The mismeasure of population differences

A critique of Kevin Bird's critique

The American geneticist Sewall Wright (1889-1988) created Fst as a measure of population differentiation (Wikicommons)

Europeans and sub-Saharan Africans differ at genes (SNPs) that predict educational attainment and, hence, cognitive ability. Biologist Kevin Bird argues that these differences, though real, are trivial. They are also more consistent with genetic drift than with natural selection. Is he right?

A new instrument for measuring cognitive ability

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 of individuals on standardized mathematics, reading, and science tests (r = 0.8);

• performance of populations on IQ tests, i.e., 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.

What do we call this new yardstick of cognitive ability? We call it the “educational polygenic score” or Edu PGS for short.

Like IQ, the Edu PGS 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 Edu PGS 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 don’t share the same alleles for cognitive ability, these differences, he argues, are trivial. 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.

Fst

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. Kevin Bird reports an Fst of 0.111 for SNPs associated with educational attainment. Is that low or high?

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

0.00 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 1.00 - 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 genetic differentiation 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 be easily explained by genetic drift.

Kevin Bird prefers the second dataset. All things being equal, it would indeed be preferable. There should be less statistical noise because siblings have similar upbringings. With less noise, population differences could more easily be identified. Yet we see the opposite here: 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 equal.

If we repeat the analysis with a much larger sample of sibling pairs, we should see less noise and should find that Europeans and Africans clearly differ at SNPs 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 be inaccurately predicted by alleles identified in 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 that has seen the most cognitive evolution among people of sub-Saharan African descent since their separation from other ancestral humans.

References

Bird, K.A. (2021). No support for the hereditarian hypothesis of the Black-White achievement gap using polygenic scores and tests for divergent selection. American Journal of Physical Anthropology. 175(2): 465-476. https://doi.org/10.1002/ajpa.24216

Fuerst, J.G., Hu, M., & Connor, G. (2021). Genetic Ancestry and General Cognitive Ability in a Sample of American Youths. Mankind Quarterly 62(1): 186-216. http://doi.org/10.46469/mq.2021.62.1.11

Guo, G., Lin, M.J., & Harris, K.M. (2019). Socioeconomic and Genomic Roots of Verbal Ability. bioRxiv, 544411. https://doi.org/10.1101/544411

Lee, J.J., Wedow, R., Okbay, A., Kong, E., Maghzian, O., Zacher, M. et al. (2018). Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nature Genetics 50(8): 1112-1121. https://doi.org/10.1038/s41588-018-0147-3

Piffer, D. (2019). Evidence for Recent Polygenic Selection on Educational Attainment and Intelligence Inferred from Gwas Hits: A Replication of Previous Findings Using Recent Data. Psych 1: 55-75. https://doi.org/10.3390/psych1010005

Rabinowitz, J. A., Kuo, S. I. C., Felder, W., Musci, R. J., Bettencourt, A., Benke, K., Sisto, D. Y., Smail, E., Uhl, G., Maher, B. S., Kouzis, A., & Ialongo, N. S. (2019). Associations between an educational attainment polygenic score with educational attainment in an African American sample. Genes, Brain and Behavior 18(5), e12558. https://doi.org/10.1111/gbb.12558

Wright, S. (1978). Evolution and Genetics of Populations, Volume 4. University of Chicago, Chicago, IL.