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Nat Hum Behav. 2019 May;3(5):513-525. doi: 10.1038/s41562-019-0566-x. Epub 2019 Apr 8.

Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits.

Author information

1
Department of Psychology, University of Texas at Austin, Austin, TX, USA. agrotzin@utexas.edu.
2
Department of Psychology, University of California, Davis, Davis, CA, USA.
3
Department of Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
4
Erasmus University Rotterdam Institute for Behavior and Biology, Rotterdam, The Netherlands.
5
Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK.
6
Department of Psychology, University of Edinburgh, Edinburgh, UK.
7
Department of Psychology, University of Texas at Austin, Austin, TX, USA.
8
Department of Biological Psychology, Vrije Universiteit University Amsterdam, Amsterdam, The Netherlands.
9
Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.
10
Division of Psychiatry, University of Edinburgh, Edinburgh, UK.
11
Population Research Center, University of Texas at Austin, Austin, TX, USA.

Abstract

Genetic correlations estimated from genome-wide association studies (GWASs) reveal pervasive pleiotropy across a wide variety of phenotypes. We introduce genomic structural equation modelling (genomic SEM): a multivariate method for analysing the joint genetic architecture of complex traits. Genomic SEM synthesizes genetic correlations and single-nucleotide polymorphism heritabilities inferred from GWAS summary statistics of individual traits from samples with varying and unknown degrees of overlap. Genomic SEM can be used to model multivariate genetic associations among phenotypes, identify variants with effects on general dimensions of cross-trait liability, calculate more predictive polygenic scores and identify loci that cause divergence between traits. We demonstrate several applications of genomic SEM, including a joint analysis of summary statistics from five psychiatric traits. We identify 27 independent single-nucleotide polymorphisms not previously identified in the contributing univariate GWASs. Polygenic scores from genomic SEM consistently outperform those from univariate GWASs. Genomic SEM is flexible and open ended, and allows for continuous innovation in multivariate genetic analysis.

PMID:
30962613
PMCID:
PMC6520146
[Available on 2019-10-08]
DOI:
10.1038/s41562-019-0566-x

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