Genome-wide association study

Phenotype

Mathematics

Evolutionary biology

Biology

Gene

Statistics

Genotype

Genetic association

Single-nucleotide polymorphism

Multivariate statistics

Univariate

Pleiotropy

Computational biology

Genetics

Structural equation modeling

Quantitative trait locus

Genetic architecture

Nature Human Behaviour

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. Grotzinger et al. develop a multivariate method for analysing the joint genetic architectures of complex traits: genomic structural equation modelling. They provide several applications of the method, including a joint analysis of five psychiatric traits.

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