Astral Codex Ten

GREML

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GREML

Article

GREML is a recurring concept in the Astral Codex Ten archive, appearing 2 times across 2 issues between June 26, 2025 and July 03, 2025. The archive places it in contexts such as “a mid-2010s technique called GREML allowed researchers to estimate the percent of variance in a trait”; “low estimates from GWAS and GREML (7% direct, 20% total)”; “polygenic scores, GWAS, GREML, RDR, and Sib-Regression are also strong designs”. It most often appears alongside Arthur Jensen, Awais Aftab, Cremieux.

Metadata

  • Category: Concepts
  • Mention count: 2
  • Issue count: 2
  • First seen: June 26, 2025
  • Last seen: July 03, 2025

Appears In

Source Context

Recovered passages from the original issue text. When the raw archive preserved outbound links inside the source passage, they are listed directly under the quote.

Missing Heritability: Much More Than You Wanted To Know
June 26, 2025 · Original source
For example, educational attainment is 50% uncorrelated with direct genetic effects. You need to square this to figure out what percent is causal; when you do that, you find that the polygenic score that explained 14% of EA is only 4%pp direct genes, with the other 10%pp being nondirect5 confounders. So yes, it seems like most polygenic scores that don’t validate within families are confounded. However unhappy we previously were that we had only found 14% of genes for EA (vs. 40% expected), we should now be much more unhappy - we really only know 4% of genes that directly cause EA. On the other hand, you might say - so before we only knew 14%pp out of 40%. Now we only know 4%pp out of 40%. This is discouraging, but it doesn’t fundamentally change what we know about nature vs. nurture. Both 4%pp and 14%pp are less than 40% - with either number, we must be missing something or doing something wrong. Probably that’s insufficient sample size. We’ll keep working on sample size and other things, and eventually scrounge up the missing 26%pp or 36%pp or whatever of the variance, so this doesn’t change anything. All it means is that one predictive method that the average person never knew about in the first place doesn’t work as well as we thought. Who cares? Not doctors. So far this research has only just barely begun to reach the clinic. But also, all doctors want to do is predict things (like heart attack risk). They don’t care if they use causal vs. nondirect genes. It doesn’t matter if you’re “only” at higher risk of heart attack because you’re black, or Norman, or because your parents read books to you - you still need more heart attack medication! Polygenic embryo selection companies should care. They offer polygenic scores that can be used to select healthier or smarter embryos. If the predictors they use rely partly on variants that aren’t causal within families, their real benefits could be far lower than advertised. I talked to one of these companies, who said they’d already adjusted for these effects and expected their competitors had too - the proper antidote to this problem, sibling controls, is a natural choice when you’re literally picking between siblings. The biggest losers are the epidemiologists. They had started using polygenic predictors as a novel randomization method; suppose, for example, you wanted to study whether smoking causes Alzheimers. If you just checked how many smokers vs. nonsmokers got Alzheimers, your result would be vulnerable to bias; maybe poor people smoke more and get more Alzheimers. But (they hoped) you might be able to check whether people with the genes for smoking get more Alzheimers. Poverty can’t make you have more or fewer genes! This was a neat idea, but if the polygenic predictors are wrong about which genes cause smoking and what effect size they have, then the less careful among these results will need to be re-examined. But the reason I spent so much time on the subject here is that this has confused a lot of people into thinking heritability itself was confounded and is actually just 4%. When I read my first few blog posts on these findings, I came away thinking they were claiming to have discredited twin studies and heritability. And although I take partial ownership of my own poor reading comprehension, I maintain that the way that the new anti-hereditarians discuss this is pretty bad. For example, Turkheimer’s treatment of the Tan study above is called Is Tan Et Al The End Of Social Science Genomics?, and includes passages like: The median [direct genomic effect] heritability for behavioral phenotypes is .048. Let that sink in for a second. How different would the modern history of behavior genetics be if back in the 80s one study after another had shown that the heritability of behavior was around .05? When Arthur Jensen wrote about IQ, he usually used a figure of .8 for the heritability of intelligence. I know that the relationship between