far additivity continues to hold outside of the normal distribution, such as checking the accuracy of predictors at different PGSes, and maybe some clever stuff in livestock.This is on our to-do list. We just haven’t had quite enough time to do it yet.
The second point is the distinction between causal for the association observed in the data, and causal when intervening on the genome, I suspect more than half of the gene is only causal for the association. I also imagine there are a lot of genes that are indirectly causal for IQ such as making you an attentive parent thus lowering the probability your kid does not sleep in the room with a lot of mold, which would not make the super baby smarter, but it would make the subsequent generation smarter.
There are some, but not THAT many. Estimates from EA4, the largest study on educational attainment to date, estimated the indirect effects for IQ at (I believe) about 18%. We accounted for that in the second version of the model.
It’s possible that’s wrong. There is a frustratingly wide range of estimates for the indirect effect sizes for IQ in the literature. @kman can talk more about this, but I believe some of the studies showing larger indirect effects get such large numbers because they fail to account for the low test-retest reliability of the UK biobank fluid intelligence test.
I think 0.18 is a reasonable estimate for the proportion of intelligence caused by indirect effects. But I’m open to evidence that our estimate is wrong.
I’m being gaslit so hard right now
One data point that’s highly relevant to this conversation is that, at least in Europe, intelligence has undergone quite significant selection in just the last 9000 years. As measured in a modern environment, average IQ went from ~70 to ~100 over that time period (the Y axis here is standard deviations on a polygenic score for IQ)
The above graph is from David Reich’s paper
I don’t have time to read the book “Innate”, so please let me know if there are compelling arguments I am missing, but based on what I know the “IQ-increasing variants have been exhausted” hypothesis seems pretty unlikely to be true.
There’s well over a thousand IQ points worth of variants in the human gene pool, which is not what you would expect to see if nature had exhaustively selected for all IQ increasing variants.
Unlike traits that haven’t been heavily optimized (like resistance to modern diseases)
Wait, resistance to modern diseases is actually the single most heavily selected for thing in the last ten thousand years. There is very strong evidence of recent selection for immune system function in humans, particularly in the period following domestication of animals.
Like there has been so much selection for human immune function that you literally see higher read errors in genetic sequencing readouts in regions like the major histocompatibility complex (there’s literally that much diversity!)
but suggests the challenge may be greater than statistical models indicate, and might require understanding developmental pathways at a deeper level than just identifying associated variants.
If I have one takeaway from the last ten years of deep learning, it’s that you don’t have to have a mechanistic understanding of how your model is solving a problem to be able to improve performance. This notion that you need a deep mechanical understanding of how genetic circuits operate or something is just not true.
What you actually need to do genetic engineering is a giant dataset and a means of editing.
Statistical methods like finemapping and adjusting for population level linkage disequilibrium help, but they’re just making your gene editing more efficient by doing a better job of identifying causal variants. They don’t take it from “not working” to “working”.
Also if we look at things like horizontal gene transfer & shifting balance theory we can see these as general ways to discover hidden genetic variants in optimisation and this just feels highly non-trivial to me? Like competing against evolution for optimal information encoding just seems really difficult apriori? (Not a geneticist so I might be completely wrong here!)
Horizontal gene transfer doesn’t happen in humans. That’s mostly something bacteria do.
There IS weird stuff in humans like viral DNA getting incorporated into the genome, (I’ve seen estimates that about 10% of the human genome is composed of this stuff!) but this isn’t particularly common and the viruses often accrue mutations over time that prevents them from activating or doing anything besides just acting like junk DNA.
Occasionally these viral genes become useful and get selected on (I think the most famous example of this is some ancient viral genes that play a role in placental development), but this is just a weird quirk of our history. It’s not like we’re prevented from figuring out the role of these genes in future outcomes just because they came from bacteria.
Sorry, I’ve been meaning to make an update on this for weeks now. We’re going to open source all the code we used to generate these graphs and do a full write-up of our methodology.
Kman can comment on some of the more intricate details of our methodology (he’s the one responsible for the graphs), but for now I’ll just say that there are aspects of direct vs indirect effects that we still don’t understand as well as we would like. In particular there are a few papers showing a negative correlation between direct and indirect effects in a way that is distinct for intellligence (i.e. you don’t see the same kind of negative correlation for educational attainment or height or anything like that). It’s not clear to us at this exact moment what’s actually causing those effects and why different papers disagree on the size of their impact.
In the latest versions of the IQ gain graph we’ve made three updates:
We fixed a bug where we squared a term that should not have been squared (this resulted in a slight reduction in the effect size estimate)
We now assume only ~82% of the effect alleles are direct, further reducing benefit. Our original estimate was based on a finding that the direct effects of IQ account for ~100% of the variance using the LDSC method. Based on the result of the Lee et al Educational Attainment 4 study, I think this was too optimistic.
We now assume our predictor can explain more of the variance. This update was made after talking with one of the embryo selection companies and finding their predictor is much better than the publicly available predictor we were using
The net result is actually a noticeable increase in efficacy of editing for IQ. I think the gain went from ~50 to ~85 assuming 500 edits.
It’s a little frustrating to find that we made the two mistakes we did. But oh well; part of the reason to make stuff like this public is so others can point out mistakes in our modeling. I think in hindsight we should have done the traditional academic thing and ran the model by a few statistical geneticists before publishing. We only talked to one, and he didn’t get into enough depth for us to discover the issues we later discovered.
I’ve been talking to people about this today. I’ve heard from two separate sources that it’s not actually buyable right now, though I haven’t yet gotten a straight answer as to why not.
Congrats on the new company! I think this is potentially quite an important effort, so I hope anyone browing this forum who has a way to get access to biobank data from various sources will reach out.
