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

行为遗传设计中测试基因与环境相互作用的问题

基本信息

批准号：
7793607
负责人：
Benjamin B Lahey
金额：
$ 18.91万
依托单位：
UNIVERSITY OF CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-04-01 至 2012-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7793607
关键词：
Accounting Achievement Anxiety Area Attention Behavior Behavioral Genetics Biometry Birth Weight Candidate Disease Gene Cations Child Cognitive Computational algorithm Computer software DSM-IV Data Data Set Development Disease Effect Modifiers (Epidemiology)Environment Environmental Risk Factor Equation Etiology Exploratory/Developmental Grant Funding Genes Genetic Genetic Polymorphism Genetic Predisposition to Disease Genetic Research Genotype Goals Health Influentials Investigation Life Literature Longitudinal Surveys Measurement Measures Mental Health Mental disorders Methodology Methods Modeling Molecular Molecular Genetics Paper Performance Phenotype Play Positioning Attribute Price Probability Procedures Psychopathology Public Health Reading Research Research Design Role Sample Size Sampling Screening procedure Seminal Series Severities Statistical Methods Statistical Models Structural Models Symptoms Tennessee Testing Twin Multiple Birth Twin Studies Work Youth anti social base computerized tools design flexibility gene environment interaction genetic epidemiology genome wide association study inattention innovation interest population based public health relevance simulation statistics tool

项目摘要

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.

描述（由申请人提供）：这是一个评估一套新的统计方法的应用程序，用于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）。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，因为环境因素常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与测量环境之间的相互作用。尽管如此，考虑到每种表型的 GxE 可能涉及的环境多种多样，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调节剂的有效筛选工具，为后续的分子研究提供信息和加强。创新：Purcell (2002) 的一篇颇具影响力的论文提出了一种在存在基因测量环境相关性的情况下测试潜在遗传影响和测量环境 (GxM) 之间相互作用的方法。我们（Rathouz 等人，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，普塞尔对基因和环境解释的方差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来对其进行测试。我们提出了一类新的统计模型，并展示了如何将它们与 GxM 测试进行比较（Rathouz 等，2008）。方法：我们请求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 Rathouz 等人（2008）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，并且我们将建立在各种条件下足够功效所需的样本量。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，我们建议在来自两个遗传信息数据集的实际精神病理学数据中的几次 GxM 测试中评估和说明我们的新模型。所有用于测试 GxM 的模型（Eaves，2006）的另一个潜在严重问题是，它们是基于分布假设（例如潜在遗传和环境因素的多元正态性）的全概率结构模型。因此，了解我们的新 GxM 模型对于违反分布假设是否具有鲁棒性，或者当这些变量的测量尺度本质上是非正态时是否会产生错误的结果，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常见的固有倾斜数据。公共卫生相关性：基因-环境相互作用 (GxE) 对公共卫生极其重要，但在行为遗传学研究中测试 GxE 的统计方法才刚刚出现。所请求的资金将使我们能够解决现有统计方法中的重要问题，并评估更灵活和稳健的新方法。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Comparing Alternative Biometric Models with and without Gene-by-Measured Environment Interaction in Behavior Genetic Designs: Statistical Operating Characteristics.

比较行为遗传设计中具有和不具有基因测量环境相互作用的替代生物识别模型：统计操作特征。

DOI：
10.1007/s10519-015-9710-1
发表时间：
2015
期刊：
Behavior genetics
影响因子：
2.6
作者：
Zheng,Hao;VanHulle,CarolA;Rathouz,PaulJ
通讯作者：
Rathouz,PaulJ

An evolutionary framework for association testing in resequencing studies.

DOI：
10.1371/journal.pgen.1001202
发表时间：
2010-11-11
期刊：
PLoS genetics
影响因子：
4.5
作者：
King CR;Rathouz PJ;Nicolae DL
通讯作者：
Nicolae DL

Is there a general factor of prevalent psychopathology during adulthood?

DOI：
10.1037/a0028355
发表时间：
2012-11
期刊：
JOURNAL OF ABNORMAL PSYCHOLOGY
影响因子：
4.6
作者：
Lahey, Benjamin B.;Applegate, Brooks;Hakes, Jahn K.;Zald, David H.;Hariri, Ahmad R.;Rathouz, Paul J.
通讯作者：
Rathouz, Paul J.

Fitting Procedures for Novel Gene-by-Measured Environment Interaction Models in Behavior Genetic Designs.

行为遗传设计中新的基因测量环境相互作用模型的拟合程序。