Let's talk about the glass delusion, the Middle Ages' bout with a mass psychogenic illness marked by people believing they were made of glass.
Glass was a valuable commodity in Europe. It was primarily owned by the noble and well-to-do, and it had a notable purpose in alchemy.
Its perception as the technology of the time was as one that's both fragile and valuable, like the nobility.
Glass was the relatively novel technology people knew, and they knew things could be transmuted into glass. Delusional people also thought transmutation could affect them.
Take King Charles VI.
He truly believed his body was made of glass.
This delusion was such that Charles felt he had to build his life around it.
He had iron rods fastened into his clothing to hold him up, and he didn't allow his advisors to approach him, lest they accidentally shatter him.
This illness set in during his twenties.
The age when his psychotic bouts started is remarkably consistent with when the illness starts today.
Were he alive today, he probably would have been prescribed antipsychotics for his condition.
But Charles was alive long before his condition was understood.
He also wasn't its only sufferer. As alchemy's popularity grew, more people started to suffer the glass delusion.
They understood glass as fragile, and themselves as fragile, and they acted out that script.
For the well-off, the delusion was treated as legitimate. Many nobles came to wear padded buttocks.
But in one case, in Saint Germain, a doctor took a man who thought his butt was made of glass and beat him until he stopped believing in the delusion.
Apparently that worked.
Amusingly, in Rene Descartes' Meditations on First Philosophy, he remarked on the glass delusion and used it as an example of how people can see the world in totally different ways.
Of course, he did say that the perception was insane.
Fast forward to the 19th century and factories are beginning to dot the landscape of Europe.
With the change in popular technology, people's delusions followed suit.
With industry, the delusions became industrial. People imagined they were being influenced by vast machines.
Patients who presented with this belief in intricate, far off machines that controlled their actions and influenced their lives could never explain how they worked.
Just as people didn't know how they became glass, they didn't get how machines were affecting them.
Factories influenced the world, machines could obviously influence us, thus...
Schizophrenics' delusions are tailored to what they vaguely know, and they learn about those half-baked delusions from others, the times, etc.
Consider exorcism. Its modern script came from a movie!
People today know about parasites, chronic pain, post-viral conditions, toxic poisoning, and more, and they have an amazing tool for finding and promoting related scripts:
The internet!
Thousands of people today believe they have a skin condition called Morgellons.
It's not real.
They just believe they're developing sores and lesions, and hairs are sprouting from them, but they're really picking themselves raw and getting freaked out by cotton fibers.
The sufferers from this condition are deeply unwell, and they spread their unwellness to other people through posting about their condition online.
People know about all the requisite concepts, and they see something on themselves and imagine it's a real symptom.
But it's not.
There are no demons, there's no Morgellons, people cannot be made of glass, and there's no big machine out there influencing people and miraculously disappearing the moment those concepts go out of fashion and get replaced by other ones with nary a cure invented.
And this keeps happening!
People are always inventing new conditions or imagining they're afflicted by something real when they're not, by reading into symptoms and gaslighting themselves.
And they sometimes act incredibly mad in these bouts of belief.
A subset of people today seem vulnerable to scripts, and some seem to have always been vulnerable to it.
It makes you wonder: must psychotic people always be like this? And what can we do to cure them of their delusions?
I suggest we don't give in with padded buttocks.
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0. The sample for the population result is *everyone* with ≥1/16 and ≤15/16 African admixture, the within-family result is for the siblings among them. I didn't plot between-families results, but they're pulled from the same distribution as the population result, so when I have those computed I'll replot, but they shouldn't be any different (will take a few hours, maybe be tomorrow, w/e).
1. Yes, datasets will be merged and sharper results will be obtained for the article on this that's coming out ~shortly. Can't be done for all phenotypes in the UKBB, but can be done for IQ at least. For example, we don't have lipoprotein(a) (lp(a)) measurements in some of the samples of young American kids, some of them don't have objective skin color measurements, etc. As a side note, this result holds up with brain size (not shown) in the UKBB, but I'm unsure if it holds up in the young samples that'll be merged with it, as they're still developing in most cases.
Anyway, the IQ p-value is p = 0.008 (two-tailed) within families and extremely low between them. The within-family result is robust, will get sharper with more data, and is also sharpened by using a latent variable instead of a score, and by correcting for measurement error (not needed with the LVM). Score shown for simplicity and ease of others with UKBB access replicating this. Also, accounting for error in the admixture computation, the p-value would drop a bit further, but not by very much since error is very small.
2. The "IQ per unit of admixture" is statistically indistinguishable between the population and within-family results, and yes, it explains most of the Black-White difference in IQ. I just wanted comparably-scaled results for all the traits here, so you're seeing r's. It's pleasant that the within-family variance reductions aren't enormous for siblings, which is what we expect even with quite high heritabilities given their genetic relatedness. It's the same result we've seen with American data, and it's also nice to see that in the case of this trait, the global admixture result *can* be interpreted like the within-family one. Presumably this only holds with measurement invariance, as we see in the U.K. when comparing Whites and Blacks there. Since we see this in the U.S. too, it's likely that the previous, already-published within-family null—which had a sizable effect in the correct direction which also could not be distinguished from the global r—was just a false-negative.
3. This result replicates with other degrees of relatedness, but we might lose the causal interpretation with those ones because the estimand for, say, a cousin test is different given the identity of the "C" variance component shifts for that comparison.
