1200 words
Why do some groups of people use chopsticks and others do not? Years back, created a thought experiment. So he found a few hundred students from a university and gathered DNA samples from their cheeks which were then mapped for candidate genes associated with chopstick use. Come to find out, one of the associated genetic markers was associated with chopstick use—accounting for 50 percent of the variation in the trait (Hamer and Sirota, 2000). The effect even replicated many times and was highly significant: but it was biologically meaningless.
One may look at East Asians and say “Why do they use chopsticks” or “Why are they so good at using them while Americans aren’t?” and come to such ridiculous studies such as the one described above. They may even find an association between the trait/behavior and a genetic marker. They may even find that it replicates and is a significant hit. But, it can all be for naught, since population stratification reared its head. Population stratification “refers to differences in allele frequencies between cases and controls due to systematic differences in ancestry rather than association of genes with disease” (Freedman et al, 2004). It “is a potential cause of false associations in genetic association studies” (Oetjens et al, 2016).
Such population stratification in the chopsticks gene study described above should have been anticipated since they studied two different populations. Kaplan (2000: 67-68) described this well:
A similar argument, bu the way, holds true for molecular studies. Basically, it is easy to mistake mere statistical associations for a causal connection if one is not careful to properly partition one’s samples. Hamer and Copeland develop and amusing example of some hypothetical, badly misguided researchers searching for the “successful use of selected hand instruments” (SUSHI) gene (hypothesized to be associated with chopstick usage) between residents in Tokyo and Indianapolis. Hamer and Copeland note that while you would be almost certain to find a gene “associated with chopstick usage” if you did this, the design of such a hypothetical study would be badly flawed. What would be likely to happen here is that a genetic marker associated with the heterogeneity of the group involved (Japanese versus Caucasian) would be found, and the heterogeneity of the group involved would independently account for the differences in the trait; in this case, there is a cultural tendency for more people who grow up in Japan than people who grow up in Indianapolis to learn how to use chopsticks. That is, growing up in Japan is the causally important factor in using chopsticks; having a certain genetic marker is only associated with chopstick use in a statistical way, and only because those people who grow up in Japan are also more likely to have the marker than those who grew up in Indianapolis. The genetic marker is in no way causally related to chopstick use! That the marker ends up associated with chopstick use is therefore just an accident of design (Hamer and Copeland, 1998, 43; Bailey 1997 develops a similar example).
In this way, most—if not all—of the results of genome-wide association studies (GWASs) can be accounted for by population stratification. Hamer and Sirota (2000) is a warning to psychiatric geneticists to not be quick to ascribe function and causation to hits on certain genes from association studies (of which GWASs are).
Many studies, for example, Sniekers et al (2017), Savage et al (2018) purport to “account for” less than 10 percent of the variance in a trait, like “intelligence” (derived from non-construct valid IQ tests). Other GWA studies purport to show genes that affect testosterone production and that those who have a certain variant are more likely to have low testosterone (Ohlsson et al, 2011). Population stratification can have an effect here in these studies, too. GWASs; they give rise to spurious correlations that arise due to population structure—which is what GWASs are actually measuring, they are measuring social class, and not a “trait” (Richardson, 2017b; Richardson and Jones, 2019). Note that correcting for socioeconomic status (SES) fails, as the two are distinct (Richardson, 2002). (Note that GWASs lead to PGSs, which are, of course, flawed too.)
Such papers presume that correlations are causes and that interactions between genes and environment either don’t exist or are irrelevant (see Gottfredson, 2009 and my reply). Both of these claims are false. Correlations can, of course, lead to figuring out causes, but, like with the chopstick example above, attributing causation to things that are even “replicable” and “strongly significant” will still lead to false positives due to that same population stratification. Of course, GWAS and similar studies are attempting to account for the heriatbility estimates gleaned from twin, family, and adoption studies. Though, the assumptions used in these kinds of studies are shown to be false and, therefore, heritability estimates are highly exaggerated (and flawed) which lead to “looking for genes” that aren’t there (Charney, 2012; Joseph et al, 2016; Richardson, 2017a).
