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Modeling the complex gene × environment interplay in the simulated rheumatoid arthritis GAW15 data using latent variable structural equation modeling

机译：使用潜在变量结构方程模型模拟风湿性关节炎GAW15数据中的复杂基因×环境相互作用

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摘要

Rheumatoid arthritis is a complex disease that appears to involve multiple genetic and environmental factors. Using the Genetic Analysis Workshop 15 simulated rheumatoid arthritis data and the structural equation modeling framework, we tested hypothesized "causal" rheumatoid arthritis model(s) by employing a novel latent gene construct approach that models individual genes as latent variables defined by multiple dense and non-dense single-nucleotide polymorphisms (SNPs). Our approach produced valid latent gene constructs, particularly with dense SNPs, which when coupled with other factors involved in rheumatoid arthritis, were able to generate good fitting models by certain goodness of fit indices. We observed that Gene F, C, DR, sex and smoking were significant predictors of rheumatoid arthritis but Genes A and E were not, which was generally, but not entirely, consistent with how the data were simulated. Our approach holds promise in unravelling complex diseases and improves upon current "one SNP (haplotype)-at-a-time" regression approaches by decreasing the number of statistical tests while minimizing problems with multicolinearity and haplotype estimation algorithm error. Furthermore, when genes are modeled as latent constructs simultaneously with other key cofactors, the approach provides enhanced control of confounding that should lead to less biased effect estimates among genes as well as between gene(s) and the complex disease. However, further study is needed to quantify bias, evaluate fit index disparity, and resolve multiplicative latent gene interactions. Moreover, because some a priori biological information is needed to form an initial substantive model, our approach may be most appropriate for candidate gene SNP panel applications.
机译：类风湿关节炎是一种复杂的疾病，似乎涉及多种遗传和环境因素。使用遗传分析研讨会15模拟的类风湿关节炎数据和结构方程建模框架，我们通过采用新颖的潜伏基因构建方法对单个基因作为潜伏变量进行建模（由多个致密和非潜伏性定义），测试了假设的“因果性”类风湿关节炎模型-致密单核苷酸多态性（SNP）。我们的方法产生了有效的潜在基因构建体，尤其是具有密集SNP的基因构建体，当与类风湿性关节炎相关的其他因素结合时，它们能够以一定的拟合优度生成良好的拟合模型。我们观察到，基因F，C，DR，性别和吸烟是类风湿关节炎的重要预测指标，但基因A和E并非如此，这与数据模拟方式通常（但不完全）一致。我们的方法在揭示复杂疾病方面具有希望，并通过减少统计测试的数量，同时最大程度地减少多共线性和单倍型估计算法错误的问题，改进了当前的“一次SNP（单倍型）一次”回归方法。此外，当将基因与其他关键辅因子同时建模为潜在构建体时，该方法可增强对混杂的控制，从而可减少基因之间以及基因与复杂疾病之间的偏倚效应估计。但是，需要进行进一步的研究以量化偏倚，评估拟合指数差异并解决可乘性潜在基因相互作用。此外，由于需要一些先验生物学信息来形成初始的实体模型，因此我们的方法可能最适合候选基因SNP面板应用。

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