标题： 多变量SEM-PGS模型：使用多基因评分来研究跨特征遗传养育和选择性婚配 显示英文标题

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

摘要： 遗传养育效应和选择性婚配（AM）出现在许多人类行为中，并可能对传统遗传模型的估计产生偏差。 这些影响通常在同一个特征内单独研究。 然而，由于缺乏合适的方法，跨特征的遗传养育效应和跨特征的AM的估计仍研究不足。 为了解决这个问题，我们开发了SEM-PGS模型的多变量扩展，适用于基因型和表型父母及后代的数据集，能够联合估计同一特征和跨特征的遗传和环境影响。 通过将单倍型多基因评分（PGS）整合到结构方程建模框架中，该模型同时估计同一特征和跨特征的直接效应、遗传养育、垂直传递和选择性婚配。 我们还提供了首次正式描述，说明如何使用copaths来模拟多变量选择性婚配，并以矩阵形式推导相应的参数期望。 在不同r^2_PGS和N_trio条件下的正向时间蒙特卡洛模拟表明，当假设满足时，该模型能提供无偏的同一特征和跨特征效应估计。 在大样本量（N_trio > 16k）下，估计的精度足够，并随着PGS预测能力的提高而改善。 此外，我们的模拟结果表明，未能建模跨特征效应会偏差同一特征的估计，强调了纳入跨特征效应的重要性。 多变量SEM-PGS模型为解析基因-环境相互作用和推进对家庭特征影响的理解提供了一个强大且灵活的工具。 显示英文摘要

摘要： Genetic nurture effects and assortative mating (AM) occur across many 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 (PGS) into a structural equation modeling framework, the model simultaneously estimates same-trait and cross-trait direct effects, genetic nurture, vertical transmission, and assortative mating. We also provide the first formal description of how copaths can be used to model multivariate assortative mating and derive the corresponding parameter expectations in matrix form. Forward-time Monte Carlo simulations under varying conditions of r^2_PGS and N_trio demonstrate that the model yields unbiased estimates of both within-trait and cross-trait effects when assumptions are met. The precision of estimates was adequate with large sample sizes (N_trio > 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.