Derived alleles,corrected polygenic scores for IQ and height


I have recently updated the new version of my paper about polygenic selection pressures on human stature published in f1000research. I chose stature not because it’s a particularly interesting trait but for the simple reason that it’s very straightforward to measure and has the largest sample size available for genome-wide association studies. Its genetic architecture is also very similar to IQ because it’s highly polygenic and normally distributed.

As far as I know, f1000research is the only other journal in the world to be “twice open” : open access and open peer review. The journal I founded ( is twice open but also free and is more interactive, besides being based on a bottom up process in the sense that reviewers choose the paper instead of the editor choosing reviewers. Apart from this, let’s come to my study.

The biggest novelty is a correction I have introduced to deal with different population frequencies of derived alleles. Derived alleles are basically human-specific mutations that are assumed to have arisen after the chimp/homo lineages split. Of course these are not the only mutations that arose during human evolution. Remember that we are talking about polymorphisms, hence this automatically excludes all mutations that are fixed  in the human population (no polymorphism, no SNP). The latter are substitutions ascertained via comparison with the chimp genome. Fixed mutations were once polymorphisms (a jargon term for SNP, which is even more alien for some people), but not all SNPs became fixed as some were lost due to random drift or purifying selection (the process that eliminates deleterious alleles).

There is a big controversy going on as to the causes of these: are they the result of relaxed puryfing selection due to population bottlenecks and decreased effective population size? (Henn et al, 2015) Or are they a result of increased mutation rate after a bottleneck? (Do et al., 2015) Were all (or almost all) mutations deleterious or were many of them adaptive? (Harris, 2010).

Besides demographic histories, there is also the problem that GWAS are usually carried out on Europeans, hence they tend to pick up derived alleles at higher frequency among European populations.

Be it as it may, I had to find ways to correct for this bias. In the case of the height GWAS (Wood, 2014), this was rather straightforward. There were 697 SNPs reaching genome-wide significance so this is a pretty big sample but 691 could be aligned for ancestral/derived status using 1000 Genomes. Among the positive effect alleles, there were slight more of the derived kind (370:321). Hence I computed two polygenic scores (mean population frequencies): ancestral and derived. Then I created a composite score by averaging them. This gives equal weight to ancestral and derived alleles (Piffer, 2015b).The end result is that populations with higher baseline frequencies of ancestral alleles (such as Africans) obtain a higher score after this correction, because more weight is given to ancestral alleles.

A corrected score of IQ increasing derived alleles was also computed and averaged across the four polygenic scores (two from Rietveld et al., 2013; one from Rietveld et al., 2014 and one from Davies et al., 2015), affecting educational attainment or fluid intelligence.

Table 1. Polygenic scores.

Corrected Height Uncorrected Height Corrected IQ Uncorrected IQ
Afr.Car.Barbados 0.487 0.473 -0.009 0.374
US Blacks 0.490 0.476 0.018 0.400
Bengali Bangladesh 0.485 0.476 0.002 0.406
Chinese Dai 0.479 0.469 0.078 0.484
Utah Whites 0.511 0.503 0.102 0.511
Chinese, Bejing 0.479 0.470 0.087 0.501
Chinese, South 0.482 0.472 0.075 0.483
Colombian 0.493 0.484 0.062 0.478
Esan, Nigeria 0.485 0.470 0.011 0.386
Finland 0.505 0.497 0.122 0.531
British, GB 0.508 0.499 0.114 0.524
Gujarati Indian, Tx 0.486 0.476 0.031 0.434
Gambian 0.486 0.471 -0.001 0.375
Iberian, Spain 0.500 0.491 0.121 0.534
Indian Telegu, UK 0.488 0.478 -0.032 0.370
Japan 0.477 0.468 0.057 0.463
Vietnam 0.480 0.470 0.105 0.507
Luhya, Kenya 0.483 0.468 -0.014 0.358
Mende, Sierra Leone 0.487 0.472 0.026 0.396
Mexican in L.A. 0.488 0.479 0.004 0.418
Peruvian, Lima 0.484 0.475 -0.043 0.378
Punjabi, Pakistan 0.491 0.482 -0.004 0.406
Puerto Rican 0.493 0.484 0.066 0.482
Sri Lankan, UK 0.487 0.478 -0.024 0.384
Toscani, Italy 0.501 0.492 0.128 0.537
Yoruba, Nigeria 0.484 0.469 0.012 0.384

The correlation between the uncorrected scores (0.602) is slightly higher than between the corrected scores (0.487).

The scores were ranked in descending order and reported in table 2.

Table 2.  Corrected polygenic scores reported in descending order.

