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

TY - JOUR

T1 - Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits

AU - Grotzinger, Andrew D.

AU - Rhemtulla, Mijke

AU - de Vlaming, Ronald

AU - Ritchie, Stuart J.

AU - Mallard, Travis T.

AU - Hill, W. David

AU - Ip, Hill F.

AU - Marioni, Riccardo E.

AU - McIntosh, Andrew M.

AU - Deary, Ian J.

AU - Koellinger, Philipp D.

AU - Harden, K. Paige

AU - Nivard, Michel G.

AU - Tucker-Drob, Elliot M.

PY - 2019/5/1

Y1 - 2019/5/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85064015118&partnerID=8YFLogxK

U2 - 10.1038/s41562-019-0566-x

DO - 10.1038/s41562-019-0566-x

M3 - Article

C2 - 30962613

AN - SCOPUS:85064015118

SN - 2397-3374

VL - 3

SP - 513

EP - 525

JO - Nature Human Behaviour

JF - Nature Human Behaviour

IS - 5

ER -