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

Andrew D. Grotzinger (), Mijke Rhemtulla, Ronald Vlaming, Stuart J. Ritchie, Travis T. Mallard, W. David Hill, Hill F. Ip, Riccardo E. Marioni, Andrew M. McIntosh, Ian J. Deary, Philipp D. Koellinger, K. Paige Harden, Michel G. Nivard and Elliot M. Tucker-Drob
Additional contact information
Andrew D. Grotzinger: University of Texas at Austin
Mijke Rhemtulla: University of California, Davis
Ronald Vlaming: Vrije Universiteit Amsterdam
Stuart J. Ritchie: University of Edinburgh
Travis T. Mallard: University of Texas at Austin
W. David Hill: University of Edinburgh
Hill F. Ip: Vrije Universiteit University Amsterdam
Riccardo E. Marioni: University of Edinburgh
Andrew M. McIntosh: University of Edinburgh
Ian J. Deary: University of Edinburgh
Philipp D. Koellinger: Vrije Universiteit Amsterdam
K. Paige Harden: University of Texas at Austin
Michel G. Nivard: Vrije Universiteit University Amsterdam
Elliot M. Tucker-Drob: University of Texas at Austin

Nature Human Behaviour, 2019, vol. 3, issue 5, 513-525

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41562-019-0566-x

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