Corrected Height Corrected IQ
Utah Whites 0.511 Toscani, Italy 0.128
British, GB 0.508 Finland 0.122
Finland 0.505 Iberian, Spain 0.121
Toscani, Italy 0.501 British, GB 0.114
Iberian, Spain 0.500 Vietnam 0.105
Puerto Rican 0.493 Utah Whites 0.102
Colombian 0.493 Chinese, Bejing 0.087
Punjabi, Pakistan 0.491 Chinese Dai 0.078
US Blacks 0.490 Chinese, South 0.075
Mexican in L.A. 0.488 Puerto Rican 0.066
Indian Telegu, UK 0.488 Colombian 0.062
Sri Lankan, UK 0.487 Japan 0.057
Afr.Car.Barbados 0.487 Gujarati Indian, Tx 0.031
Mende, Sierra Leone 0.487 Mende, Sierra Leone 0.026
Gujarati Indian, Tx 0.486 US Blacks 0.018
Gambian 0.486 Yoruba, Nigeria 0.012
Bengali Bangladesh 0.485 Esan, Nigeria 0.011
Esan, Nigeria 0.485 Mexican in L.A. 0.004
Yoruba, Nigeria 0.484 Bengali Bangladesh 0.002
Peruvian, Lima 0.484 Gambian -0.001
Luhya, Kenya 0.483 Punjabi, Pakistan -0.004
Chinese, South 0.482 Afr.Car.Barbados -0.009
Vietnam 0.480 Luhya, Kenya -0.014
Chinese, Bejing 0.479 Sri Lankan, UK -0.024
Chinese Dai 0.479 Indian Telegu, UK -0.032
Japan 0.477 Peruvian, Lima -0.043

We can see that the ranking of corrected polygenic scores for height and IQ gives higher scores to Africans compared to the uncorrected scores, as predicted on the basis of their lower background derived frequencies. The bottom place for height is occupied by East Asian populations (Japan, Chinese, Vietnamese), and the top place by North Europeans (White Americans, Finns, British) matching anthropometric descriptions and available statistics ( The bottom places of the IQ polygenic scores are occupied by South American, South Asian and African populations. It must be noted that the South Asian populations (Indian Telegu, Sri Lankan) are living in the UK and I am not aware of the existence of any reliable studies on their average IQ.

These results are encouraging because they provide discriminant validity (only a moderate correlation between the height and IQ polygenic scores, which can be explained by phylogenetic autocorrelation) and predictive validity (a moderately good fit with phenotypic population averages (IQ and height). A less than perfect fit is expected given that we have not sampled all the SNPs, that these represent only signals of polygenic pressure (thus not including all the non-additive effects) and the importance of environment for these variables, as showed from the dramatic secular trend in height and IQ observed within Western countries.

A Piffer-Mantel test (Piffer, 2015) was carried out by calculating the distances between all pairs of populations for the polygenic scores. The height polygenic score was used as the dependent variable and Fst distances + the IQ score as the independent variables.

There was a slight positive Beta coefficient for the IQ PS (0.387) but Fst was close to 0 (0.06) (Piffer, in press). The average value obtained using 100 polygenic scores from the SNPs (2+ millions) contained in Rietveld et al. (including the non-significant ones) is 0.06 with SD=0.176.if we assume that the tiny deviation from 0 (0.06) was a result of chance or residual signal contained in some of the Rietveld hits, we can calculate the deviation from null expectations: 0.387/0.176= 2.19 Zs.

A partial correlation (height ps, IQ ps, Fst) gave almost identical result (r=0.386).

To confirm that this is a sign of common selection pressures we’ll need more population samples but this is still a suggestive finding.


This article shows that it’s necessary to control for background frequencies of derived and ancestral alleles when computing population-level polygenic scores.


Davies, G., Armstrong, N., Bis, J. C., et al. (2015). Genetic contributions to variation in general cognitive function: a meta-analysis of genome-wide association studies in the CHARGE consortium (N=53949).Molecular Psychiatry, 20:183-192. doi: 10.1038/mp.2014.188

Do, R., Balick, B., Li, H., Adzhubei, I., Sunyaev, S., & Reich, D. (2015). No evidence that selection  has been less effective at removing mutations in Europeans than Africans. Nature Genetics, doi:10.1038/ng.3186

Harris, E.E. (2010). Nonadaptive processes in primate and human evolution. Yearbook of Physical Anthropology, 53: 13-45.

Henn, B.M., Botigué, L.R., Peischl, S., Dupanloup,I.,  Lipatov,M., Maples,B.K., Martin, A.R., Musharoff, S., Cann, H., Snyder,M.P., Excoffier, L., Kidd, J.M.,  Bustamante, C.D. (2015). Distance from sub-Saharan Africa predicts mutational load in diverse human genomes. PNAS ; published ahead of print December 28, 2015, doi:10.1073/pnas.1510805112

Piffer, D. (2015a). A review of intelligence GWAS hits: Their relationship to country IQ and the issue of spatial autocorrelation. Intelligence, 53, 43-50.

Piffer D. (2015b). Evidence of polygenic selection on human stature inferred from spatial distribution of allele frequencies. F1000Research, 4:15

Piffer, in press. Polygenic selection of cognitive ability: polygenic scores predict average group intelligence. Is selection signal a function of GWAS significance?

Rietveld, C.A., Medland, S.E., Derringer, J., Yang, J., Esko, T., Martin, N.W., et al. (2013). GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science, 340, 1467-1471. doi:

Rietveld, C.A., Esko, T., Davies, G., Pers, T.H., Turley, P., Benyamin, B., et al. (2014). Common genetic variants associated with cognitive performance identified using the proxy-phenotype method. Proceedings of the National Academy of Sciences, USA, 111, 13790-13794. doi:10.1073/pnas.1404623111

Wood AR, Esko T, Yang J,et al.: Defining the role of common variation in the genomic and biological architecture of adult human height. Nat Genet. 2014; 46(11): 1173–86.








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