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: 8185739
• 负责人: Fei Zou
• 金额: $ 22.07万
• 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 2015-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8185739
• 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

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. PUBLIC HEALTH RELEVANCE: 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. They will greatly enhance our ability to understand some of today's most common and complex diseases. 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 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 CC line selection for predictive biology, and develops novel statistical methods to address these unique features.

描述（由申请人提供）：