I've started a gene therapy company, raised money, opened a lab, hired the inventor of one of the best multiplex gene editing techniques to be our chief scientific officer, and am currently working on cell culture experiments with the help of a small team.

I may write a post about what's happened at some point. But things are moving.

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!

It's probably worth noting that there's enough additive genetic variance in the human gene pool RIGHT NOW to create a person with a predicted IQ of around 1700.

You're not going to be able to do that in one shot due to safety concerns, but based on how much we've been able to influence traits in animals through simple selective breeding, we ought to be able to get pretty damn far if we are willing to do this over a few generations. Chickens are literally 40 standard deviations heavier than their wild-type ancestors, and other types of animals are tens of standard deviations away from THEIR wild-type ancestors in other ways. A human 40 standard deviations away from natural human IQ would have an IQ of 600.

Even with the data we have TODAY we could almost certainly make someone in the high 100s to low 200s just with gene editing an a subset of the not-all-that-great IQ test data we've already collected:

If one of the big government biobanks just allows the data that has ALREADY BEEN COLLECTED to be used to create an IQ predictor, we could nearly double the expected gain (in fact, we would more than double it for higher numbers of edits)

All we need is time. In my view it's completely insane that we're rolling the dice on continued human existence like this when we will literally have human supergeniuses walking around in a few decades.

The biggest bottleneck for this field is a reliably technique to convert a stem cell to an embryo. There's a very promising project that might yield a workable technique to do that and the guy who wants to run it can't because he doesn't have $4 million to do primate testing (despite the early signs showing it will pretty plausibly work).

If we have time, human genetic engineering literally is the solution to the alignment problem. We are maybe 5-8 years out from being able to make above-average altruism, happy, healthy supergeniuses and instead of waiting a few more decades for those kids to grow up, we've collectively decided to roll the dice on making a machine god.

We have this incredible situation right now where the US government is spending tens of billions of dollars on infrastructure designed to make all of its citizens obsolete, powerless and possibly dead, yet won't even spend a few million on research to make humans better.

I’ve seen this and will reply in the next couple of days. I want to give it the full proper response it deserves.

Also thanks for taking the time to write this. I don’t think I would get this level or quality of feedback anywhere else online outside of an academic journal.

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.

It's a good question. The remarkable thing about human genetics is that most of the variants ARE additive.

This sounds overly simplistic, like it couldn't possible work, but it's one of the most widely replicated results in the field.

There ARE some exceptions. Personality traits seem to be mostly the result of gene-gene interactions, which is one reason why SNP heritability (additive variance explained by common variants) is so low.

But for nearly all diseases and for many other traits like height and intelligence, ~80% of variance is additive. somewhere between 50 and 80% of the heritable variance is additive. And basically ALL of the variance we can identify with current genetic predictors is additive.

This might seem like a weird coincidence. After all, we know there is a lot of non-linearity in actual gene regulatory networks. So how could it be that all the common variants simply add together?

There's a pretty clear reason from an evolutionary point of view: evolution is able to operate on genes with additive effects much more easily than on those with non-additive effects.

The set of genetic variants inherited is scrambled every generation during the sperm and egg formation process. Those that need other common variants present to work their effects just have a much harder time spreading among the population because their benefits are inconsistent across generations.

So over time the genome ends up being enriched for additivity.

There IS lots of non-additivity happening in genes which are universal among the human population. If you were to modify two highly conserved regions, the effects of both edits could end up being much greater or much less than the sum of the effects of the two individual variants. But that's also not that surprising; evolution has had a lot of time to build dependencies on these regions, so we should expect modifying them to have effects that are hard to predict.

You also had a second question embedded within your first, which is about second order effects from editing, like increased IQ resulting in more mental instability or something.

You can just look at people who naturally have high IQ to see whether this is a concern. What we see is that, with the exception of aspbergers, higher IQ actually tends to be associated with LOWER rates of mental illness.

Also you can see from my chart looking at genetic correlations between diseases that, with a few exceptions, there just isn't that much correlation between diseases. The set of variants that affects two different diseases are mostly disjoint sets.

As someone who works in genetics and has been told for years he is a "eugenicist" who doesn't care about minorities, I understand your pain.

It's just part of the tax we have to pay for doing something that isn't the same as everyone else.

If you continue down this path, it will get easier to deal with these sorts of criticisms over time. You'll develop little mental techniques that make these interactions less painful. You'll find friends who go through the same thing. And the sheer repetitiveness will make these criticisms less emotionally difficult.

And I hope you do continue because the work you're doing is very important. When new technology causes some kind of change, people look around for the nearest narrative that suits their biases. The narratives in leftist spaces right now are insane. AI is not a concern because it uses too much water. It's not a concern because it is biased against minorities (if anything it is a little biased in favor of them!)

There is one narrative that I think would play well in leftist spaces which comes pretty close to the truth, and isn't yet popular:

AI companies are risking all of our lives in a race for profits

Simply getting this idea out there and more broadly known in leftist spaces is incredibly valuable work.

So I hope you keep going.

OpenAI's continued practice of publishing the blueprints allowing others to create more powerful models seems to undermine their claims that they are worried about "bad actors getting there first".

If you were a scientist working on the Manhattan project because you were worried about Hitler getting the atomic bomb first, you wouldn't send your research on centrifuge design to german research scientists. Yet every company that claims they are more likely than other groups to create safe AGI continues to publish the blueprints for creating AGI to the open web.

Is there any actual justification for this other than "The prestige of getting published in top journals makes us look impressive?"