Problems in testing gene-by-enviornment interaction in behavior genetic designs

描述(由申请人提供)：这是一种应用程序，用于评估在定量行为遗传学研究中存在基因-环境相关性的情况下测试基因-环境相互作用(GxE)的一套新的统计方法。意义：基因很少单独影响健康和心理健康。相反，遗传脆弱性在疾病的病因学中是必要的，但并不总是充分的，因为环境因素经常影响遗传脆弱性的表达。因此，GxE对于所有关于健康的遗传学研究来说都是一个至关重要的课题。最终目标是确定特定的遗传多态和测量的环境之间的相互作用。尽管如此，考虑到每种表型GxE可能涉及的环境种类繁多，推断潜在遗传影响的定量行为遗传学研究可以作为遗传影响的潜在环境修饰者的有效筛选工具，为后续的分子研究提供信息和加强。创新：Purcell(2002)的一篇有影响力的论文提出了一种方法，用于在存在基因测量的环境相关性的情况下测试潜在的遗传影响和测量的环境(GxM)之间的交互作用。我们(Ratuz等人，2008年)最近检查了Purcell方法的统计方面，发现当GxM在某些条件下不存在时，它错误地识别GxM。此外，珀塞尔对基因和环境所解释的方差的分解中的数学错误有时会产生误导性的结论。由于准确识别GxM的及时重要性，必须有可靠的统计程序对其进行测试。我们提出了一类新的此类统计模型，并展示了如何将它们与GxM测试进行比较(Ratuz等人，2008年)。方法：我们申请资金来评估我们使用模拟研究测试GxM的新模型和程序。我们将开发公开可用的统计软件，用于拟合RaTower z等人(2008)提出的在标准结构方程建模软件中不可估量的模型，并将确定在各种条件下足够功率所需的样本量。当GxM被测试时，我们将评估Purcell数学上不正确的方差分解公式的含义。此外，我们建议在GxM的几个测试中评估和说明我们的新模型，这些测试来自两个遗传信息数据集的实际精神病理学数据。所有GxM检验模型(Eaves，2006)的另一个潜在的严重问题是，它们是基于分布假设的全概率结构模型，例如潜在遗传和环境因素的多变量正态分布。因此，了解我们的新GxM模型是否对违反分布假设是稳健的，或者当这些变量的测量尺度本身是否是非正态的时，它们是否产生不正确的结果是极其重要的。因此，我们将进行一系列模拟研究，以检验我们的统计模型在违反分布假设时的性能和稳健性，特别是对于精神病理学研究中典型的固有倾斜数据类型。公共卫生相关性：基因-环境相互作用(GxE)对公共健康非常重要，但在行为遗传学研究中测试GxE的统计方法才刚刚出现。所要求的资金将使我们能够解决现有统计方法中的重要问题，并评估更加灵活和稳健的新方法。

DESCRIPTION (provided by applicant): This is an application to evaluate a new set of statistical methods for testing gene-environment interactions (GxE) in the presence of gene-environment correlation in quantitative behavior genetic studies. Significance: Genes rarely influence health and mental health in isolation. Rather, genetic vulnerabilities are necessary but not always sufficient in the etiology of disorders because environmental factors often influence the expression of genetic vulnerabilities. Therefore, GxE is a critically important topic for all genetic research on health. The ultimate goal is to identify interactions between specific genetic polymorphisms and measured environments. Nonetheless, given the wide variety of possible environments involved in GxE for each phenotype, quantitative behavior genetic studies that infer latent genetic influences can serve as efficient screening tools for potential environmental modifiers of genetic influences, informing and strengthening subsequent molecular studies. Innovation: An influential paper by Purcell (2002) proposed a method for testing interactions between latent genetic influences and measured environments (GxM) in the presence of gene-measured environment correlation. We (Rathouz et al., 2008) recently examined statistical aspects of Purcell's approach and found that it incorrectly identifies GxM when it does not exist under some conditions. In addition, mathematical errors in Purcell's decomposition of the variance explained by genes and environments sometimes yield misleading conclusions. Because of the timely importance of accurately identifying GxM, robust statistical procedures must be available for testing it. We proposed a new class of such statistical models and showed how they can be compared to test for GxM (Rathouz et al., 2008). Approach: We request funding to evaluate our new models and procedures for testing GxM using simulation studies. We will develop publicly-available statistical software for fitting the models proposed in Rathouz et al (2008) that are not estimable in standard structural equation modeling software and we will establish sample sizes needed for adequate power under various conditions. We will evaluate the implications of Purcell's mathematically incorrect variance decomposition formulae when GxM is tested. In addition, we propose to evaluate and illustrate our new models in several tests of GxM in actual psychopathology data from two genetically informative data sets. An additional potentially serious concern with all models for testing GxM (Eaves, 2006) is that they are full probability structural models based on distributional assumptions such as the multivariate normality of latent genetic and environmental factors. Therefore, it is extremely important to know if our new GxM models are robust to violations of distributional assumptions or if they yield incorrect results when the scale of measurement of those variables is inherently non-normal. Therefore, we will conduct a series of simulation studies to examine the performance and robustness of our statistical models when distributional assumptions are violated, especially for the kinds of inherently skewed data that are typical in studies of psychopathology. PUBLIC HEALTH RELEVANCE: Gene-environment interactions (GxE) are profoundly important to the public health, but statistical methods for testing GxE in behavior genetic studies are just emerging. The requested funds will allow us to solve important problems in existing statistical methods and evaluate new methods that are more flexible and robust.