Systems Genetics of the HPA

该项目的目标是开发和应用统计方法来分析收集到的数据 来自协作交叉(CC)菌株及其衍生物的多个表型结构域。这些 包括CC的8个方正菌株之间的双列杂交和重组自交系(Rix) 由CC菌株的F1后代组成的群体。使用基因可复制但远超繁殖的Rix 动物将在具有自然水平的动物中实现跨多个表型域的数据集成 杂合性。对这种新的交叉设计的分析将需要新的方法学开发。我们会 实施通用统计软件包R中的分析工具。新方法的执行情况将 使用模拟和对实验数据的应用来评估和验证。集成 对遗传、环境和生理变量的分析将提供对 对整个生物体生物学的压力源。 具体目标1：我们将开发和应用统计方法来分析中国人的遗传图谱 协作性杂交重组近交系及其衍生物。这些分析的目标将是 确定与实验定义的应激源相互作用的遗传因素对均值和 被测表型的协方差。 具体目标2：我们将开发和应用统计方法，对 跨多个表型领域的协作交叉数据。我们将重点介绍图形技术的应用 使用直观的视觉表示来捕捉这些表型之间的交互作用的模型。

The objectives of this project are to develop and apply statistical methods for the analysis of data collected across multiple phenotypic domains from collaborative cross (CC) strains and their derivatives. These include a diallel cross among the eight founder strains of the CC and a recombinant inbred intercross (RIX) population consisting of the F1 progeny of CC strains. The use of genetically reproducible but outbred RIX animals will enable the integration of data across multiple phenotypic domains in animals with natural levels of heterozygosity. Analysis of this novel cross design will require new methodology development. We will implement analysis tools in the general statistical software package R. Performanceof new methodology will be assessed and validated using simulations and in applications to the experimental data. Integrated analysis of genetic, environmental and physiological variables will provide new insights into the role of stressors on whole organism biology. Specific Aim 1: We will develop and apply statistical methods for genetic mapping analysis of the collaborative cross recombinant inbred strains and their derivatives. The objective of these analyses will be to identify genetic factors that interact with experimentally defined stressors in their effects on mean and covariance of measuredphenotypes. Specific Aim 2: We will develop and apply statistical methods to conduct an integrated analysis of collaborative cross data across multiple phenotypic domains. We will focus on the application of graphical models that capture interactions among these phenotypes using intuitive visual representations.