A Review of Intelligence GWAS Hits: Their Relationship to Country IQ and the Issue of Spatial Autocorrelation

James Thompson, Psychological Comments, September 3, 2015

Country IQ (Lynn and Vanhanen 2012) has become a much researched variable, and now this paper out today links alleles associated with individual’s IQ to overall country IQ. This will  begin to provide estimates of the extent to which country intelligence levels have a genetic cause. A key methodological aspect of this paper is extracting a common factor from among the SNPs, utilizing un-weighted least squares factor analysis, yielding a metagene–this being a term utilized in genetics to describe patterns of covariance among genes.

A review of intelligence GWAS hits: Their relationship to country IQ and the issue of spatial autocorrelation

Davide Piffer, Ulster Institute for Social Research, London, UK



Published Genome Wide Association Studies (GWAS), reporting the presence of alleles exhibiting significant and replicable associations with IQ, are reviewed. The average between-population frequency (polygenic score) of nine alleles positively and significantly associated with intelligence is strongly correlated to country-level IQ (r = .91). Factor analysis of allele frequencies furthermore identified a metagene with a similar correlation to country IQ (r = .86). The majority of the alleles (seven out of nine) loaded positively on this metagene. Allele frequencies varied by continent in a way that corresponds with observed population differences in average phenotypic intelligence. Average allele frequencies for intelligence GWAS hits exhibited higher inter-population variability than random SNPs matched to the GWAS hits or GWAS hits for height. This indicates stronger directional polygenic selection for intelligence relative to height. Random sets of SNPs and Fst distances were employed to deal with the issue of autocorrelation due to population structure. GWAS hits were much stronger predictors of IQ than random SNPs. Regressing IQ on Fst distances did not significantly alter the results nonetheless it demonstrated that, whilst population structure due to genetic drift and migrations is indeed related to IQ differences between populations, the GWAS hit frequencies are independent predictors of aggregate IQ differences.

The author says:

The average frequency (polygenic score) of nine alleles positively associated with IQ and proxy phenotypes at the individual differences level in published GWAS is strongly and significantly correlated to population, or country IQ (r = .91). Factor analysis of allele frequencies yielded a metagene factor with a similar correlation to IQ (.86). The majority of alleles (seven out of nine) loaded positively on this factor. 40 unrelated SNPs were drawn at random and their frequencies factor analyzed for use as a control. The pattern of very high-magnitude positive or negative correlations suggests that spatial autocorrelation might be inflating the relationships between variables. That is to say, factors extracted utilizing random SNPs exhibited very high correlations to the GWAS hits factors (r = .6 to .98) and similarly high correlations with country IQ distances. Unexpectedly, the method of correlated vectors produced very high values also when run using the random SNPs, rendering the extremely high magnitude and significant correlation (.99, p < .05) found for the GWAS hits somewhat less impressive. However, the correlation of IQ with the GWAS hits metagene (.89) was somewhat higher than the IQ correlation with the random SNP factors (.74).

Comparison of allele frequency means for the five continental groups from the 1000 Genomes database revealed frequency differences that closely correspond to observed continent-level aggregate IQ, yielding the following pattern: East Asian > European > South Asian > American (Hispanic) > African. However, ANOVA did not yield p values that meet the conventional significance threshold (p < .05), furthermore Tukey’s test produced confidence intervals that bisected zero. The lack of statistical significance is clearly due to the very small sample size (N = 9). Increasing numbers of GWA studies will undoubtedly provide more hits in the future permitting the generation of an increasingly accurate picture of cognitive-related genetic variation, both within and between populations.

This is a very promising start, after a long review process. Once we get larger genetic samples of people in different continents it should be possible to move from country level aggregate intelligence scores to individual personal scores.

Read it all here.

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