twin heritabilities and SNP heritabilities is complicated, and in fact the DGE heritability of ability is one of the higher ones, at .2336. But still, it seems to me that the appropriate conclusion from these results is that among people who don’t have an identical twin, genomic information is a statistically non-zero but all in all relatively minor contributor to behavioral differences. And comments included things like: I don’t know if [this study] is the end of social science genomics, but it should certainly be the end of attributing significant genetic influence to behavioral traits (despite the recent scientist-generated cartoons touting genes for “income”). And: There's no doubt that this reported findings have dealt a fatal blow to my conviction that behavioral traits are pre-eminently heritable…This is a remarkable example of an objective statistical fact mercilessly crushing the more subjective experiential sense of "A looks and acts more like B than C because A and B have the same parents." This subjective evidence is almost unshakable and universal in its application as a tried and tested psychosocial heuristic. And yet, here we are. Turkheimer is either misstating the relationship between polygenic scores and narrow-sense heritability, or at least egging on some very confused people who are doing that, and the dynamic was bad enough that I got confused myself for a while. But even more confusing, the new anti-hereditarians actually are saying that lots of behavioral traits have very low heritability! But this point requires different arguments, only tangentially related to these. So let’s move on to… Is Heritability Genuinely Low? (Part 1: GWAS & GREML) In the mid 2010s, when genome-wide association studies (GWAS) based polygenic predictors were getting better every year, it was easy to hope they might reach 40% and close the “missing heritability”. But since then, progress has stalled. The second-to-last tripling of sample size, from 300K to 1M between 2016 - 2018, increased predictive power from 6% → 12%. The last tripling, from 1M to 3M between 2018 - 2022, only increased predictive power from 12% → 14%. If you graph sample size vs. predictive power, it looks like there's an asymptote between 15 - 20% or so. (of which - remember - only 5% is directly causal!) Worse, a mid-2010s technique called GREML allowed researchers to estimate the percent of variance in a trait that comes from the sorts of common genes studied in GWAS, without having to identify the genes involved. A 2016 GREML paper suggested that the maximum share of variance that GWASs of educational attainment could ever discover was about 21% (again, compared to 40% predicted genetic from twin studies). Since unavoidable methodological issues will prevent GWASs from reaching the literal maximum possible, this agrees with the evidence suggesting an asymptote between 15 - 20%. So either twin studies are wrong and traits are less heritable than believed, or the heritability must lie somewhere other than the common genes identifiable by GWAS. What about rare genes? GWASs focus on genetic variation common enough to be worth including in a basic genetic test. Most of this is single nucleotide polymorphisms (“SNPs”). A single nucleotide is one letter of DNA - for example, a C or a G. Polymorphisms are genes that commonly vary in humans - sometimes across races (for example, some humans have a gene for light skin, and other humans have a gene for dark skin), and other times within races (for example, some white people have a gene that makes cilantro taste like soap, and others don’t). So SNPs are single-letter spots in DNA where different people often have different letters. How often? Some people say 1%, but the more practical definition is “often enough that someone has noticed and added it to the test panel”. There are three billion letters in the genome, of which only a few million are commonly-tested SNPs. But these SNP studies have limited7 ability to measure personal mutations and rare variants. Sometimes your parents’ egg and sperm cells mess up copying a nucleotide of DNA, and you get a mutation that isn’t inherited from your ethnic group or even from your subgroup/family line - it’s just some idiosyncratic DNA change that you might be the first person in history to have. Since scientists have never seen this mutation before, they don’t know about it and can’t test for it without doing something more expensive than a simple SNP screen. And SNP studies have limited ability to detect anything more complicated than a single letter changing to another single letter. But some mutations are more complicated structural variants. For example, some bits of DNA get stuck on repeat - one person might have GATGAT, another person might have GATGATGATGAT, and a third person might have fifty GATs in a row. Other bits come out backwards. Sometimes a whole chunk of DNA goes missing, or moves to the wrong place. Occasionally