Robust Methods for Complex Trait Mapping with Collaborative Cross

描述（由申请人提供）： 一种新的小鼠资源，协作杂交（CC）将提供有史以来最多样化的小鼠品系，这将更密切地反映人类的遗传变异。可以通过产生亲本CC RI系的F1杂种以模拟人类群体来产生远交重组近交互交（RIX）。CC RIX将大大提高我们了解当今一些最常见和最复杂疾病的能力。关于RIX基因型、表达和复杂表型的综合信息将是有史以来最丰富的。CC项目的成功在很大程度上依赖于良好的实验设计和适当的统计分析，我们在本提案中解决。该提案的最终目标是为从事CC小鼠研究的科学家提供一个统计分析平台，其中包含专门为CC小鼠数据设计的分析工具，从简单的单变量分析到更复杂的多变量和纵向数据分析，以及高度复杂的集成高维数据分析。本项目的具体目标是：1）开发适合于CC RIX样本特殊相关结构的单变量分析工具; 2）将目标1中的分析方法扩展到更复杂的纵向和多变量表型，以及选定的表型; 3）DNA、基因表达和表型之间关系的联合建模，和4）开发用于选择CC RIX系以用于预测生物学和用于精确表型预测的策略。拟议的项目不仅解决了大多数高维全基因组遗传研究所面临的共同分析挑战，而且还确定了CC项目的独特功能，如表型选择，并开发了新的统计方法来解决这些独特的功能。所提出的方法的性能将进行评估广泛的模拟研究与广泛的模拟设置和遗传模型。实现具体目标的软件将在R或C计算环境中开发和实施，供公众分发。

DESCRIPTION (provided by applicant): A new mouse resource, the Collaborative Cross (CC) will provide access to the most diverse mouse strains ever created which will more closely reflect the genetic variation in humans. Outbred recombinant inbred intercrosses (RIX) can be generated by producing F1 hybrids of parental CC RI lines to mimic human populations. CC RIX will greatly enhance our ability to understand some of today's most common and complex diseases. The combined information on genotype, expression, and complex phenotypes of RIX will be among the richest ever compiled. The success of the CC project relies heavily on good experimental designs and appropriate statistical analysis, which we address in this proposal. The ultimate goal of the proposal is to provide scientists working on CC mice with a statistical analysis platform, which contains specially designed analytical tools for CC mouse data, ranging from simple univariate analysis to more complicated multivariate and longitudinal data analysis, and highly complex integrated high-dimensional data analysis. The specific aims of this project are: 1) developing appropriate univariate analysis tools that account for the special relatedness structure of CC RIX samples; 2) extending the analysis methods in Aim 1 to more complicated longitudinal and multivariate phenotypes, and to selected phenotypes; 3) joint modeling of the relationship between DNA, gene expression, and phenotype, and 4) developing strategies for selecting CC RIX lines for predictive biology and for accurate phenotypic prediction. The proposed project not only addresses common analytical challenges faced by most high-dimensional genome-wide genetic studies, but also identifies unique features of CC projects, such as phenotype selection, and develops novel statistical methods to address these unique features. The performance of the proposed methods will be evaluated by extensive simulation studies with a wide range of simulation setups and genetic models. Software to carry out the specific aims will be developed and implemented in R or C computing environments for public distribution.