Evidence for Recent Polygenic Selection on Educational Attainment and Intelligence Inferred from Gwas Hits: A Replication of Previous Findings Using Recent Data

Abstract

Genetic variants identified by three large genome-wide association studies (GWAS) of educational attainment (EA) were used to test a polygenic selection model. Weighted and unweighted polygenic scores (PGS) were calculated and compared across populations using data from the 1000 Genomes (n = 26), HGDP-CEPH (n = 52) and gnomAD (n = 8) datasets. The PGS from the largest EA GWAS was highly correlated to two previously published PGSs (r = 0.96–0.97, N = 26). These factors are both highly predictive of average population IQ (r = 0.9, N = 23) and Learning index (r = 0.8, N = 22) and are robust to tests of spatial autocorrelation. Monte Carlo simulations yielded highly significant p values. In the gnomAD samples, the correlation between PGS and IQ was almost perfect (r = 0.98, N = 8), and ANOVA showed significant population differences in allele frequencies with positive effect. Socioeconomic variables slightly improved the prediction accuracy of the model (from 78–80% to 85–89%), but the PGS explained twice as much of the variance in IQ compared to socioeconomic variables. In both 1000 Genomes and gnomAD, there was a weak trend for lower GWAS significance SNPs to be less predictive of population IQ. Additionally, a subset of SNPs were found in the HGDP-CEPH sample (N = 127). The analysis of this sample yielded a positive correlation with latitude and a low negative correlation with distance from East Africa. This study provides robust results after accounting for spatial autocorrelation with Fst distances and random noise via an empirical Monte Carlo simulation using null SNPs.

Global Ancestry and Cognitive Ability

“…Thus, contrary to some arguments, European-specific alleles do not seem to be biasing prediction with eduPGS. Our results corroborate those of Piffer [111] who found a strong ecological correlation between MTAG-derived SNPs and population IQ (r = 0.86). We also found, using both regression and path analysis that, while the eduPGS mediates the association between European ancestry and cognitive ability, skin color scores do not.…”

“…Although the validity in the African-American sample was approximately half of that in the European-American sample (r AA = 0.1115; r EA = 0.2269), the relations were statistically significant in both populations (p < 0.0001). As with Piffer [111] we found large African-/European-American differences in these eduPGS (d = 1.89). Using the beta in the African-American sample and controlling for the effect of European ancestry (B = 0.124; Model 1b; Table 10), we estimate that the known eduPGS can naïvely explain as much as 20%-25% of the African-/European-American intelligence gap.…”

“…This theory supposes that the European ancestors of African-Americans were genetically disposed toward either lower or higher cognitive ability in a systematic way. Since Piffer [111] found that other African descent groups have similarly low eduPGS, this explanation would necessitate that the eduPGS are not indexing genetic differences related to cognitive ability in African groups. Determining if this is the case is important for future research.…”

No support for the genetic hypothesis of the Black-white achievement gap using polygenic scores and tests for divergent selection

“…A research team conducting a GWAS takes a large group of (mostly) unrelated individuals, usually from the same ethnicity, and correlates hundreds of thousands or millions of variable DNA base pairs (SNPs) with the phenotype. As stockpiles of GWAS data have accumulated, some have attempted to employ polygenic scores, which are linear combinations of alleles identified by a GWAS used to produce an estimated genetic trait value, to test racial genetic hypotheses about achievement and cognitive ability 21,22 .…”

“…These studies have claimed to support a genetic hypothesis with one of two methodologies: calculating correlations between national polygenic scores and national IQ scores 21 ; and path modeling of the relationship between ancestry, polygenic scores and IQ scores 22 . Several problems with these analyses exist.…”