a gene reads The Selfish Gene by Richard Dawkins, takes it too seriously, and evolves some ridiculous trick for spamming itself all over the genome. So if even the best molecular studies seem to be asymptoting around 15-20% of variance in educational attainment, but twin studies suggest it’s 40% genetic, might rare variants and structural variants make up the missing 20-25%pp? This remains a topic of bitter disagreement. On the one side, hereditarians bring up a Darwinian argument: imagine a genetic engineer who hopes to find the genes for educational attainment and edit them to make everyone smart and successful. She looks harder and harder, becoming more and more exasperated as they fail to materialize. Finally, she realizes she’s been scooped: evolution has been working on the same project, and has a 100,000 year head start. In the context of intense, recent selection for intelligence, we should expect evolution to have already found (and eliminated) the most straightforward, easy-to-find genes for low intelligence. Therefore, everything left should be convoluted or hidden or impossible to work with. So although this requires a sort of god-of-the-gaps argument - where we keep pushing heritability into whatever genes are too weird for existing techniques to detect - there are some reasons to think God really is in the gaps here. And a 2017 paper uses some clever techniques to estimate the share of intelligence variation lurking in hard-to-measure genes and finds it’s more than half: “By capturing these additional genetic effects, our models closely approximate the heritability estimates from twin studies for intelligence and education.” (see also Wainschtein 2022, Sidorenko 2024) The anti-hereditarians disagree. They cite papers like Zeng which measure the strength of selection on intelligence and suggest that it’s too weak to concentrate so much of the variation in rare genes8. And Sasha Gusev mentions Weiner 2023, which finds that in fact rare variants “explain 1.3% (SE = 0.03%) of phenotypic variance on average – much less than common variants” (other experts say that burden heritability only captures some rare variants and is not the right tool for this problem). But it may not even matter, because another set of findings suggests that heritability is genuinely low even when the rare variants are counted. Is Heritability Genuinely Low? (Part 2: Sib-Regression and RDR) Two newer methods, Sib-Regression and RDR, ask: using what we know from genetic studies, how much genetic variation do we think exists, total, across both common and rare genes? On average siblings share 50% of genes. But there’s a little randomness in meiosis, so some siblings might share 40% and others might share 60%. The more genetic influence on a trait, the more similar sibling pairs who share 60% of their genes will be, compared to sibling pairs who only share 40% of their genes. Since 60%-gene siblings and 40%-gene siblings are both equally part of the same family, you can use these numbers to calculate heritability unconfounded by a range of family factors. This is Sib-Regression. If you do a more complicated statistical process to extend the same idea to relatives other than siblings, it’s relatedness disequilibrium regression or RDR. GWAS asks: Looking at common easy-to-study genes, how much variation in a trait have we explained right now? GREML asks: looking at common easy-to-study genes, how much variation could we ever explain? But sib-regression and RDR ask a question more like twin studies: considering all genes, whether common / rare / easy-to-study / hard-to-study, how much variation is there total? This could address the rare variant objection mentioned above. And in many ways, these techniques are better than twin studies - Sib-Regression eliminates many potential biases, and RDR eliminates even more (although it’s harder to pull off, requiring more genetic information and computational resources). These techniques are new and hard-to-use, and only a few published studies have applied them to the sorts of behavioral traits we’re interested in: Young et al (2018) did Sib-Regression and RDR to genetic data from Iceland. Sib-regression found educational attainment = 40% (±15%) heritable, and RDR found 17% (±9%) heritable. Kemper et al (2021) did Sib-Regression only to genetic data from Britain. It found educational attainment = 14% heritable. This number conflicts with the 40% from the Young paper. Why? Unclear, but it could be selection bias - Young’s Icelandic sample was representative of the country; Kemper’s British population were Biobank volunteers who tend tend to be healthier and higher-class than the population at large. Upper-class people may have restricted range in educational attainment, or different factors affecting their educational attainment compared to the overall population. Either way, these are closer to the low estimates from GWAS and GREML (7% direct, 