New genes, same results: group-level genotypic intelligence for 26 and 52 populations

Davide Piffer

pifferdavide@gmail.com

I recently posted a pretty detailed account of my analysis of the new intelligence GWAS, based on the latest GWAS of intelligence. (Un)surprisingly, the estimates of genotypic intelligence (or actually to be precise, of polygenic selection strength, because genotypic intelligence also includes non-additive components) are almost identical to those from my previous 2013 and 2015 studies. By this, I mean that the factor and polygenic score I had estimated for 26 populations in 2015 are almost identical (r=0.96-0.99) to the factor extracted from the new intelligence GWAS (18 SNPs) and from a factor extracted by pooling together the hits from two educational attainment GWAS published after my 2015 study (9 replicated genomic loci), see my paper for more details. This is called a successful replication. Since the old and new results are almost identical, I report the post-2015 factor scores. Robustness of the findings is supported by Monte Carlo simulation using REAL SNPs (not computer-generated junk), which is the best technique to test the robustness of these findings, since it includes all possible sorts of confounding factors (LD decay, spatial autocorrelation, etc.) in one omnibus test.

Table 1. Factor scores for educational attainment and intelligence

Population G Factor score (18 SNPs) EA  factor score (9 SNPs)
Afr.Car.Barbados -1.276 -1.351
US Blacks -0.961 -1.177
Bengali Bangladesh -0.075 -0.209
Chinese Dai 1.35 1.017
Utah Whites 0.844 0.471
Chinese, Bejing 1.109 1.511
Chinese, South 1.208 1.382
Colombian 0.357 0.01
Esan, Nigeria -1.66 -1.453
Finland 0.771 0.702
British, GB 0.797 0.745
Gujarati Indian, Tx -0.049 0.271
Gambian -1.358 -1.397
Iberian, Spain 0.631 0.35
Indian Telegu, UK -0.074 0.049
Japan 0.878 1.342
Vietnam 1.267 1.346
Luhya, Kenya -1.599 -1.488
Mende, Sierra Leone -1.444 -1.403
Mexican in L.A. 0.215 0.056
Peruvian, Lima -0.06 0.05
Punjabi, Pakistan 0.066 0.24
Puerto Rican 0.375 -0.004
Sri Lankan, UK -0.391 0.134
Toscani, Italy 0.764 0.248
Yoruba, Nigeria -1.684 -1.443

 

Some may remember I also published factors derived from ALFRED, whose sample is bigger than 1000 Genomes (50-75 populations), but the coverage is much weaker.

I looked up the 18 intelligence GWAS SNPs and the 9 EA quasi-replicated SNPs and could find 4 in ALFRED. Factor analysis was run on them, producing a very interesting factor. For ease of interpretation, I report results ranked from highest to lowest:

Continent Population Factor
EastAsia Tujia 1.507
East Asia Mongolian 1.358
EastAsia Daur 1.246
EastAsia Yi 1.19
EastAsia Koreans 1.127
EastAsia Miao 1.078
EastAsia Japanese 1.018
EastAsia Dai 0.987
EastAsia Hezhe 0.98
EastAsia Han 0.936
EastAsia Lahu 0.877
EastAsia Tu 0.828
EastAsia Xibe 0.802
Europe Orcadian 0.753
EastAsia She 0.737
EastAsia Uyghur 0.566
Asia Hazara 0.506
Asia Kalash 0.475
Asia Oroqen 0.445
Europe Italians_N 0.437
Europe Italians_C 0.404
SE Asia Cambodians, Khmer 0.34
Siberia Yakut 0.311
Europe Adygei 0.257
Asia Druze 0.254
Europe French 0.217
Asia Burusho 0.151
EastAsia Naxi 0.113
Europe Russians 0.073
Asia Balochi 0.055
Asia Palestinian -0.071
Europe Basque -0.088
Asia Bedouin -0.156
Europe Sardinian -0.225
Asia Brahui -0.334
Asia Pashtun -0.426
Asia Sindhi -0.438
Oceania Melanesian, Nasioi -0.533
Oceania Papuan New Guinean -0.569
Africa Mozabite -0.768
Africa Mandenka -1.153
Africa Yoruba -1.27
NorthAmerica Maya, Yucatan -1.3
NorthAmerica Pima, Mexico -1.312
SouthAmerica Amerindians -1.366
Africa Biaka -1.369
Africa Bantu Kenya -1.381
SouthAmerica Surui -1.382
Africa Mbuti -1.415
Africa Bantu SA -1.454
Africa San -1.488
SouthAmerica Karitiana -1.53
     

We see the that East Asians are at the top. Mongolic tribes from the north, such as Mongolians and the Daur, occupy the top positions. These populations live in really cold climates, and would provide suggestive evidence to the cold winter theory. The Siberian Yakut however, do not fare as well as the East Asians, despite living in cold climates. However, the Yakut are not a Mongolic tribe, but they belong to the Turkic ethnic group.

ALFRED has data from groups not present in 1000 Genomes, such as the Amerindian tribes or the Oceanians.

Let’s have a look at the sub-continental average factor scores:

Continent Factor
E Asia 0.959
SE Asia 0.34
Siberia 0.311
Europe 0.293
M East 0.009
W Asia -0.002
Oceania -0.551
North Africa -0.768
Sub-S. Africa -1.287
America -1.378

Native Americans and Africans occupy the lowest places, despite being genetically very different. The Native American result is a huge problem for people who want to explain the pattern in term of drift or migrations, because despite being the closest genetically to the East Asians, they are at the opposite of the spectrum in terms of factor scores.

This also suggests that whatever created the East Asian advantage happened after 15kya (the earliest estimate of a migration across the Bering strait into the Americas).  It is possible that the extremely low population density in the Americas reduced intraspecific competition, hence selection pressure on higher intelligence was lower.

I calculated the correlation between distance from Eastern Africa (Addis Ababa) and factor scores and this was negative (around -0.45), not supporting the novel environment hypothesis a la Kanazawa.

It seems that what caused different selection pressures on different populations is a mix of cold winters, population size and gene-culture co-evolution.