The mismeasure of population differences: A critique of Kevin Bird's critique

PETER FROST puts up some precise arguments below that may not be easily followed by all readers.  So I think I should attempt a summary:  

He notes that genetic studies now strongly support what IQ tests have been telling us all along:  There is such a thing as educational attainment and it can just as strongly be predicted by a person's genetic makeup as by his/her IQ tests results.  The genes behind IQ have in other words been substantially identified.  IQ is real and it makes a difference.

But the old, old issue of racial differences arises.  Do the genes that predict educational attainment among whites also predict attainment among Africans?  One answer is that by using white gene counts Africans score poorly. Both according to IQ tests and genetic scores they do badly

That of course upsets some people and arguments have been put up to say that the genes for educational success are not the same among blacks.  Black genes are different, to put it crudely.  So we really have to redo all our genetic studies if we want to use genes to predict black educational success.

Not much of that has been done but what is available suggests that black genetics may indeed give a  poor prediction of educational outcomes.  Blacks and whites may not be "equal" genetically.  That of course treats blacks as being almost a separate species, which is unlikely to please everyone but that is a price you may have to pay if you want to use genetics to predict black educational attainment.

Meanwhile the "gap" in educational attainment between blacks and whites  is as large as ever, no matter  how  you explain it.  Great efforts have been expended to close that gap but nothing so far has worked.  There does seem to be something in blacks that militates against high average levels of educational attainment.  Genetics may one day explain it but that is out of reach at the moment.  We can predict it (via IQ tests) but not explain it with any certainty

We have identified thousands of genes (SNPs) whose alleles are associated with variation in educational attainment (Lee et al., 2018). By finding out which alleles are present on the genome, we can create an estimate of cognitive ability that strongly correlates with performance on standardized mathematics, reading, and science tests (r = 0.8) and, on a group level, with mean population IQ (r = 0.9) (Piffer, 2019).

Those high correlations are made possible by the logic of sampling. To estimate the mean cognitive ability of a population, it is unnecessary to identify all of the relevant SNPs, just a large enough sample. The SNPs are "witnesses" to natural selection. We need only question a sufficient number of them to understand the strength and direction of selection, and its consequences.

Like IQ, the polygenic score differs on average among human populations. It seems to have increased during the northward spread of modern humans out of Africa and into Europe and Asia, with East Asians scoring the highest. This pattern is in line with IQ data. The mean polygenic score is also high among Ashkenazi Jews and Finns, again in line with IQ data (Piffer, 2019).

Can a mean polygenic score be used as a proxy for mean IQ? No, says biologist Kevin Bird (2021) in his paper “No support for the hereditarian hypothesis of the Black-White achievement gap using polygenic scores and tests for divergent selection.” Although Europeans and sub-Saharan Africans have different alleles associated with educational attainment, these differences, he argues, correspond to trivial differences in cognitive ability. In fact, they are more consistent with genetic drift than with natural selection.

To prove his argument, he performed two analyses on the genetic data: an Fst and a test for polygenic selection. In my opinion, both analyses are dubious.

This is the most common measure of genetic differentiation between populations. If the Fst is low, differentiation is trivial and consistent with genetic drift. If it is high, differentiation is substantial and consistent with natural selection. For SNPs associated with EA, Kevin Bird reports an Fst of 0.111. Is that low or high?

When the American geneticist Sewall Wright created Fst, he defined four categories of differentiation:

0 to 0.05 - little genetic differentiation

0.05 to 0.15 - moderate genetic differentiation

0.15 to 0.25 - great genetic differentiation

0.25 to 1 - very great genetic differentiation (Wright, 1978, pp. 82-85)

Wright's categories are widely cited. A search in Google Scholar for "moderate genetic differentiation" and "0.05 - 0.15" shows over two hundred papers.

So does an Fst of 0.111 mean moderate genetic differentiation? Not according to Kevin Bird, who sees little to none below a benchmark of 0.118. That benchmark may be valid, but it cannot be easily verified and appears nowhere else in the literature. Nor does he explain why it is better than the ones put forward by Sewall Wright. In fact, he makes no reference to the latter's benchmarks.

One may also question the Fst of 0.111. For the data source, the reader is referred to Lee et al. (2018), a study done only with European participants. Moreover, Kevin Bird used 1,259 SNPs to calculate that Fst, even though he found only 685 SNPs that had data on both Africans and Europeans. The Fst of 0.111 seems to refer only to Europeans. That value is what would be expected, but it says nothing about diversification between Europeans and sub-Saharan Africans.

Polygenic selection analysis
The second analysis is more relevant but poses another problem. There are two possible ways to calculate the effect size of each allele. One way is to use between-family data, and the other is to use within-family data. When Kevin Bird used the first dataset, he found a clear difference in genetic capacity for educational attainment between Europeans and Africans. When he used the second dataset, he found a much smaller one that could easily be explained by genetic drift.

Kevin Bird prefers the second dataset. All things being equal, it would indeed be preferable. There would be less statistical noise because siblings have similar upbringings. With less noise, population differences could more easily be identified. Yet, here, we see the opposite: Europeans and Africans are significantly different in the between-family data but not in the within-family data. The reason is that the between-family data came from over a million participants whereas the within-family data came from 20,000 sibling pairs. Being smaller, the second dataset had a lot more noise. All things being equal, it should have had less. But some things were not.

If we repeat the analysis with a much larger sample of sibling pairs, there would be less noise and Europeans and Africans would clearly differ in alleles associated with educational attainment. Kevin Bird anticipates this eventuality. Even with a much larger within-family dataset, "there is still likely to be some level of confounding from population structure" (Bird, 2021, p. 7). He elaborates on this point:

[...] the [polygenic] scores might be biased by a variety of factors, including the nonrandom ways that society is geographically structured [...]. For instance, Black people in the US, for reasons unrelated to genetics, live in areas with poorer air quality and more exposure to environmental toxins (Bird, 2021, p. 8)

Yet, as he notes further on, the SNP alleles were identified only in European participants, and the effects on educational attainment were estimated only from European data. How, then, could different alleles among Europeans be spuriously associated with differences in educational attainment among Europeans because of socioeconomic deprivation among Black Americans? How do the latter enter the picture?

Kevin Bird is right on one point: cognition in other human populations may not be accurately predicted by alleles identified from European participants or by allele effects calculated from European data. This is especially so for sub-Saharan Africans, who seem to have a different architecture of cognition (Fuerst et al., 2021; Guo et al., 2019; Rabinowitz et al., 2019). That factor, however, would introduce even more noise into the data and decrease, rather than increase, any measurable differences between Africans and Europeans.

It does look like cognitive evolution has followed a different trajectory among sub-Saharan Africans. Rabinowitz et al. (2019) found that the polygenic score of Black Americans predicts some abilities better than others, notably general academic success (pursuit of postsecondary education) and compliance with rules (absence of a criminal record). For school tests, it has some power to predict ability in mathematics but none in reading. Processing of language may be the mental domain where people of sub-Saharan African descent have undergone the most cognitive evolution since their separation from other ancestral humans.


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