20% total), than to the higher estimates from twin studies (40%, generally presumed direct). And we can no longer use contributions from rare variants to paper over the difference. So what is going on? It seems like we have to accept one of three possibilities: Either something is wrong with twin studies. Or something is wrong with Sib-Regression and RDR (and then we can explain away GWAS and GREML by saying they’re missing rare variants). Or something is wrong with how we’re thinking about this topic and comparing things. What’s Going On? (Part 1: Is Something Wrong With Twin Studies?) Twin studies have dominated discussion of behavioral genetics for decades, so there’s a vast literature investigating their various assumptions and whether something might be wrong with them. Here are some of the assumptions and what the research says about each. Some of these will be duplicates of the GWAS confounders above, but we’ll go through them again anyway to review how they apply to twins. 1: Parents Treat Fraternal And Identical Twins The Same: Twin studies claim that twins are a uniquely powerful genetic laboratory; both fraternal and identical twin pairs have equally concordant environments, but identical twins have more concordant genes. Therefore, the more similar identical twin pairs are relative to fraternal twin pairs, the more heritable a trait must be. But this conclusion falls apart if identical twin pairs actually have more similar environments than fraternal twin pairs do, maybe because parents (knowing their twins are identical) treat them more similarly than they would fraternal twins. Would-be twin-study-discreditors have been trying to argue that this must be true for decades, but it’s always been a kind of quixotic battle. Remember, twin studies find many behavioral traits like IQ are >60% heritable, so you would need to prove not only that parents treat identical twin pairs differently from fraternal, but that this was an overwhelming effect. Parents of identical twins would have to obsessively expose them to the exact same stimuli in the exact same order; parents of fraternal twins would have to send one to the Gifted Advanced Placement Acceleration program while locking the other in a box and force-feeding them lead pellets. Common sense tells us there are no such differences, and studies confirm this: when parents are wrong about their twins’ status (eg they have fraternal twins, but falsely think they’re identical, or vice versa) their trait similarity matches their real status, rather than the incorrect status that determined how their parents treat them; parental treatment explains less than 1% of why identical twin pairs are more concordant (2, 3, 4). See also Felson 2013, which tries to measure environmental similarity and adjust for it, with minimal effects. Are these two cuties monozygotic or dizygotic? Are you sure? (answer) 2: Fraternal And Identical Twins Have Equally Concordant Uterine Environments: Fraternal twins have different sacs in the uterus and use different placentas. Most identical twins share a placenta, and some share an amniotic sac. If trait similarity is caused by sharing a placenta or sac (maybe because the placenta is defective, the fetal brain is starved of nutrients, and so the person has a lower IQ when they grow up), twin studies would falsely read this identical-fraternal difference as genetic. Luckily this is easy to study; not all identical twins share a placenta or sac, so you can cleanly separate the effect of uterine environment from genetics. If you measure enough traits, you can find small deviations in some, but it’s not clear whether this is just multiple testing, and in any case the deviations are small. The best studies suggest this chips off somewhere between 0 - 3% from heritability estimates9. 3: There is little assortative mating: We discussed this one above in the earlier section on GWAS - smart/pretty/kind/whatever people tend to marry other smart/pretty/kind/whatever people. Why would this bias twin study results? Identical twins share 100% of their genes. Fraternal twins ought to share 50% of their genes - but they get half their genes from their mother, and half from their father. In the degenerate case where the mother and father have exactly the same genes (“would you have sex with your clone?”) even fraternal twins will be extremely similar (although not quite identical, since they’ll get different alleles from each clone). In the more plausible case where mothers and fathers are just a little more alike than chance (eg because smart people tend to marry other smart people), fraternal twins will share a genetic tendency towards a trait somewhat more than their 50% shared genes suggest. Since this makes fraternal twin pairs more (genetically) like identical twin pairs, and twin studies assess heritability as the difference in fraternal-identical-twin-pair concordance, this bias would make twin studies underestimate