Results (excerpt from https://thewinnower.com/papers/estimating-the-genotypic-intelligence-of-populations-and-assessing-the-impact-of-socioeconomic-factors-and-migrations )

Factor analysis of 4 IQ increasing alleles.

A previous study found that the specific factor extraction method employed did not affect results much except for the use of principal components analysis which produced inflated loadings (Kirkegaard, 2014). To further examine how factor extraction method influences results several methods were employed (minimum residuals, weighted least squares, generalized least squares, principal axis factoring, maximum likelihood) and factor scores were obtained using different methods (Thurstone, Harman, and Bartlett). These all produced nearly identical results, yet they were averaged to create a composite vector. The composite factor had slightly higher validity, as suggested by its slightly higher correlation with Lynn and Vanhanen’s national IQs (r=0.92 vs 0.91).  Conversely, the component extracted with PCA had a slightly lower correlation (r=0.88). These are all in the right direction (positive) and high.

Table 1: Factor loadings of 4 g increasing alleles.

Factor scores and average population IQs are reported in table 2.

Table 2: Factor scores of 4 g increasing alleles and phenotypic IQs.

*AFR= Sub-Saharan African; HISP= Hispanic/Latin American; E.ASN= East Asian; Eur= European; SAS= South Asian

The correlation between National IQ and factor scores was 0.92 (N=23, p=0.000). Together with the factor loadings, this suggests that this factor represents a signal of polygenic selection on human intelligence and can be used as an indicator of population-level “genotypic intelligence” or “intellectual potential”.

The regression of IQ on the 4 SNPs g factor is plotted in figures 1a and 1b. Inspection of the Q-Q(residuals vs. theoretical quantiles) plot revealed that residuals were normally distributed.