The genetic correlation between educational attainment, intracranial volume and IQ is due to recent polygenic selection on general cognitive ability

Abstract

This is the first paper to report evidence of a cross-population genetic correlation between educational attainment and IQ. Principal component analysis (PCA) was used to identify the underlying genetic structure resulting from recent (after the out of Africa migration, about 60Kya) directional selection. Converging evidence from two different genetic databases (1000 Genomes and ALFRED, comprising 14 populations and 8 racial groups, respectively) and two independent sets of genes (14 in total) found by GWAS to have a significant association with IQ and Educational Attainment, reveals a high correlation (around 0.9) between these two social science constructs across populations and ethnic groups. This correlation in turn provided convergent validity for the genetic measures identified with PCA. Two alleles associated with higher intracranial volume were positively correlated to the two PCs. A separate analysis carried out on an independent set of alleles related to human stature suggests that this trait was subject to different directional selective pressures and provided evidence for the discriminant validity of alleles supposedly related to IQ and educational attainment. One-way ANOVA showed that the average frequencies of height increasing alleles for three racial groups (East Asian, Sub-Saharan African and European) differed significantly and that Africans had significantly higher frequencies than East Asians. A significant negative genetic correlation (r around 0.9) between height and intelligence was found at a cross-population level, contrasting the generally positive correlation found within populations. Evolutionary explanations in terms of Allen’s rule and the theory of cold winters are attempted. The GWAS hits for height were from studies carried out on different ethnic groups, and the converging results suggests that the same genes have similar effects across racial groups. This paper provides evidence that Principal Component Analysis and ANOVA can be used to detect deviations from random drift and detect directional selection on polygenic traits in recent human evolutionary history.