heritability. But this is the opposite of what you would need to “discredit” twin studies - if this bias is true, then everything is more genetic than twin studies think. And unlike the previous two biases, this one seems real and important, so much so that when you adjust for it, the heritability of educational attainment rises from ~40% to ~50%. I’m only mentioning this one here because some anti-hereditarians argue that you can’t trust twin studies because of assortative mating, without mentioning that this can only bias them down. 4: Population stratification: This is often large and worth worrying about, but it applies to identical and fraternal twin pairs equally, and doesn’t bias twin study heritability estimates much (though it might shift the balance between shared and non-shared environment). See eg the sentence around footnote 30 here. 5: Non-additive / “interaction” effects: These are theoretically interesting, but all research thus far has found they are minimal (1, 2). Some experts think this may miss rarer or harder-to-find interactions; we’ll return to this later. 6: “Genetic nurture”, parent-to-child Mentioned above: if there is a gene for reading books to kids, and reading books raises IQ, it will look like a “gene for IQ”. This isn’t as relevant to twin study estimates of heritability, since both identical twins and fraternal twins are equally related to their parents, and any trait caused by genetic nurture wouldn’t differ between them (and therefore would not falsely appear heritable in this design). Rather, they would appear as shared environment. 7: “Genetic nurture”, sibling-to-sibling That is, suppose your sibling’s traits influence your own development. For example, suppose your sibling has a gene that makes them sabotage your schoolwork, causing you to fail and drop out of school early. An identical twin would share this gene with their sibling more often than a fraternal twin, making it look like a “gene for doing badly at school” (since the people who have it do worse at school than those who don’t). Why are we even talking about this? Do we really think it’s a big part of the variance in behavioral traits? Challenging twin study heritability estimates through this route requires inhabiting a weird no-man’s-land where otherwise-invisible genetic and environmental pathways suddenly flare up when you say the magic words “it was done by a sibling”. For example, this requires a strong effect of shared environment - that is, your educational attainment has to depend on whether you’re being sabotaged or not. But in general, shared environmental effects are weak. And it requires a strong effect of genes - that is, this mechanism only works if your sibling’s tendency to sabotage you is highly genetically determined. But we’re deploying this claim to deny that traits like IQ or educational attainment are highly genetically determined. So to get much out of this, the tendency to sabotage siblings would have to be more genetic than other behavioral traits! The reason this convoluted possibility gets brought up so often is that, unlike the more plausible parent-to-child genetic nurture, twin studies can’t rule it out. So if you really want to deny twin studies, this is one of your best bets. But when investigated, this has effects indistinguishable from zero. I’ve been a bit mean in this whole section, because people really like to dismiss twin studies as “Oh, don’t you know, those depend on assumptions, I bet you never considered that assumptions might be wrong”, and then Gish Gallop you with different assumptions until you give up. But scientists have actually done a lot of really good work checking the assumptions and they mostly hold. An alternative way of validating twin studies (brought up by Noah Carl in this article) is to check them against their close cousins, adoption studies and pedigree studies. Pedigree studies investigate large family trees, and check how trait similarity decreases with genetic distance. They avoid twin specific biases (like different treatment of fraternal vs. identical twin pairs, or different prenatal environments), while adding others like assortative mating. Here are the heritabilities of IQ and EA found in pedigree studies10 (see footnote for sources and caveats, and see also here and here for somewhat similar designs): Adoption studies investigate whether adoptees’ traits are more correlated with their adoptive or biological parents. They avoid a large swathe of biases, at the risk of introducing new adoption-related biases of their own (like the possibility that agencies deliberately place adoptive children with parents who are culturally or behaviorally similar, or the possibility that adoptees were adopted late enough to still get some shared environment from their biological parents). Here are the findings of some of the largest and best11: Both straightforwardly confirmed the larger heritability numbers found in