Richardson’s (2017b) argument is simple: (1) there is genetic stratification in human populations which will correlate with social class; (2) since there is genetic stratification in human populations which will correlate with social class, the genetic stratification will be associated with the “cognitive” variation; (3) if (1) and (2) then what GWA studies are finding are not “genetic differences” between groups in terms of “intelligence” (as shown by “IQ tests”), but population stratification between social classes. Population stratification still persists even in “homogeneous” populations (see references in Richardson and Jones, 2019), and so, the “corrections for” population stratification are anything but.
So what accounts for the small pittance of “variance explained” in GWASs and other similar association studies (Sniekers et al, 2017 “explained” less than 5 percent of variance in IQ)? Population stratification—specifically it is capturing genetic differences that occurred through migration. GWA studies use huge samples in order to find the genetic signals of the genes of small effect that underline the complex trait that is being studied. Take what Noble (2018) says:
As with the results of GWAS (genome-wide association studies) generally, the associations at the genome sequence level are remarkably weak and, with the exception of certain rare genetic diseases, may even be meaningless (13, 21). The reason is that if you gather a sufficiently large data set, it is a mathematical necessity that you will find correlations, even if the data set was generated randomly so that the correlations must be spurious. The bigger the data set, the more spurious correlations will be found (3).
Calude and Longo (2016; emphasis theirs) “prove that very large databases have to contain arbitrary correlations. These correlations appear only due to the size, not the nature, of data. They can be found in “randomly” generated, large enough databases, which — as we will prove — implies that most correlations are spurious.”
So why should we take association studies seriously when they fall prey to the problem of population stratification (measuring differences between social classes and other populations) along with the fact that big datasets lead to spurious correlations? I fail to think of a good reason why we should take these studies seriously. The chopsticks gene example perfectly illustrates the current problems we have with GWASs for complex traits: we are just seeing what is due to social—and other—stratification between populations and not any “genetic” differences in the trait that is being looked at.
NotPoliticallyCorrect 
“then what GWA studies are finding are not “genetic differences” between groups in terms of “intelligence” (as shown by “IQ tests”), but population stratification between social classes”
This is what we call a distinction without a difference.
IQ does not directly measure social class. It measures visuospatial, processing speed, working memory, math, and vocabulary. Higher scores in the former are predicted by higher social class. Richardson’s critique boils down to “muh culture” as if cognitive abilities being coextensive with culture was ever a problem to begin with. This leads me to believe Richardson doesn’t understand the nature/nurture feedback loop because his criticism is only valid if you dichotomize the two. The only type of people these tests would be bias to are people who don’t live in civilization.
Subsequently, this also means population stratification poses no such problem for GWAS of IQ
LikeLike
Yes, if a society is remotely meritocratic, then the higher social classes really are more intelligent on average.
Also, Polygenic scores predicts outcomes even between siblings! Now tell me how that’s caused by social class?
LikeLike
Melo,
Knowing that Richardson is a developmental systems theorist, he does not “dichotomize the two.” His arguments on IQ and class are sound. Read his book and you’ll see that he dispenses with the dichotomy (like all DSTists).
It most definitely does, for genomic (and IQ score) reasons.
Some guy,
Source?
LikeLike
This one for example:
“In a second analysis based on all SNPs, we estimate that within-family effect sizes are roughly 40% smaller than GWAS effect sizes” – https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6393768/
So, 40% smaller, not 100% smaller as you would expect if it was only measuring population stratification.
LikeLike
Are you implying that the results still can’t be spurious in light of this?
What do PGS measure and how would we get to function? Just because SNPs are found near brain tissue and whatnot doesn’t mean that it has a causal effect on the trait, as the chopsticks example clearly shows.
LikeLike
What’s your explanation? How could it be spurious if it predicts outcomes for siblings? Coincidence strikes again, even in 22,000 sibling pairs?
Oh, yes, those SNPs that correlate with cognitive ability just happen to be particularly expressed in the brain. There’s a lot of coincidences in your worldview, aren’t there?
Since polygenic scores predict outcomes for different European groups and other races that were not used in construction of the polygenic scores, does population stratification just happen to center around the same SNPs in all populations? Quite a coincidence.
What do you actually believe, that there are no polygenic differences between people or that they can’t be measured with any accuracy at all because of reasons?
At some point theoretical arguments have to give way to empirical evidence.
LikeLike
“Knowing that Richardson is a developmental systems theorist, he does not “dichotomize the two.””
If one endorses DST they must agree with the above contention that I laid out.
