Thanks! I spent an embarrassingly long time writing it.
Your question about iterated embryo selection is an interesting one. I suspect that performing this procedure multiple times without adding genetic material WOULD result in higher defect rates, though I’m not positive. If one is already selecting against the types of negative traits that inbreeding increases, would we still expect to see higher rates of health conditions even after selection, or would inbreeding simply decrease our the average quality of embryos due to a higher percent having health issues?
Part of my problem is not understanding exactly why inbreeding is bad. I’m familiar with the standard answer that “inbreeding increases the chance that offspring inherit recessive diseases”, but why exactly is that? One answer is Muller’s ratchet, which says that environmental damage leads to a constant increase in deleterious mutations to the germline, and the only feasible way to decrease mutational load is through sex. Under this model, sex is kind of like a simple error correction mechanism: a single mutation is unlikely to occur in both organisms, so given the production of enough offspring, one of them is likely to have reduced mutational load.
So under this model, inbreeding is bad because it correlates genetic mutations. If two organisms share a larger portion of their DNA, they are likely to inherit many of the same mutations, preventing their descendants from shedding mutational load through lucky recombination.
But if that analysis is correct and inability to shed mutational load is the main reason for increased health problems among inbred offspring, then perhaps it wouldn’t be such a big deal for iterated embryo selection after all, since there is very little time between generations for the embryos to accrue deleterious mutations.
In the end though, one almost certainly would want to introduce genetic material from other parents simply because it would allow for additional valuable genetic material from which to select.
I wonder how much of it can be avoided by both optimizing for a positive trait X while simultaneously optimizing against the traits of people with negative life outcomes.
You’ve put your finger right on one of the most important questions for the future of this field. If we simply add enough important traits to our linear equation, can we push as far as we want into the tails of these trait distributions? I think we should be able to get at least 3 or 4 standard deviations from the mean of most traits with this method. Possibly much further. But how far can we actually go before we end up optimizing against some important trait that we simply didn’t include in the equation because we either didn’t think about it or didn’t realize it was important?
This is one of the reasons I’ve tried to keep up with AI safety research. They are far far ahead of biologists in understanding how to properly frame and begin to answer these types of questions. If you squint hard enough, trait enhancement with iterated embryo selection starts to look a lot like iterated amplification and distillation, and the question of how far we should push into the tails of the distributions becomes a question of how high we can safely set the learning rate.
One of the topics I’d like to write about in the future is how to apply ideas from AI safety to the field of genetic enhancement. It’s actually quite hard to do this rigorously because many of the assumptions that underlie techniques to align AI don’t really work with genetic engineering. With humans, you have an insanely long lag before you can validate that the changes actually worked, and the cost of getting things wrong is huge. With software models, you can explore actions that your current policy says aren’t optimal. Models can be updated very quickly. But with humans the cost of exploration is extremely high.
And unless everyone in this community is wildly off about their timelines to transformative AI, we may only get one genetically engineered generation before biological organisms like ourselves cease to be relevant to the larger strategic picture.
Thanks! I spent an embarrassingly long time writing it.
Your question about iterated embryo selection is an interesting one. I suspect that performing this procedure multiple times without adding genetic material WOULD result in higher defect rates, though I’m not positive. If one is already selecting against the types of negative traits that inbreeding increases, would we still expect to see higher rates of health conditions even after selection, or would inbreeding simply decrease our the average quality of embryos due to a higher percent having health issues?
Part of my problem is not understanding exactly why inbreeding is bad. I’m familiar with the standard answer that “inbreeding increases the chance that offspring inherit recessive diseases”, but why exactly is that? One answer is Muller’s ratchet, which says that environmental damage leads to a constant increase in deleterious mutations to the germline, and the only feasible way to decrease mutational load is through sex. Under this model, sex is kind of like a simple error correction mechanism: a single mutation is unlikely to occur in both organisms, so given the production of enough offspring, one of them is likely to have reduced mutational load.
So under this model, inbreeding is bad because it correlates genetic mutations. If two organisms share a larger portion of their DNA, they are likely to inherit many of the same mutations, preventing their descendants from shedding mutational load through lucky recombination.
But if that analysis is correct and inability to shed mutational load is the main reason for increased health problems among inbred offspring, then perhaps it wouldn’t be such a big deal for iterated embryo selection after all, since there is very little time between generations for the embryos to accrue deleterious mutations.
In the end though, one almost certainly would want to introduce genetic material from other parents simply because it would allow for additional valuable genetic material from which to select.
You’ve put your finger right on one of the most important questions for the future of this field. If we simply add enough important traits to our linear equation, can we push as far as we want into the tails of these trait distributions? I think we should be able to get at least 3 or 4 standard deviations from the mean of most traits with this method. Possibly much further. But how far can we actually go before we end up optimizing against some important trait that we simply didn’t include in the equation because we either didn’t think about it or didn’t realize it was important?
This is one of the reasons I’ve tried to keep up with AI safety research. They are far far ahead of biologists in understanding how to properly frame and begin to answer these types of questions. If you squint hard enough, trait enhancement with iterated embryo selection starts to look a lot like iterated amplification and distillation, and the question of how far we should push into the tails of the distributions becomes a question of how high we can safely set the learning rate.
One of the topics I’d like to write about in the future is how to apply ideas from AI safety to the field of genetic enhancement. It’s actually quite hard to do this rigorously because many of the assumptions that underlie techniques to align AI don’t really work with genetic engineering. With humans, you have an insanely long lag before you can validate that the changes actually worked, and the cost of getting things wrong is huge. With software models, you can explore actions that your current policy says aren’t optimal. Models can be updated very quickly. But with humans the cost of exploration is extremely high.
And unless everyone in this community is wildly off about their timelines to transformative AI, we may only get one genetically engineered generation before biological organisms like ourselves cease to be relevant to the larger strategic picture.