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.

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".

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.

Hanania advocated passionately against "race mixing" for years, so he knows what he's talking about here. But it's worth adding that race-IQ obsessives also tend to make very poor predictions about the future. Let's review ...

https://twitter.com/RichardHanania/status/1920962584468951418

The Bell Curve, published at the peak of the 80-90's crime wave, predicted a coming dystopian urban hellscape with a "cognitive underclass" living in state-managed facilities. Not only did all this fail to materialize, but crime rates collapsed.

https://x.com/SashaGusevPosts/status/1781762773069811989

Nice! Here we have an interesting paper using genetic ancestry to classify race/ethnicity in modern data and algorithms. Let's take a look at what this paper found: 🧵

https://twitter.com/timothycbates/status/1916500806196404395

First, I don't want to get too hung up on language, but TCB's tweet starts talking about "ethnicity", then shifts to "continental ancestries", and then entirely omits the largest ethnic group in the US: Hispanics. These terms have distinct definitions (). nap.nationalacademies.org/catalog/26902/…

Always a red flag when people cite a 20yr paper for a question we have much better data on today. On the left is the race/ancestry clustering from Tang et al 2005, and on the right is the race/ancestry clustering for modern biobanks collected over the past few years.

https://twitter.com/SwipeWright/status/1915867631497707589

Of course you can also find similar looking patterns of structure within self-reported race/ethnic groups: (a) white Europeans, (b) white Brits, (2b,c) China, (2f) Canada, (2e) Japan.

I wrote about how population stratification in genetic analyses led to a decade of false findings and almost certainly continues to bias emerging results. But we are starting to have statistical tools to sniff it out. A 🧵: First, stratification = genetic structure + environmental structure. If two populations have some genetic variation (e.g. due to drift) and differing environmental influences on a trait, that will induce a false/non-causal correlation between genes and the trait.

This is a good example of how pointless a lot of the "data oriented" conversations on X are. DataRepublican, a DOGE analyst, makes a bold claim that 0/60,000 sampled government contracts had outlays < potential award ...

https://twitter.com/Noahpinion/status/1900561846307893621

Judd Legum, a journalist, points out that having outlays lower than the potential award amount happens frequently, explains why, and highlights a number of specific examples. Seems like a pretty basic error, should be easy to acknowledge right?

https://x.com/JuddLegum/status/1900228310753230988

So it turns out the person running this account and accusing mainstream behavioral geneticists of fraud was actually one of the authors of the discredited Pesta at al. paper that was being criticized. Pretending to be an objective third party so they could sling mud.

https://twitter.com/SashaGusevPosts/status/1806854731010027966

FWIW I don't have a problem with anon accounts and enjoy interacting with many on here. I understand that people may want to partition their on-line/IRL lives. But setting up a sock puppet persona so you can aggro out on colleagues that disagree with you is pathetic.

It's been interesting seeing Murray become an Ibram X. Kendi figure but for the right. Everyone knows his "analyses" in Human Diversity -- like comparing non-causal allele frequencies between populations -- are completely bogus. Razib knows this too.

https://twitter.com/razibkhan/status/1893177231150649495

But Murray says the things that are politically correct and pleasing to that audience's ego so he regularly gets trotted out for softball interviews and never needs to exhibit any rigor.

This thread and especially the underlying LessWrong post are a good demonstration of the IQ super-baby conspiracy theory that seems to be gripping Silicon Valley. Here's how it works ...

https://twitter.com/ESYudkowsky/status/1892374125089341444

First, claim that we already have the knowledge of how DNA affects college graduation rates but no one is interested in applying it. This is false, we almost never know *which* genetic variant is actually causal nor *how* it actually influences the associated trait.

How population stratification makes environments look like genes. A short 🧵: Start with two populations undergoing neutral drift but with no frequency differences on the alleles that influence the trait (i.e. no genetically causal population differences).

This account is a firehose of quantitative racism but it is worth re-iterating that: if a trait like skin color is caused by genes, and society determines outcomes based on skin color, then -- yes -- those outcomes will also be heritable. This is heritability 101! The fact that these guys were able to cook up a simulation that "disproved" a hypothesis that any intro genetics student knows is TRUE is a testament to how much nonsense people can get up to on here with a poor grasp of R and ggplot.

I wrote about the National Institutes of Health and the various serious and unserious proposals for NIH reform that have been floating around. It is important to understand how this agency actually functions and point criticism at the right problems. A short 🧵: The vast majority of the NIH budget goes towards funding research proposals in some form. I walk through the grant review process but the takeaway is that proposals are evaluated by groups of scientists on importance + rigor and most proposals *do not get funded*.

I wrote about the evidence for selective sweeps from genomic data over the past 50,000 years. A few highlights: Accurately detecting loci under selection is complicated by three main factors: neutral drift (which adds noise to allele frequencies), gene flow (which can hide or falsify frequency changes), and background selection (which induces more drift and temporal covariance).

One thing that has been difficult to understand from the election coverage is how Trump's policies are going to impact *me*, a rootless cosmopolitan and an Ivy League professor. Let's take a look: I'm on the right side of this chart, so Trump is going to blow up the deficit to give me a tax cut, while 40% of households get effective tariff hikes. Is this populism?

Really interesting new paper from Akbari et al. identifying a lot more selection in ancient DNA than previous approaches. I think it gets at three core challenges for this type of analysis where our understanding is still limited. 🧵

https://twitter.com/iosif_lazaridis/status/1835311235237290078

The core idea is to model allele frequency in ancient DNA as a function of time. If frequency has changed more than would be expected from drift and gene flow, that may be evidence of selection. Modeling drift/gene flow is hard, and the authors develop a new mixed model to do it.

Last week The Atlantic featured an article on the rising popularity of race/IQ science on the right (). The obvious point that "intelligence is not like height" sparked an unusual amount of whinging. I wrote about how this is now more true than ever. 🧵 theatlantic.com/technology/arc…
The specific claim in the article was that genetic variants for intelligence "predict only a small fraction of someone’s IQ score", unlike for height. This is true today and it will remain true, because the heritability of IQ is much lower than that of height.

I've written the first part of a chapter on the heritability of IQ scores. Focusing on what IQ is attempting to measure. I highlight multiple paradoxical findings demonstrating IQ is not just "one innate thing".



I'll summarize the key points here. 🧵 gusevlab.org/projects/hsq/#…
First, a few reasons to write this. 1) The online IQ discourse is completely deranged. 2) IQists regularly invoke molecular heritability as evidence for classic behavioral genetics findings while ignoring the glaring differences (ex: from books by Ritchie and Haier/Colom/Hunt).

It pains me to see facile critiques of GWAS on here from our clinical/biostats friends while the many actually good reasons to be critical of GWAS get little attention. So here's a thread on what GWAS does, what critics get wrong, and where GWAS is genuinely still lacking. 🧵: Here’s an example of what I’m talking about from Frank Harrell’s otherwise excellent critique of bad biomarker analysis []. This gets GWAS completely wrong. Genome-wide significance is not about "picking winners" or "ranking" the losers. fharrell.com/post/badb/

I’ve seen critiques of the poor methodology and cherry-picking in The Bell Curve but I haven’t seen much about the absolutely deranged fever dream of predictions about the coming decades in its closing chapters. It has been 30 years, so let's review. 🧵: Low skill labor will become worthless, attempts to increase the minimum wage will backfire. In the not-too-distant future, people with low IQ will be a ”net drag” on society.