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

Andrew D. Grotzinger, Mijke Rhemtulla, Ronald de 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, Elliot M. Tucker-Drob

Research output: Contribution to JournalArticleAcademicpeer-review

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.

LanguageEnglish
JournalNature Human Behaviour
DOIs
Publication statusPublished - 1 Mar 2019

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Genome-Wide Association Study
Single Nucleotide Polymorphism
Phenotype
Genetic Models
Psychiatry
Multivariate Analysis
Joints

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Grotzinger, Andrew D. ; Rhemtulla, Mijke ; de Vlaming, Ronald ; Ritchie, Stuart J. ; Mallard, Travis T. ; Hill, W. David ; Ip, Hill F. ; Marioni, Riccardo E. ; McIntosh, Andrew M. ; Deary, Ian J. ; Koellinger, Philipp D. ; Harden, K. Paige ; Nivard, Michel G. ; Tucker-Drob, Elliot M. / Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. In: Nature Human Behaviour. 2019.
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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.",
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Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. / Grotzinger, Andrew D.; Rhemtulla, Mijke; de Vlaming, Ronald; Ritchie, Stuart J.; Mallard, Travis T.; Hill, W. David; Ip, Hill F.; Marioni, Riccardo E.; McIntosh, Andrew M.; Deary, Ian J.; Koellinger, Philipp D.; Harden, K. Paige; Nivard, Michel G.; Tucker-Drob, Elliot M.

In: Nature Human Behaviour, 01.03.2019.

Research output: Contribution to JournalArticleAcademicpeer-review

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AU - Koellinger, Philipp D.

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AU - Nivard, Michel G.

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