bioRxivGeneticsPreprint
Multivariate genetic analysis of personality and cognitive traits reveals abundant pleiotropy and improves prediction
Guy HindleyiD, Alexey A. ShadriniD, Dennis van der MeeriD, Nadine ParkeriD, Weiqiu ChengiD, Kevin Sean O’ConnelliD, Shahram BahramiiD, Aihua Lin, Naz KaradagiD, Børge HoleniD, Thomas Doug BjellaiD, Chun Chieh FaniD, Torill UelandiD, Srdjan DjuroviciD, Olav Bjerkehagen SmelandiD, Oleksandr FreiiD, Anders Martin DaleiD, Ole A. AndreasseniD
- Citations
- 3
Abstract
Personality and cognition are heritable mental traits, and their genetic determinants may be distributed across interconnected brain functions. However, previous studies have employed univariate approaches which reduce complex traits to summary measures. We applied the "pleiotropy-informed" multivariate omnibus statistical test (MOSTest) to genome-wide association studies (GWAS) of 35 item and task-level measures of neuroticism and cognition from the UK Biobank (n=336,993). We identified 431 significant genetic loci and found evidence of abundant pleiotropy across personality and cognitive domains. Functional characterisation implicated genes with significant tissue-specific expression in all tested brain tissues and enriched in brain-specific gene-sets. We conditioned independent GWAS of the Big 5 personality traits and cognition on our multivariate findings, which boosted genetic discovery in other personality traits and improved polygenic prediction. These findings advance our understanding of the polygenic architecture of complex mental traits, indicating a prominence of pleiotropic genetic effects across higher-order domains of mental function. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/481967v2_ufig1.gif" ALT="Figure 1"> View larger version (89K): org.highwire.dtl.DTLVardef@1641108org.highwire.dtl.DTLVardef@9a24eeorg.highwire.dtl.DTLVardef@1eeae99org.highwire.dtl.DTLVardef@108db7f_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: human
- assay: GWAS summary statistics
- research focus: polygenic risk
- research focus: GWAS / association mapping
- approach: statistical model
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