eLife Assessment: The Multivariate SEM-PGS Model: Using Polygenic Scores to Investigate Cross-Trait Genetic Nurture and Assortative Mating

Genetic nurture effects and assortative mating (AM) occur across a range of human behaviors and can bias estimates from traditional genetic models. These influences are typically studied univariately, within the same trait. However, estimation of cross-trait genetic nurture effects and cross-trait AM remains underexplored due to the absence of suitable approaches. To address this, we developed a multivariate extension of the SEM-PGS model for datasets with genotyped and phenotyped parents and offspring, enabling joint estimation of within-trait and cross-trait genetic and environmental influences. By integrating haplotypic polygenic scores (PGSs) into a structural equation modeling framework, the model simultaneously estimates sametrait and cross-trait direct effects, genetic nurture, vertical transmission, and AM. We also provide the first formal description of how copaths can be used to model multivariate AM, and we derive the corresponding parameter expectations in matrix form. Forward-time Monte Carlo simulations under varying conditions of and Ntrio demonstrate that the model yields unbiased estimates of both within-trait and cross-trait effects when assumptions were met. The precision of estimates was adequate with large sample sizes (Ntrio > 16k) and improved as PGS predictive power increased. In addition, our simulation results show that failing to model cross-trait effects biases within-trait estimates, underscoring the importance of incorporating cross-trait effects. The multivariate SEM-PGS model offers a powerful and flexible tool for disentangling gene-environment interplay and advancing the understanding of familial influences on human traits.