Systems Genetics of the HPA

这个项目的目标是发展和应用统计方法来分析所收集的数据 跨来自协作杂交（CC）菌株及其衍生物的多个表型域。这些 包括CC的八个创始株系之间的双杂交和重组近交互交（RIX）， 由CC株的F1后代组成的群体。使用基因可繁殖但远系繁殖的RIX 动物将能够整合动物中多个表型域的数据， 杂合性这种新的交叉设计的分析将需要新的方法学的发展。我们将 在通用统计软件包R中实现分析工具。新方法的实施将 使用模拟和应用于实验数据进行评估和验证。集成 对遗传、环境和生理变量的分析将为了解 压力源对整个生物体的影响。 具体目标1：我们将开发和应用统计方法进行遗传图谱分析， 协同杂交重组近交系及其衍生物。这些分析的目的是 确定与实验定义的压力源相互作用的遗传因素， 测量表型的协方差 具体目标2：我们将开发和应用统计方法，对以下方面进行综合分析： 跨多个表型域的协作交叉数据。我们将重点介绍图形化的应用 这些模型使用直观的视觉表示来捕捉这些表型之间的相互作用。

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.