Preprint Article Version 3 NOT YET PEER-REVIEWED

Evidence for Recent Polygenic Selection on Educational Attainment and Underlying Cognitive Abilities Inferred from GWAS Hits: A Monte Carlo Simulation Using Random SNPs

Version 1 : Received: 26 January 2017 / Approved: 27 January 2017 / Online: 27 January 2017 (03:55:50 CET)
Version 2 : Received: 28 January 2017 / Approved: 29 January 2017 / Online: 29 January 2017 (07:55:37 CET)
Version 3 : Received: 23 May 2017 / Approved: 23 May 2017 / Online: 23 May 2017 (17:08:33 CEST)

How to cite: Piffer, D. Evidence for Recent Polygenic Selection on Educational Attainment and Underlying Cognitive Abilities Inferred from GWAS Hits: A Monte Carlo Simulation Using Random SNPs. Preprints 2017, 2017010127 (doi: 10.20944/preprints201701.0127.v3). Piffer, D. Evidence for Recent Polygenic Selection on Educational Attainment and Underlying Cognitive Abilities Inferred from GWAS Hits: A Monte Carlo Simulation Using Random SNPs. Preprints 2017, 2017010127 (doi: 10.20944/preprints201701.0127.v3).

Abstract

Background: The genetic variants identified by three large genome-wide association studies (GWAS) of educational attainment were used to test a polygenic selection model. Methods: Average frequencies of alleles with positive effect (polygenic scores or PS) were compared across populations (N=26) using data from 1000 Genomes. A null model was created using frequencies of random SNPs. Results: Polygenic selection signal of educational attainment GWAS hits is high among a handful of SNPs within genomic regions replicated across GWAS publications. A polygenic score comprising 9 SNPs predicts population IQ (r=0.88), outperforming 99% of the polygenic scores obtained from sets of random SNPs (Monte Carlo p= 0.011). Its predictive power remains unaffected after controlling for spatial autocorrelation (Beta= 0.83). The largest polygenic score (161 SNPs) exhibits similar predictive power (Beta=0.8). Random polygenic scores are moderate predictors of population IQ (thanks to spatial autocorrelation), and their predictive power increases logarithmically with the number of SNPs, indicating an exponential reduction in noise. Conclusion: This study provides guidance for using GWAS hits together with random SNPs for testing polygenic selection using Monte Carlo simulations.

Subject Areas

educational attainment; polygenes; polygenic selection; IQ; GWAS

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