twin studies. I would add the evidence from some less formal “adoption studies”12. During residency, I spent a few months working in a child psychiatric hospital for the worst of the worst - kids who committed murder or rape or something before age 18. Many of these children had similar stories: they were taken from their parents just after birth because the parents were criminals/drug addicts/in jail/abusing them. Then they were adopted out to some extremely nice Christian family whose church told them that God wanted them to help poor little children in need. Then they promptly proceeded to commit crime / get addicted to drugs / go to jail / abuse people, all while those families’ biological children were goody-goodies who never got so much as a school detention. When I met with the families, they would always be surprised that things had gone so badly, insisting that they’d raised them exactly like their own son/daughter and taught them good Christian morals. I had to resist the urge to shove a pile of twin studies in their face. This has left me convinced that behavioral traits are highly heritable to a level that it would be hard for any study to contradict. Ultimate source here. Although the study is confusing about this, I think it’s trying to say that almost 90% of subjects were adopted before age 2. But I don’t think studies do contradict this. Given the degree to which their assumptions have been validated, and the level of confirmation from pedigree and adoption studies, I think they have earned a presumption of accuracy. Doubting the twin studies doesn’t seem like a promising route to reconciling the twin-vs-Sib-Regression/RDR discrepancy. What’s Going On? (Part 2: Is Something Wrong With Sib-Regression And RDR?) Sib-Regression is a clever way of avoiding most biases. Its independent variable - the degree to which some sibling pairs end up with slightly more shared genes than others - is even more random and exogenous than the difference between fraternal and identical twins. It can sometimes have biases related to assortative mating (which would falsely push heritability down), but otherwise it’s pretty good. RDR has many of the same advantages, and allows more diverse relationships and so larger sample sizes. It’s hard to think of ways these methods could be wildly off. There is one caveat: although RDR includes most of the rare and structural variants missed by GWAS, in theory it can miss certain ultra-rare variants which are so uncommon that they aren’t shared between some of the relative pairs used in RDR. De novo variants that occurred during the subject’s own conception would be in this category, if the subject didn’t have children or didn’t pass on that gene13. This seems like a pretty small subcategory of genetic variation, and I wouldn’t normally expect that much of importance to be hiding here, but maybe it’s more important than it seems. RDR also doesn’t include much variance caused by statistical interactions between genes. Although we said above that these are usually found to be insignificant, they might be more important in a trait like intelligence that has been under recent evolutionary selection that lops off easily-detectable sources of variance and leaves only the weird obscure ones behind. There’s limited ability for classical Mendelian dominance to affect common variants, but more complicated genetic interactions might still prove important. Overall these are strong methods, and their failure to converge is troubling. If forced to explain them away, we might tell a story like: So far, there is only one RDR study and a few Sib-Regression studies, so we should wait for more data before updating too hard.
Inline links: 5, Polygenic embryo selection, need, Is Tan Et Al The End Of Social Science Genomics?, 6, like, And, 300K, 1M, 3M, A 2016 GREML paper, 7, evolves some ridiculous trick, a Darwinian argument, a 2017 paper, Wainschtein 2022, Sidorenko 2024, Zeng, 8, Weiner 2023, other experts say, meiosis, Young et al (2018), Kemper et al (2021), their trait similarity, matches their real status, 2, 3, 4, Felson 2013, https://substackcdn.com/image/fetch/$s_!r3kV!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd575f2d6-3619-40e6-9a5e-f9f1ec1399a5_650x422.png, answer, The best studies, 9, seems real and important, here, 1, 2, when investigated, in this article, 10, here, here, https://substackcdn.com/image/fetch/$s_!b3LF!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc094f9c0-4c71-48cf-89dc-615498d94812_483x51.png, 11, https://substackcdn.com/image/fetch/$s_!XFWU!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F85f433e5-d141-47b7-8dc5-2271925032e9_483x102.png, 12, https://x.com/cremieuxrecueil/status/1935731422205010135, here, 13