P1: Cognition is experience dependent, this means cognition is a product of the interaction between environmental stimuli and the Nervous system.
P2: For individuals that live in civilization, social class is an accurate depiction of ones learning environment
C1: If an individual within the aforementioned population has high social class knowledge they will score high on measures of cognition such as IQ tests.
The above argument is sound, so if Richardson DS theorist then he either doesn’t understand the implications or he’s purposefully creating a dichotomy the opposite direction, namely cultural determination.
The reason his distinction is meaningless is because cognition is relative to the environment. These tests don’t work for Hunter gatherers.
“It most definitely does, for genomic (and IQ score) reasons.”
It most definitely does not. GWAS of IQ cannot suffer from population stratification from Social class if the distinction is not real. Social class cannot possibly confound the results. SO population stratification in the kind you have stated does not either. Unless you meant something else.
LikeLike
Melo,
On page 293, Richardson (2002) writes:
Since all human cognition takes place through the medium of cultural/psychological tools, the very idea of a culture-free test is, as Cole (1999) notes, ‘a contradiction in terms . . . by its very nature, IQ testing is culture bound’ (p. 646). Individuals are simply more or less prepared for dealing with the cognitive and linguistic structures built in to the particular items.
That’s where the conclusion of the argument derives.
If you’d have read his books, then you’d see that he has chapters on developmental/dynamic systems theory and how it ties into cognition.
What’s the error in his argument in Richardson (2017) and Richardson and Jones (2019)?
Some Guy,
So what? See the chopsticks example.
LikeLike
“Since all human cognition takes place through the medium of cultural/psychological tools, the very idea of a culture-free test is, as Cole (1999) notes, ‘a contradiction in terms . . . by its very nature, IQ testing is culture bound’ (p. 646). Individuals are simply more or less prepared for dealing with the cognitive and linguistic structures built in to the particular items.”
I’m aware of this. My point is that he doesn’t understand the implications of this fact. IQ is indistinguishable from Social class knowledge. This doesn’t contradict on how we conceptualize Intelligence as it is experience dependent along with all cognitive elements.
Subsequently this means GWAS on IQ cannot be confounded by stratification of social class. If I’ve misunderstood something please elucidate what.
LikeLike
phil jackson is called “the zen master”.
when he coached the lakers he asked his players, “what is the sound of half a butt farting?” they thought it about for a while, and shaq said, “melo.”
filipinos who wish they were black are unbelievably sad…and dumb.
LikeLike
The chopstick example SNPs would not over-expressed anywhere in particular and those SNPs would not predict chopstick use between siblings. It doesn’t apply.
LikeLike
What justifies the causal inference?
The chopsticks example most definitely applies to PGS. “Chopsticks genes” were found near a chromosome linked to motor behavior but it was just spurious. GWA studies find genes associated with brain tissue and whatever else with low “variance explained”, therefore the same may be happening here—and the evidence points to that being the case with population stratification being the likely cause.
It’s also worth noting that Richardson and Jones (2019) write:
These increasing doubts have not gone unnoticed. Indeed, following numerous enquiries, the Social Science Genetic Association Consortium (2018) (sponsors of the Lee et al., 2018, and other recent studies) published a set of answers to FAQs. It qualifies some of the more hasty interpretations, including problems over population structure, and a recognition that correlations can be spurious. Quite correctly, the authors also warn that, “characterizing these as ‘genes for educational attainment’ is still likely to mislead, for many reasons” (FAQ 3.2). Finally, to the question, “What policy lessons do you draw from this study?“, they answer: “None whatsoever” (FAQ 3.6).
LikeLike
From said FAQ:
“…we can test whether the GWAS results are entirely due to population stratification, because if they were, then the sibling estimates would not line up with the GWAS estimates. In fact, we find that the within-family estimates are more similar to the GWAS estimates in both sign and magnitude than would be expected by chance. These results imply that our GWAS results are not solely due to population stratification.
As another analysis to assess how much population stratification still remained in our data after our efforts to minimize it, we applied a state-of-the-method from statistical genetics called LD Score regression (Bulik-Sullivan et al. 2015). The results of this analysis indicated that the biases in our results due to population stratification are small.”
LikeLike
How does one get to function from this? How do you prove they are causal?
Nevermind the fact that “genes for” is misleading.
Do the results from Claude and Longo (2016) not have bearing in this discussion?
