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. Can We Detect Polygenic Selection on Cognitive Ability Using GWAS Hits? Employing Random SNPs as a Null Model.. Preprints2017, 2017010127. https://doi.org/10.20944/preprints201701.0127.v2
Piffer, D. Can We Detect Polygenic Selection on Cognitive Ability Using GWAS Hits? Employing Random SNPs as a Null Model.. Preprints 2017, 2017010127. https://doi.org/10.20944/preprints201701.0127.v2
Piffer, D. Can We Detect Polygenic Selection on Cognitive Ability Using GWAS Hits? Employing Random SNPs as a Null Model.. Preprints2017, 2017010127. https://doi.org/10.20944/preprints201701.0127.v2
APA Style
Piffer, D. (2017). <strong><b>Can We Detect Polygenic Selection on Cognitive Ability Using GWAS Hits? Employing Random SNPs as a Null Model.</b></strong>. Preprints. https://doi.org/10.20944/preprints201701.0127.v2
Chicago/Turabian Style
Piffer, D. 2017 "<strong><b>Can We Detect Polygenic Selection on Cognitive Ability Using GWAS Hits? Employing Random SNPs as a Null Model.</b></strong>" Preprints. https://doi.org/10.20944/preprints201701.0127.v2
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.ethods: 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=0.9), outperforming 99.9% of the polygenic scores obtained from sets of random SNPs. Its predictive power remains unaffected after controlling for spatial autocorrelation. Even 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.
Keywords
GWAS; educational attainment; polygenic selection
Subject
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.