Robust Methods for Complex Trait Mapping with Collaborative Cross

通过协作交叉进行复杂性状映射的稳健方法

• 批准号: 8711483
• 负责人: Fei Zou
• 金额: $ 22.27万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-04-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8711483
• 关键词: Accounting; Address; Age; Alleles; Biologic Characteristic; Biology; Cluster Analysis; Complex; Computer software; DNA; Data; Data Analyses; Dimensions; Disease; Dose; Environment; Experimental Designs; Gene Expression; Genetic; Genetic Models; Genetic Variation; Genotype; Goals; Human; Hybrids; Inbreeding; Joints; Least-Squares Analysis; Maps; Measurement; Measures; Methods; Modeling; Mouse Strains; Mus; Parents; Performance; Phenotype; Population; Population Genetics; Predisposition; Quantitative Trait Loci; Recombinants; Research Personnel; Resources; Sampling; Scientist; Selection Criteria; Statistical Methods; Structure; Testing; Time; Time Study; Work; analytical method; analytical tool; base; data mining; data reduction; design; genome wide association study; genome-wide; improved; novel; research study; response; simulation; success; tool; trait; Accounting; Address; Age; Alleles; Biologic Characteristic; Biology; Cluster Analysis; Complex; Computer software; DNA; Data; Data Analyses; Dimensions; Disease; Dose; Environment; Experimental Designs; Gene Expression; Genetic; Genetic Models; Genetic Variation; Genotype; Goals; Human; Hybrids; Inbreeding; Joints; Least-Squares Analysis; Maps; Measurement; Measures; Methods; Modeling; Mouse Strains; Mus; Parents; Performance; Phenotype; Population; Population Genetics; Predisposition; Quantitative Trait Loci; Recombinants; Research Personnel; Resources; Sampling; Scientist; Selection Criteria; Statistical Methods; Structure; Testing; Time; Time Study; Work; analytical method; analytical tool; base; data mining; data reduction; design; genome wide association study; genome-wide; improved; novel; research study; response; simulation; success; tool; trait

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.

描述（由申请人提供）： 协作杂交（CC）是一种新的鼠标资源，将提供对有史以来最多样化的小鼠菌株的访问，这将更紧密地反映人类的遗传变异。可以通过生成父母CC RI系的F1杂种来模仿人类种群来产生近交性重组近交近交联（RIX）。 CC RIX将大大增强我们了解当今最常见和最复杂的疾病的能力。 RIX的基因型，表达和复杂表型的组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取决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了这一问题。该提案的最终目的是为在CC小鼠上工作的科学家提供统计分析平台，该平台包含用于CC鼠标数据的专门设计的分析工具，范围从简单的单变量分析到更复杂的多变量和纵向数据分析，以及高度复杂的复杂集成的高维数据分析。该项目的具体目的是：1）开发适当的单变量分析工具，以说明CC RIX样本的特殊相关性结构； 2）将AIM 1中的分析方法扩展到更复杂的纵向和多元表型，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，以及4）制定为预测生物学和准确表型预测选择CC RIX线的策略。拟议的项目不仅解决了大多数高维基因组遗传研究所面临的常见分析挑战，而且还确定了CC项目的独特特征，例如表型选择，并开发了新的统计方法来解决这些独特的特征。提出的方法的性能将通过广泛的模拟研究来评估，并具有广泛的模拟设置和遗传模型。将在R或C计算环境中开发和实施以进行公共分发的软件。