Structural equation modeling (SEM) is a powerful, comprehensive, and flexible multivariate statistical method for modeling relationships between observed and latent variables. However, in genetic association analysis, frequentist approaches to fitting SEMs do not always lead to convergence and admissible solutions for complex models, categorical variables, complicated data structures such as pedigree data and small sample sizes. Accordingly, to conduct a SEM pedigree data analysis, Stan platform as a probabilistic programming language was applied in our study to propose a new version of the Bayesian approach that adopts Hamiltonian Monte Carlo (HMC) and data augmentation techniques. At first, a comprehensive simulation study was conducted to compare the precision of each parameter of the suggested method with that of the classic technique in terms of bias, alpha error rate, and coverage probability. After that, the method was applied to real data with a conceptual model including ordinal indicators in order to conduct genetic association analysis of two well‐known genetic markers with metabolic syndrome trait. The simulation findings revealed the proposed Bayesian method was a more efficient technique than MLE approach. Moreover, Bayesian approach yielded a better statistical performance in solving the problems than did classic approach.

中文翻译：

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一般谱系数据分析中的贝叶斯结构方程模型

结构方程模型（SEM）是一种强大，全面且灵活的多元统计方法，用于对观测变量和潜在变量之间的关系进行建模。但是，在遗传关联分析中，拟合SEM的频繁方法并不总会导致收敛和为复杂模型，分类变量，复杂数据结构（如谱系数据和小样本量）提供可接受的解决方案。因此，为了进行SEM谱系数据分析，在我们的研究中使用了Stan平台作为一种概率编程语言，以提出采用汉密尔顿蒙特卡洛（HMC）和数据增强技术的贝叶斯方法的新版本。首先，进行了全面的模拟研究，以比较建议的方法的每个参数与传统技术在偏差，α误码率和覆盖概率方面的精度。之后，将该方法应用于具有序数指示符的概念模型的真实数据，以便对两个具有代谢综合征特征的著名遗传标记进行遗传关联分析。仿真结果表明，提出的贝叶斯方法比MLE方法更有效。此外，与经典方法相比，贝叶斯方法在解决问题上具有更好的统计性能。将该方法应用于包含序数指示符的概念模型的真实数据，以便对两个具有代谢综合征特征的著名遗传标记进行遗传关联分析。仿真结果表明，提出的贝叶斯方法比MLE方法更有效。此外，与经典方法相比，贝叶斯方法在解决问题上具有更好的统计性能。将该方法应用于包含序数指示符的概念模型的真实数据，以对具有代谢综合征特征的两个著名遗传标记进行遗传关联分析。仿真结果表明，提出的贝叶斯方法比MLE方法更有效。此外，与经典方法相比，贝叶斯方法在解决问题上具有更好的统计性能。