One of my greatest hopes for these new models is that they will provide a way for us to predict the effects of novel genetic variants on traits like intelligence or disease risk.
When I wrote my post on adult intelligence enhancement at the end of 2023, one of the biggest issues was just how many edits we would need to make to achieve significant change in intelligence.
Neither delivery nor editing tech is good enough yet to make anywhere close to the number of edits needed for adult cognitive enhancement. But it’s plausible that there exist a set of gene edits such that just 10 or 20 changes would have a significant impact.One of my greatest hopes is that these foundation models will generalize well enough out-of-distribution that we can make reasonably accurate predictions about the effect of NEW genetic variants on traits like intelligence of health. If we can, (and ESPECIALLY if we can also predict tolerance to these edits), it could 10x the impact of gene editing.
This may end up being one of the most important technologies for adult cognitive enhancement. So I hope anyone who might be able to help with data access reaches out to you!
I’ve checked. Have heard from multiple people they “it’s not for sale in reality”
I don’t have any details yet. But obviously am interested.
I certainly hope we can do this one day. The biobanks that gather data used to make the predictors we used to identify variants for editing don’t really focus on much besides disease. As a result, our predictors for personality and interpersonal behavior aren’t yet very good.
I think as the popularity of embryo selection continues to increase, this kind of data will be gathered in exponentially increasing volumes, at which point we could start to think about editing or selecting for the kinds of traits you’re describing.
There will be an additional question to what degree parents will decide to edit for those traits. We’re going to have a limited budget for editing and for selection for quite some time, so parents will have to choose to make their child kinder and more benificent to others at the expense of some other traits. The polygenicity of those personality traits and the effect sizes of the common alleles could have a very strong effect on parental choices; if you’re only giving up a tiny bit to make your child kinder then I think most parents will go for it. If it’s a big sacrifice because it requires like 100 edits, I think far fewer will do so.
It may be that benificence towards others will make these kinds of children easier to raise as well, which I think many parents would be interested in.
In the last year it has really hit me at a personal level what graphs like these mean. I’m imagining driving down to Mountain View and a town once filled with people who had “made it” and seeing a ghost town. No more jobs, no more prestige, no more promise of a stable life. As the returns to capital grow exponentially and the returns to labor decline to zero, the gap between the haves and the have-nots will only grow.
If someone can actually get superintelligence to do what they want, then perhaps universal basic income can at the very least prevent actual starvation and maybe even provide a life of abundance.
But I can’t help but feeling such a situation is fundamentally unstable. If the government’s desires become disconnected from those of the people at any point, by what mechanism can balance be restored?
In the past the government was fundamentally reliant on its citizens for one simple reason; citizens produced taxable revenue.
That will no longer be the case. Every country will become a petro state on steroids.
I spoke with one of the inventors of bridge recombinases at a dinner a few months ago and (at least according to him), they work in human cells.
I haven’t verified this independently in my lab, but it’s at least one data point.
On a broader note, I find the whole field of gene therapy very confusing. In many cases it seems like there are exceptionally powerful tools that are being ignored in favor of sloppy, dangerous, imprecise alternatives.
Why are we still using lentiviral vectors to insert working copies of genes when we can usually just fix the broken gene using prime editors?
You look at gene therapies like Casgevy for sickle cell and they just make no fucking sense.
Sickle cell is predominantly cause by an adenine to thymine swap at the sixth codon in the HBB gene. Literally one letter change at a very well known spot in one protein.
You’d think this would be a perfect use case for gene editing, right? Just swap out that letter and call it a day!
But no. This is not how Casgevy works. Instead, Casgevy works by essentially flipping a switch to make the body stop producing adult hemoglobin and start producing fetal hemoglobin.
Fetal hemoglobin doesn’t sickle, so this fixes sickle cell. But like… why? Why not just change the letter that’s causing all the problems in the first place?
It’s because they’re using old school Cas9. And old school Cas9 editing is primarily used to break things by chopping them in half and relying on sloppy cellular repair processes like non-homologous end joining to stitch the DNA back together in a half-assed way that breaks whatever protein is being produced.
And that’s exactly what Casgevy does; it uses Cas9 to induce a double stranded break in BCL11A, a zinc finger transcription factor that normally makes the cells produce adult hemoglobin instead of the fetal version. Once BCL11A is broken, the cells start producing fetal hemoglobin again.
But again...
Why?
Prime editors are very good at targeting the base pair swap needed to fix sickle cell. They’ve been around for SIX YEARS. They havery extremely low rates of off-target editing. Their editing efficiency is on-par with that of old-school Cas9. And they have lower rates of insertion and deletion errors near the edit site. So why don’t we just FIX the broken base pair instead of this goofy work-around?
Yet the only thing I can find online about using them for sickle cell is a single line announcement from Beam Therapeutics that vaguely referecing a partnership with prime medicine that MIGHT use them for sickle cell.
This isn’t an isolated incident either. You go to conferences on gene editing and literally 80% of academic research is still using sloppy double strand breaking Cas9 to do editing. It’s like if all the electric car manufacturers decided to use lead acid batteries instead of lithium ion.
It’s just too slow. Everything is too fucking slow. It takes almost a decade to get something from proof of concept to commercial product.
This, more than anything, is why I hope special economic zones like Prospera win. You can take a therapy from animal demonstration to commercial product in less than a year for $500k-$1 mil. If we had something like that in the US there would be literally 10-100x more therapeutics available.
I mean… I think adult gene therapy is great! It can cure diseases and provide treatments that are otherwise impossible. So I think it’s more impactful than heated seats.
It never really got any traction. And I think you’re right about the similarity to eugenics somewhat defeating the purpose.
I think terms like “reproductive freedom” or “reproductive choice” actually get the idea across better anyways since you don’t have to stop and explain the meaning of the word.