Many of the boring medical traits have as much “missing heritability” as educational attainment. For example, creatinine is a measure of kidney function; although twin studies find it’s about 55% heritable, Sib-Regression and RDR find less than half that. But here there are limited opportunities for confounders. Nobody assortative-mates on kidney function. It’s hard to see how family members could push other family members to have better or worse kidney function. Identical twins don’t have more similar kidney function environments than fraternal twins. Could there be some remaining possibility for confounding? Maybe there’s a gene for teaching your kid to have a good diet, and good diet causes better kidney function? Or maybe the measurements for creatinine were really bad during the Sib-Regression study (but apparently better during the twin studies?) I don’t really know what’s going on here. What’s Going On (Part 5: No, Seriously, What’s Going On?) So how heritable are complex traits, and why can’t different methods agree on this? I think the twin / pedigree / adoption estimates are mostly right. They are strong designs, their assumptions are well-validated, and they all converge on similar results. They also pass sanity checks and common sense observation. Although polygenic scores, GWAS, GREML, RDR, and Sib-Regression are also strong designs, they’re newer, have less agreement among themselves, and have more correlated error modes in their potential to miss rarer variants and interactions. Although it’s hard to figure out a story of exactly what’s going on with these rarer variants and interactions, there seems to be some evidence that they exist (again, see 1, 2, 3)15, and it seems easier to doubt this new and fuzzy area than the strong and simple conclusions from twin / pedigree / adoption work. In this model, polygenic scores, GWAS, and GREML could straightforwardly fail to pick up rare variants and interactions. The conclusions of RDR and Sib-Regression are harder to explain, but most of these anomalies are in educational attainment in particular - which is such a cursed construct, and so variable from sample to sample, that perhaps we can put it aside and focus on more stable traits. Otherwise, we can take solace in these methods’ failure to stay consistent even among themselves, which makes their inconsistency with twin studies somewhat less jarring. What mysteries remain? The parts that still bother me are: Why did the Iceland study find significantly lower numbers for Sib-Regression/RDR than twin studies for almost every trait? (hilariously, not for educational attainment with Sib-Regression this time, although I suspect this is just the big margin of error and the real number is commensurate with the other traits studied)
Inline links: 1, 2, 3, 15
Are we going to find and cash out “rare variants and interactions” soon? If we don’t, how long should we wait for genetic science to advance before changing our mind and deciding we must be missing something more fundamental? Alex Young thinks that once we get enough whole genomes sequenced (probably soon!) we might be able to use a technique called GREML-WGS to get more definitive answers about rare variants. But other experts I talked to said that if complex interactions were a big part of the picture, this might be “computationally intractable”. On the other hand, “computationally intractable” is a relative term: with enough data, genomic language models offer the potential for improved understanding of nonlinear effects. I’m encouraged to see increasingly good discussion of these topics on Substack, Twitter, and elsewhere. People like Sasha Gusev and Eric Turkheimer deserve credit for opening the discussion, but I would like to see a robust back-and-forth with the other side. Thanks to everyone who helped me review this post, including Ruben Arslan, Alex Young, Damien Morris, and some other people who didn’t respond to my email asking if I had their permission to list their names publicly (if this is you, let me know and I’ll edit you in). Most of what’s valuable is theirs, and all errors are mine alone the fault of o3, which provided invaluable research assistance but also hallucinated constantly. 1I’m abbreviating “two percentage points” as 2%pp. Nitpickers complain if I don’t use the “percentage points” framing, but it’s too long to spell out each time. 2Geneticists distinguish between three related concepts: Polygenic score r^2 is the degree to which our current best genetic models can predict traits. You might use this to discuss the accuracy of a genetic test or an embryo selection procedure.
Inline links: we might be able to use a technique called GREML-WGS, genomic language models, 1, 2
Highlights From The Comments On Missing Heritability
July 03, 2025 · Original source
Schizophrenia has about the same level of missing heritability as IQ, EA, or any other trait (80% heritable in twin studies, ~10% heritable in best polygenic predictors, ~25% heritable according to GREML). I don’t really understand on what grounds you can object to the twin heritability estimates of IQ/EA/etc, but believe the ones for schizophrenia.