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.

Unpopular opinion (just look at the QT's) but nearly every "dogmatic, outdated, and misleading" claim about IQ listed here is either objectively accurate or heavily debated dispute within the field itself.

Let's take them one at a time:

https://twitter.com/DegenRolf/status/1773611740455968817

"IQ tests were necessarily biased"

One way test bias is evaluated within the field is by testing for strong measurement invariance (i.e. that subtest behavior is consistent across groups). This method is almost never applied in the classic literature or applied poorly (MCV).

Some thoughts on the ability to distinguish populations with genetic variation, why that means little for trait differences, and why there are other good reasons to collect diverse data. 🧵 I was pleasantly surprised to see no one mount a strong defense of "biological race" in this thread. Even the people throwing this term around seem to realize it's not supported by data. Instead the conversation shifts to population "distinguishability".

https://twitter.com/SashaGusevPosts/status/1760367948177252797

Something I don't want to get lost is that the field is much better now at studying, visualizing, and discussing complex populations than it has ever been, and there are many resources to help do this effectively. A few suggestions below: The NAES report and interactive on using population descriptors [] and Coop on genetic similarity [].

Carlson et al. [] and Lewis et al. [] on accurate presentation of ancestry.nap.nationalacademies.org/resource/26902…
arxiv.org/abs/2207.11595
nature.com/articles/d4158…
pubmed.ncbi.nlm.nih.gov/35420968/

I've written about race, genetic ancestry, analyses of large biobanks, and human history



I'll summarize the key points here 🧵: gusevlab.org/projects/hsq/#…
Let's define some terms. Race is a social categorization of people into groups, typically based on physical attributes. Genetic ancestry is a quantification of genetic similarity to a reference population. While correlated, they have fundamentally different causes & consequences.

I’ve seen quotes from David Reich’s “Who We Are and How We Got Here” passed around with the insinuation that it is secretly supportive of racist and hereditarian theories, even though it directly criticizes such views. It's worth looking at what Reich actually wrote: 🧵 Reich writes at length about Nick Wade's book 'A Troublesome Inheritance', a distillation of the hereditarian position. He makes clear that Wade misleads "naive readers" into a position that has "no merit": that genetic differences correspond to traditional racial stereotypes.

So this is pretty typical of the low-information content you get from the genetic racists. The majority of this post is just blather but there is one (1) specific claim about genetics: that the molecular genetic contribution to IQ keeps going up every year. This is false. A 🧵:

https://twitter.com/Steve_Sailer/status/1752075291881771354

The first study in 2011 into the heritability of IQ using molecular genetic methods found moderately high estimates 40-51%. But this approach was flawed technically (estimator bounds and population structure) and conceptually (environmental confounding).