4. What is architectural sparsity and why is it relevant? Consider this table from nature.com/articles/s4159… (cc: @hsu_steve):
Basically, sparsity refers to the number of variants involved in a trait. It also refers to their effect size distribution. So, for Alzheimer's, for example, the trait is highly polygenic, but APOE explains more variance than the entire rest of the PGS, so while being under highly polygenic control, it remains moderately sparse.
If you're still not grokking what I mean here, consider some distributions of cumulative effects across the chromosomes. Here's the result for lp(a), which is already known to be influenced by essentially one gene. Guess which chromosome it's located on:
Consider, as a comparison, creatinine, which is considerably less sparse, and thus has effects distributed across all chromosomes:
Now, consider what this does to within-family admixture assessments. Skin color, for example, is controlled by only a handful of genes. This means that global admixture shouldn't tag it very much between siblings. The same is even more true for lp(a). And in fact, now we have confirmation of this!
But, we know from other methods that lp(a) is effectively entirely genetically-caused between populations. This is just accepted within the medical community because it is an obvious fact that follows from its strong control by a single gene. You can also figure this out using local ancestry estimates. Basically, the correlation between genome-wide ancestry and ancestry at a causal loci is what we want to get at, and if you know the causal loci, you gain power by restricting your analysis to that area. This is what we find with lp(a) (not shown, but use your brain. The obviousness of this fact is why the author used lp(a) as an example).
Also, in some sense, the frequency of that locus between ancestries gives you what you need without doing all this within-family stuff, if you're confident it's causal. The effect estimate might still be biased by population structure though, so that's worth keeping in mind.
There will be a post soon with more details and the expanded set of results with the additional datasets, robustness tests, and plenty of other fun things to look at.
TL;DR: this is a spoiler, and it shows that, yes, you can explain the Black-White IQ difference in Britain mostly genetically, and the global admixture result that I've posted here before is equivalent to the within-family one. Woohoo for things that should hold up, in fact, holding up!
As a sort of replication of the Young paper, you cannot explain the difference in educational attainment (as years of education) in this way. Why? Well, hard to say. Compensatory factors like I found with the GCSEs? (Haven't read that post yet? Go check it out here: cremieux.xyz/p/explaining-a…). Poor phenotype quality? Very plausible, because education really is a huge garbage heap, but why would that be in the general population and not within families? Maybe it has to do with what other traits admixture tags? Maybe it does replicate, but we just can't see it, because the precision is too poor (possibly the lp(a) story too). Who knows!
Q: Will the combination with more datasets allow us to fix this educational attainment result?
A: No, because most of the other datasets involve young people, not people who have almost all completed their educations, as in the UKBB. That plus the generational change and international incomparability in the definition of educational attainment makes it too poor as a phenotype. Sorry!
Any questions?
Link to a fun previous post from the same dataset, showing a result that *does* hold up within families: x.com/cremieuxrecuei…
Link to another post mentioning the forthcoming article earlier: x.com/cremieuxrecuei…
I want to ping an old post that I've also posted some replications for.
Basically, parents are inequality averse, and they try to compensate for when one sibling is less gifted than another, reducing the ancestry/PGS effect on education within families.
Ever wondered why advertisements heavily feature Black actors when they're just 12-14% of the population?
I might have an explanation:
Black viewers have a strong preference for seeing other Blacks in media, whereas Whites have no racial preferences.
These results are derived from a meta-analysis of 57 pre-2000 and 112 post-2000 effect sizes for Blacks alongside 76 and 87 such effect sizes for Whites.
If you look at them, you'll notice that Whites' initial, slight preference declined and maybe reversed.
It's worth asking if this is explained by publication bias.
It's not!
Neither aggregately (as pictured), nor with results separated by race.
You're on trial, and the jury can't make up their minds. The decision is a coin flip: 50/50, you either get it or you don't.
Your odds of a given verdict depend on the "peers" making up your jury.
If you're Black and they're Black, your odds are good; if you're White, pray.
Though White jurors have, on average, no racial bias, the same can't be said for Black jurors.
Where the White jury gives you approximately the coin flip you deserve, the Black jury's odds for a verdict are like a coin rigged to come up heads 62% of the time.
Once you get to sentencing, things get even worse.
The White jury is still giving you a coin flip on a lighter or a harsher sentence, but the Black jury is giving lenient sentences to Blacks about 70% of the time.
Posts saying Charlie said things he didn't, oftentimes even including videos where he doesn't say what the post says he does, have convinced me.
There will be no organic temperature-lowering coming from the left, because they don't want it.
They believe the things they claim.
They actually believe the people they dislike *are* racists, fascists, homophobes, transphobes, all of it.
They are not reasonable, and they cannot form anything like a reasoned argument for these perceptions.
Even still, it's really what they think.
Accordingly, they will not lower the temperature.
You can't expect people to stop using slurs like "fascist" or "racist" or "sexist"—even if they are completely untrue and it's impossible to provide valid evidence for them!—if they believe they're true.
It shows that the gender wage gap is mostly about married men and their exceptional earnings.
In this thread, I'm going to explain why married men earn so much more than everyone else🧵
The question is:
Does marriage maketh man?
Or
Are all the good men married?
That is, does marriage lead men to earn more, or do men who earn more get married more often?
To answer this question, we have to work through the predictions of different theories.
For example, one of my favorite papers on the subject looked into three different hypotheses to explain the "marriage premium" to wages, and they laid out a few testable predictions: