Open pre-computed polygenic scores. My PGS-computing code and the 1000 Genomes files were made publicly available on OSF (https://osf.io/g9yx8/). They were converted to .csv format so that anyone can load them on R without having to convert them from VCF format or PLINk-like formats (i.e. .bed). I will compute correlations between the latest polygenic score, as inferred from the largest GWAS of Educational attainment (EDU) to date and the polygenic score inferred from previous GWAS of Educational attainment and IQ (Piffer, 2015).

PS_IQ=read.csv(file.choose(),header=TRUE)#Lee_PGS_1kg - IQ_EDU.csv
PS_IQ$Leeetal_PGS=scale(PS_IQ$Leeetal2018,center = TRUE, scale= TRUE)
PS_IQ$Piffer2015_PGS=scale(PS_IQ$Piffer2015,center=TRUE, scale=TRUE)
library(ggplot2)
library(ggrepel)

Show standardized PGS from latest EDU GWAS (Lee et al., 2018) and population IQs

PS_IQ[-c(2,3)]
##             Population Piffer.2015.IQ Leeetal_PGS Piffer2015_PGS
## 1     Afr.Car.Barbados           83.0 -1.37594223    -1.25437693
## 2            US Blacks           85.0 -1.34771901    -1.04680466
## 3   Bengali Bangladesh           81.0 -0.23772175     0.02962202
## 4          Chinese Dai             NA  1.00457195     0.83495705
## 5          Utah Whites           99.0  0.67001603     0.52870288
## 6      Chinese, Bejing          105.0  1.52051142     1.53140171
## 7       Chinese, South          105.0  1.32390023     1.32496372
## 8            Colombian           83.5  0.15170640     0.02281637
## 9        Esan, Nigeria           71.0 -1.37653934    -1.38935562
## 10             Finland          101.0  1.01435644     0.64439890
## 11         British, GB          100.0  0.50219103     0.67502432
## 12 Gujarati Indian, Tx             NA  0.14736720     0.33360764
## 13             Gambian           62.0 -1.37601288    -1.56630247
## 14      Iberian, Spain           97.0  0.88241636     0.52416578
## 15   Indian Telegu, UK             NA  0.16962908     0.26555116
## 16               Japan          105.0  1.44907202     1.41116859
## 17             Vietnam           99.4  1.08120251     1.22741609
## 18        Luhya, Kenya           74.0 -1.44924767    -1.69220696
## 19 Mende, Sierra Leone           64.0 -1.60059964    -1.49257461
## 20     Mexican in L.A.           88.0 -0.36510679     0.04436759
## 21      Peruvian, Lima           85.0 -1.06348465     0.19749467
## 22   Punjabi, Pakistan           84.0 -0.09592599     0.16346643
## 23        Puerto Rican           83.5  0.02261036    -0.04524011
## 24      Sri Lankan, UK           79.0 -0.03207193    -0.08834255
## 25      Toscani, Italy           99.0  0.69588907     0.45043793
## 26     Yoruba, Nigeria           71.0 -0.31506821    -1.63435895

Corelation between IQ and EDU 2018 PGS:

Correlationb between EDU 2018 PGS and Piffer 2015 PGS

Show correlation matrix plot

##                Piffer.2015.IQ Leeetal_PGS Piffer2015_PGS
## Piffer.2015.IQ           1.00        0.87           0.91
## Leeetal_PGS              0.87        1.00           0.91
## Piffer2015_PGS           0.91        0.91           1.00
##                Piffer.2015.IQ Leeetal_PGS Piffer2015_PGS
## Piffer.2015.IQ              1        0.87           0.91
## Leeetal_PGS                NA        1.00           0.91
## Piffer2015_PGS             NA          NA           1.00

References:

Lee et al. (2018). Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment“. Nature Genetics

Piffer, D. (2015). A review of intelligence GWAS hits: Their relationship to country IQ and the issue of spatial autocorrelation. Intelligence, 53: 43-50.

Piffer, D. (2018, May 25). 1000 Genomes frequencies. Retrieved from osf.io/g9yx8