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

描述（由申请人提供）：这是一份申请，用于评估定量行为遗传研究中存在基因-环境相关性的基因-环境相互作用（GxE）测试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是所有健康基因研究的一个至关重要的课题。最终目标是确定特定遗传多态性和测量环境之间的相互作用。尽管如此，考虑到每种表型的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有效的筛选工具，为遗传影响的潜在环境修饰因子提供信息并加强后续的分子研究。创新：Purcell（2002）的一篇有影响力的论文提出了一种方法，在存在基因-测量环境相关性的情况下，测试潜在遗传影响与测量环境（GxM）之间的相互作用。我们（Rathouz et al., 2008）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，它会错误地识别GxM。此外，Purcell对由基因和环境解释的差异进行分解时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来测试它。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（Rathouz et al., 2008）。方法：我们请求资金来评估我们的新模型和程序，以使用模拟研究来测试GxM。我们将开发公开可用的统计软件来拟合Rathouz等人（2008）中提出的模型，这些模型在标准结构方程建模软件中是不可估计的，我们将建立在各种条件下足够功率所需的样本量。当测试GxM时，我们将评估Purcell在数学上不正确的方差分解公式的含义。此外，我们建议在两个遗传信息性数据集的实际精神病理学数据中对GxM的几个测试进行评估和说明我们的新模型。所有用于测试GxM的模型（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性。因此，了解我们的新GxM模型是否对违反分布假设具有鲁棒性，或者当这些变量的测量尺度本质上是非正态的时，它们是否产生不正确的结果，这一点非常重要。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扭曲数据。公共卫生相关性：基因-环境相互作用（GxE）对公共卫生非常重要，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，并评估更加灵活和可靠的新方法。

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.