In a companion paper Balbona et al. (Behav Genet, in press), we introduced a series of causal models that use polygenic scores from transmitted and nontransmitted alleles, the offspring trait, and parental traits to estimate the variation due to the environmental influences the parental trait has on the offspring trait (vertical transmission) as well as additive genetic effects. These models also estimate and account for the gene-gene and gene-environment covariation that arises from assortative mating and vertical transmission respectively. In the current study, we simulated polygenic scores and phenotypes of parents and offspring under genetic and vertical transmission scenarios, assuming two types of assortative mating. We instantiated the models from our companion paper in the OpenMx software, and compared the true values of parameters to maximum likelihood estimates from models fitted on the simulated data to quantify the bias and precision of estimates. We show that parameter estimates from these models are unbiased when assumptions are met, but as expected, they are biased to the degree that assumptions are unmet. Standard errors of the estimated variances due to vertical transmission and to genetic effects decrease with increasing sample sizes and with increasing \(r^2\) values of the polygenic score. Even when the polygenic score explains a modest amount of trait variation (\(r^2=.05\)), standard errors of these standardized estimates are reasonable (\(< .05\)) for \(n=16K\) trios, and can even be reasonable for smaller sample sizes (e.g., down to 4K) when the polygenic score is more predictive. These causal models offer a novel approach for understanding how parents influence their offspring, but their use requires polygenic scores on relevant traits that are modestly predictive (e.g., \(r^2>.025)\) as well as datasets with genomic and phenotypic information on parents and offspring. The utility of polygenic scores for elucidating parental influences should thus serve as additional motivation for large genomic biobanks to perform GWAS’s on traits that may be relevant to parenting and to oversample close relatives, particularly parents and offspring.

在 Balbona 等人的配套论文中。 （Behav Genet，出版中），我们引入了一系列因果模型，使用来自遗传和非遗传等位基因、后代性状和亲本性状的多基因评分来估计由于亲本性状对后代性状的环境影响而产生的变异（垂直传播）以及附加遗传效应。这些模型还估计并解释了分别由选型交配和垂直传播引起的基因-基因和基因-环境的协变。在当前的研究中，我们模拟了遗传和垂直传播场景下父母和后代的多基因评分和表型，假设两种类型的选型交配。我们在 OpenMx 软件中实例化了配套论文中的模型，并将参数的真实值与模拟数据拟合模型的最大似然估计进行比较，以量化估计的偏差和精度。我们表明，当满足假设时，这些模型的参数估计是无偏的，但正如预期的那样，它们在不满足假设的程度上存在偏差。由于垂直传播和遗传效应而导致的估计方差的标准误差随着样本量的增加和多基因得分的\(r^2\)值的增加而减少。即使多基因得分解释了适度的性状变异 ( \(r^2=.05\) )，这些标准化估计的标准误差对于\(n=16K\ ) 也是合理的 ( \(< .05\) ) ）三重奏，当多基因评分更具预测性时，对于较小的样本量（例如，低至 4 K ）甚至可以是合理的。 这些因果模型提供了一种新的方法来理解父母如何影响其后代，但它们的使用需要对具有适度预测性的相关性状进行多基因评分（例如， \(r^2>.025)\）以及具有基因组和父母和后代的表型信息。因此，多基因评分在阐明亲代影响方面的效用应成为大型基因组生物库对可能与养育相关的性状进行 GWAS 的额外动力，并对近亲（尤其是父母和后代）进行过度采样。