Meiosis is all you need
denovo.substack.com
denovo.substack.com
* Lots can go wrong during meiosis; many gametes have severe genetic abnormalities (e.g. aneuploidy). Due to these abnormalities, upwards of 20% of conceptions result in spontaneous abortion (often before the mother even knows she’s pregnant).
* Normal stem cell lines already have a proclivity to undergo cancer-like selection when being cultured, acquiring growth-enhancing mutations.
* Haploid cell lines are genetically unstable and have a proclivity to revert back to a diploid state (or quickly acquire chromosomal abnormalities in the process of trying to revert to a diploid state).
* Haploid cell lines that manage to remain haploid can select for cancerous mutations much more quickly than their diploid counterparts.
This doesn’t even touch on issues with polygenic scores, or that optimizing for a genome with the most alleles contributing to a PGS for a given trait has no guarantee of actually maximizing that trait, since many alleles are epistatic—their effects are dependent on the presence or absence of a specific set of other alleles, which is not captured by the simple linear mixed models used to compute a PGS. Analogously, throwing together all the most commonly used spices across all 3 Michelin star restaurants would result in a horrible dish, even though each spice on its own would contribute highly to a “polyspice dish score.”
Even if we had some magic way of solving all the aforementioned problems, we still have no way of knowing that optimizing for a certain set of alleles that definitively results in some desirable traits would not also inadvertently result in some other horrible traits, which we would have no way of knowing from population statistics because they’ve been selected out of the population.
Agreed! To be a bit more charitable to polygenic scores, they can be mediocre descriptive models—given a real individual’s genotype, they can sometimes decently predict their phenotype (if you consider explaining ~10% of phenotypic variance “decent”).
They are unequivocally horrible generative models—you will not obtain a synthetic genotype that would result in a desired phenotype by sampling from a PGS model, i.e. by randomly maximizing the number of high-scoring PGS alleles in a synthetic genome, as proposed by the article.
These are all valid concerns (especially about spontaneous diploidization), but I think they can be overcome with sufficiently rigorous quality control (probably whole genome sequencing, not just genotyping).
The issues with epistasis (i.e. will polygenic scores continue to be valid at extreme values) are also worth noting. I think that improved models (besides the linear ones in current use) are likely to solve this though.
The hard part is actually doing the meiosis.
I think we're still very far from arbitrary genetic editing and whole genome design and synthesis. I don't know how much of this is "technically feasible but way too expensive" vs "we have promising research directions on some of the challenges, but definitely don't know how to do this effectively at all", but it all adds up to that not being an option today.
The best we actually have available today is embryo selection. We don't know exactly what phenotype each embryo will express, but we've got enough statistical clues that we can make fairly good guesses most of the time. So, you make 100 embryos, sequence their DNA, then choose whichever embryo is given the best score by your statistical analysis, and use that one.
Embryo selection is far from perfect, but it lets you avoid any genetic conditions that we know how to detect, and it lets you get an embryo with more of the preferred variants of genes statistically correlated with whatever you want to select on, and it's absurdly cheaper and more efficient than alternatives like "Let each embryo grow for 30 years, then evaluate the results of the genome".
With iterated embryo selection, the idea is that instead of one large batch of 100, you do a small batch of 10, choose the best from that group, and use that as source material to make another batch of 10. This acts as a ratchet, letting you get more selection with fewer embryos.
So, the point of finding an input to a function on a computer that gets a good score is that it's the cheapest, most efficient way we know of so far to get a human embryo whose genome scores highly on this function. There are also some other nice properties, like having the embryo be highly correlated with the genomes of the parents, which a lot of people like, and is something you can explicitly add to the scoring if you want.
Just like any selection/optimization procedure, you can get some pretty bad outcomes when you go to extremes, as for a lot of traits, your function on the computer is only a statistical model of observed correlations, not a real comprehensive model of what the genome means and does. I imagine you're probably going to get something with severe issues if you tried to grow the embryo you get from 10,000 rounds of IES.
Embryo selection is trying to use what we think we know to choose the best embryo, given only the genetics. We don't have perfect knowledge, but we do have some knowledge, and we can make some bounded use of it.
I think this is the really hard part, and part which cannot be made reliable without growing enough intermediate specimens sometimes and assessing their success in the actual environment.
If we were actually any good at predicting the success of an organism just from genetic composition, we'd be able to just genetically engineer a highly successful variant, obviating the many intermediate steps.
* If you're not evaluating/testing phenotypes in between anyway, why not just directly synthesize or modify your desired genetic sequence?
* If you're using genes to actually directly achieve some learning goal, have you looked at hypermutation?
Thinking in snps and haplotypes has been tripping up geneticists for years. Everything in genomics is highly nonlinear, not additive, and with massive amounts of long range interactions, feedback, and state.
Also organismal phenotypes are very different from molecular phenotypes- often organismal phenotypes are actually several underlying things grouped with a common label simply because they have similar features.
If it's for eugenics I'm done with HN.
"Note: this post is still a rough draft but I think this idea is important enough to publish it anyway. I intend to revise it in the future."
And gametogenesis research has been advancing rapidly recently, so it probably won't even be that long. One can do stem cell->gamete and successfully create offspring in mice https://www.gwern.net/docs/genetics/gametogenesis/2021-yoshi... and there are already startups to try to do it for humans: https://www.technologyreview.com/2021/10/28/1038172/concepti...