LW - Superbabies: Putting The Pieces Together by sarahconstantin

The Nonlinear Library: LessWrong

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Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Superbabies: Putting The Pieces Together, published by sarahconstantin on July 12, 2024 on LessWrong. This post was inspired by some talks at the recent LessOnline conference including one by LessWrong user "Gene Smith". Let's say you want to have a "designer baby". Genetically extraordinary in some way - super athletic, super beautiful, whatever. 6'5", blue eyes, with a trust fund. Ethics aside[1], what would be necessary to actually do this? Fundamentally, any kind of "superbaby" or "designer baby" project depends on two steps: 1.) figure out what genes you ideally want; 2.) create an embryo with those genes. It's already standard to do a very simple version of this two-step process. In the typical course of in-vitro fertilization (IVF), embryos are usually screened for chromosomal abnormalities that would cause disabilities like Down Syndrome, and only the "healthy" embryos are implanted. But most (partially) heritable traits and disease risks are not as easy to predict. Polygenic Scores If what you care about is something like "low cancer risk" or "exceptional athletic ability", it won't be down to a single chromosomal abnormality or a variant in a single gene. Instead, there's typically a statistical relationship where many genes are separately associated with increased or decreased expected value for the trait. This statistical relationship can be written as a polygenic score - given an individual's genome, it'll crunch the numbers and spit out an expected score. That could be a disease risk probability, or it could be an expected value for a trait like "height" or "neuroticism." Polygenic scores are never perfect - some people will be taller than the score's prediction, some shorter - but for a lot of traits they're undeniably meaningful, i.e. there will be a much greater-than-chance correlation between the polygenic score and the true trait measurement. Where do polygenic scores come from? Typically, from genome-wide association studies, or GWAS. These collect a lot of people's genomes (the largest ones can have hundreds of thousands of subjects) and personal data potentially including disease diagnoses, height and weight, psychometric test results, etc. And then they basically run multivariate correlations. A polygenic score is a (usually regularized) multivariate regression best-fit model of the trait as a function of the genome. A polygenic score can give you a rank ordering of genomes, from "best" to "worst" predicted score; it can also give you a "wish list" of gene variants predicted to give a very high combined score. Ideally, "use a polygenic score to pick or generate very high-scoring embryos" would result in babies that have the desired traits to an extraordinary degree. In reality, this depends on how "good" the polygenic scores are - to what extent they're based on causal vs. confounded effects, how much of observed variance they explain, and so on. Reasonable experts seem to disagree on this.[2] I'm a little out of my depth when it comes to assessing the statistical methodology of GWAS studies, so I'm interested in another question - even assuming you have polygenic score you trust, what do you do next? How do you get a high-scoring baby out of it? Massively Multiplexed, Body-Wide Gene Editing? Not So Much, Yet. "Get an IVF embryo and gene-edit it to have the desired genes" (again, ethics and legality aside)[3] is a lot harder than it sounds. First of all, we don't currently know how to make gene edits simultaneously and abundantly in every tissue of the body. Recently approved gene-editing therapies like Casgevy, which treats sickle-cell disease, are operating on easy mode. Sickle-cell disease is a blood disorder; the patient doesn't have enough healthy blood cells, so the therapy consists of an injection of the patient's own blood cells which h...