Preprint Article Version 1 This version not peer reviewed

Polygenic Selection, Polygenic Scores, Spatial Autocorrelation and Correlated Allele Frequencies. Can We Model Polygenic Selection on Intellectual Abilities?

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. Polygenic Selection, Polygenic Scores, Spatial Autocorrelation and Correlated Allele Frequencies. Can We Model Polygenic Selection on Intellectual Abilities?. Preprints 2017, 2017010127 (doi: 10.20944/preprints201701.0127.v1). Piffer, D. Polygenic Selection, Polygenic Scores, Spatial Autocorrelation and Correlated Allele Frequencies. Can We Model Polygenic Selection on Intellectual Abilities?. Preprints 2017, 2017010127 (doi: 10.20944/preprints201701.0127.v1).

Abstract

The majority of polygenic selection signal of educational attainment GWAS hits is confined to 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, and their predictive power increases logarithmically with the number of SNPs, indicating an exponential reduction in noise.Thus, the predictive power of polygenic scores has to be scaled in proportion to the number of SNPs composing them.

Subject Areas

GWAS; educational attainment; polygenic selection

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