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.
Highlights
IntroductionPopulation geneticists have recognized that most traits are highly polygenic, and have moved away from the study of genetic evolution using the single-gene, Mendelian approach, towards models that examine many genes together (i.e. polygenic models)
Over the last decade, population geneticists have recognized that most traits are highly polygenic, and have moved away from the study of genetic evolution using the single-gene, Mendelian approach, towards models that examine many genes together.research into global variations in complex traits shows a significant amount of differentiation, for example in height [1], cardiovascular disease [2] and BMI [3,4]
In both 1000 Genomes and Genome Aggregation Database (gnomAD), there was a weak trend for lower genome-wide association studies (GWAS) significance SNPs to be less predictive of population IQ
Summary
Population geneticists have recognized that most traits are highly polygenic, and have moved away from the study of genetic evolution using the single-gene, Mendelian approach, towards models that examine many genes together (i.e. polygenic models). Research into global variations in complex traits shows a significant amount of differentiation, for example in height [1], cardiovascular disease [2] and BMI [3,4]. Identifying polygenic adaptation is complicated by several factors. Identifying a number of SNPs which is sufficient to explain at least 5 or 10% of the total variance in a trait requires very large samples, and these have become available only recently Environmental factors that differ across populations can influence the phenotype, masking genetic differentiation
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