LikeLike
How could they not be causal if the correlation is not spurious? Genotype affects phenotype, the reverse is not possible. And how can it be spurious if they keep replicating Polygenic scores in independent samples like siblings?
LikeLike
How would it be the case that they’re causal if the correlation is not spurious? “Spurious” means “not what it seems like.” So what are the interpretations of the PGSs? How would we show which genes are “causing” the phenotype? Can you elaborate on this statement? “Genotype affects phenotype, the reverse is not possible” What do you mean that “the reverse is not possible”?
How does that refute Richardson’s (2017) and Richardson’s and Jones’ (2019) arguments on population stratification being the cause of the (small) GWAS hits “for intelligence”?
What about Claude and Longo’s (2016) paper showing that “very large databases have to contain arbitrary correlations. These correlations appear only due to the size, not the nature, of data. They can be found in “randomly” generated, large enough databases, which — as we will prove — implies that most correlations are spurious.”
LikeLike
“In statistics, a spurious relationship or spurious correlation[1][2] is a mathematical relationship in which two or more events or variables are associated but not causally related, due to either coincidence or the presence of a certain third, unseen factor”
If it’s not spurious then process of elimination means genotype>phenotype is the only possible explanation of the correlation.
Tell me, do these scientists of yours say that even studies that have been replicated between siblings are spurious? How would that be possible?
LikeLike
How does that follow? Can you elaborate on your previous claim that phenotype doesn’t affect genotype?
LikeLike
Phenotype: the set of observable characteristics of an individual resulting from the interaction of its genotype with the environment.
A phenotype by definition is the effect and not the cause of a genotype. For example: blue-eye SNPs cause blue eyes, blue eyes don’t cause blue-eye SNPs.
In any case, is the ultimate claim that genes are driving the relationship between genes and “IQ tests” due to the PGS? Again, how would that claim be established functionally?
LikeLike
You said the phenotype can’t affect the genotype. That’s false. Read Oyama, Baedke, Richardson, Miele, Jablonka and Lamb, Moore, Lickliter, Griffiths and Stotz.
LikeLike
Why did you edit my comment to say:
“In any case, is the ultimate claim that genes are driving the relationship between genes and “IQ tests” due to the PGS? Again, how would that claim be established functionally?”
By process of elimination. There’s no other explanation due to replications amongst siblings etc.
How about you who’ve read all that theory give an alternative explanation for why PGS replicate in sibling-studies? You can’t, because there isn’t one.
LikeLike
Damn, sorry I thought I edited my own.
That they “replicate” is evidence they’re causal?
LikeLike
Yes, because that’s the only possible explanation.
If you disagree, please give me an alternative explanation of why Polygenic scores predicts outcomes for sibling pairs.
LikeLike
Why are you assuming that it’s driving the outcome?
LikeLike
As I’ve said, process of elimination:
If A and B are correlated, either it’s
1. coincidence
2. A affects B
3. B affects A
4. another factor C affects them both
Replications in different samples proves it’s not coincidence.
If A is genotype and B is phenotype, B cannot cause A. Intelligence doesn’t change your genotype, just like blue eyes don’t cause blue eye SNPs to appear.
Replication in siblings proves it’s not a third factor C like population stratification. What other third factor can affect both genotype and intelligence in siblings?
So the only explanation left is 2. A affects B. Genotype affects Phenotype.
LikeLike
Such papers presume that correlations are causes… it is a mathematical necessity that you will find correlations…
Population stratification still persists even in “homogeneous” populations…
that’s bullshit. what is the ancient stratification of the population if iceland which would explain away any putative GWAS hits?
I fail to think of a good reason why we should take these studies seriously.
indeed. you shouldn’t. they disprove the twin studies and the effects they supposedly identify are meaningless unless one is a PGS outlier and maybe not even then.
to date not a single one of these studies has been replicated. not even for physical traits. because even if the researchers are sophisticated enough to understand what p-hacking and data snooping are they’re too retarded to understand that the findings must be replicated in disparate societies in order to establish genes per se as causal. professor shoe is a great example of what taleb terms an IYI (Intellectual Yet Idiot).
hereditists are not just mistaken. they’re DUMB.
melo is so utterly fucking retarded he’s an hereditist only because he’s half filipino because he thinks filipinos are ne asians.
it’s the stupidity stupid.
LikeLike