Eric Turkheimer has a good piece about a bet he made with Charles Murray regarding the genetic understanding of IQ (or, really, the lack of it). Murray being so wrong in his prediction should make us question his world model, but it's also worth commenting on his response.
Murray has, for some time now, been workshopping the excuse that progress on IQ genetics was blocked by researchers being denied the access to the relevant databases. This is patently untrue!
First, one of the largest genetic analyses to date of *any* trait is of educational attainment, a phenotype Murray himself has used as a proxy for intelligence. Surely a study of 3 million should have been enough to satisfy Murray's prediction.
Second, a massive (n=~500k) rare variant study of cognitive function (including a short IQ test) was published in 2023 in one of the most prestigious journals in the field. That study identified a mere eight genes, and the overall variance explained was just 0.15% (that's right).
Finally, a recent pre-print imputed higher quality IQ into the full >450k UK Biobank population. Putting *that* data together with the largest available studies of IQ produced a common variant genetic score that explained ... 2.6% of the trait.
In short, the genetic mechanisms of IQ have been extraordinarily well studied both from the rare and common variant perspective and now explain ... essentially none of it. Turkheimer clearly wins the bet, and makes a compelling case that the whole premise was flawed:
So what's @charlesmurray up to now? He's been laundering fake analyses that he himself doesn't understand from anonymous race bloggers who can't be bothered to write them up (FWIW "substantive reply" did not come). Not with a bang but with a whimper.
Murray and most of race twitter has apparently been fooled by this completely fabricated analysis purporting to show African ancestry is associated with IQ. People lie on twitter all the time, but this is both more revealing and more disturbing than usual. A 🧵
Revealing in that it shows how quantitative racism is a just an exercise in manipulating data to fit the preconceived conclusion. Disturbing because this time private data is being used and the results, which cannot be easily verified, are just flatly invented.
What's actually going on? Some guy claims to have an analysis showing that African ancestry differences between siblings are associated with IQ differences in the UK Biobank. Implying an ancestry difference in the within-family influences.
A few thoughts on Herasight, the new embryo selection company. First, the post below and the white paper imply that competitors like Nucleus have been marketing and selling grossly erroneous risk estimates. This is shocking if true! 🧵
I wrote last year about the un-seriousness with which Nucleus approached their IQ product and the damage it could do to genetic prediction and research more broadly (). This appears to have been a broader pattern beyond IQ, extending even to rare disease.theinfinitesimal.substack.com/p/genomic-pred…
People who care about this technology should be furious at Nucleus and their collaborators (as well as Orchid and Genomic Prediction for their own errors). Finding such flaws should not require reverse-engineering by a competitor. These products clearly need independent audits.
Oof. Polygenic scores for IQ lose 75% of their explained variance when adding family controls, even worse than the attenuation for Educational Attainment. These are the scores Silicon Valley is using to select embryos 😬.
The TEDS cohort used here is a very large study with high-quality cognitive assessments collected over multiple time points. It is probably the most impressive twin study of IQ to date. That means very little room for data quality / measurement error issues.
It is important to highlight surprising null results. Just last week we were hypothesizing that large IQ score attenuation could be a study bias or an artifact of the Wilson Effect. Now we see it replicate in an independent study with adults.
Racism twitter has taken to arguing that observed racial differences must be "in part" explained by genetic differences, though they demure on how much. Not only is this claim aggressively misleading, it is completely unsupported by data. A 🧵:
Genetic differences between any two populations can go in *either* direction, matching the phenotypic differences we observe or going against them. Genes also interact with the environment, which makes the whole notion of "explaining" differences intractable.
The mere fact that a trait is heritable within populations tells us nothing about the explanatory factors between populations. See: Lewontin's thought experiment; Freddie de Boer's analogy to a "jumping contest"; or actual derivations (). pubmed.ncbi.nlm.nih.gov/38470926/
James Lee and @DamienMorris have an interesting perspective paper out describing "some far-reaching conclusions" about the genetics of intelligence. This type of "where are we now" paper is very fun and more people should write them! So, where are we now? 🧵
It's a short paper and it surveys three core findings from the past decade of intelligence genetics. These sections follow a structure that I would cheekily call ... "make a bold claim in the title, then walk it back in the text".
First up, they address the concern that associations with intelligence may actually be mediated by functionally irrelevant traits like physical appearance or pigment. The argument is that IQ GWAS has demonstrated enrichments for CNS/brain structure gene sets. This is true!
The SAT/meritocracy debate has always been a bit odd to me when the test makers themselves have studies showing self-reported high-school GPA is a consistently better predictor of college GPA and always adds on top of SATs.
Clearly SATs are neither the only nor even the best measure we have of college success and "holistic" admissions can be "meritocratic". It's up for debate whether the additional <10% predictive variance SATs give you are worth the high-school testing industrial complex.
A challenge with all of these analyses is they are measured after selection on the predictor variables themselves, which can induce biased estimates through range restriction. The raw correlations are even lower, and it is hard to know whether correcting is appropriate.