Core--Statistics

该中心的组成部分5涉及开发和应用统计模型和计算方法，用于分析大型和复杂的基因组数据集，包括基因数据。全球目标是开发将这些数据与生理或临床状态和结果联系起来的统计方法。这一基本方法论研究计划将使用心血管计划的数据集作为开发环境和试验台，以及来自癌症和其他研究的其他数据。这些发展被纳入该中心的基因组技术、遗传分析和信息部分，与候选基因分析和SNP发现部分的数据分析相联系，并与教育部分相连接。该项目将产生广泛适用的生物信息学工具，用于基因表达谱、分子表型和筛选，用于大规模关联研究，将大规模SNP基因分型信息与临床结果和条件联系起来，以及用于基因相互作用分析。其他方面涉及以数据库为基础的发展和基因组数据新统计方法的公共领域实施。 表达研究的核心是一个指导性概念，即阐明许多可能相互作用的基因之间的结构和表达模式的变化，这些基因共同与复杂的人类疾病的结局相关，即使当单独考虑时，子集内的许多基因可能表现出少量或适度的表达水平和变化。因此，基因集合及其相互作用的综合效应才是最重要的问题。因此，一个主要的研究重点是开发方法，将高维表达谱与定义的结果联系起来，从而推断相关的模式和相关的基因子集。一个互补的优先事项是开发有效的统计和计算方法，用于分析此类基因亚集和通过其他中心方法确定的其他基因亚集的高通量SNP基因数据的关联，同样以决定因素往往涉及多个基因变异的组合和相互作用的观点为指导。

Component 5 of the Center is concerned with the development and application of statistical models and computational methods for analysis of large and complex genomic data sets, including gene global objective is to develop statistical methods that relate such data to physiological or clinical states and outcomes. This basic methodological research program will use the data sets arising from the cardiovascular program is a development setting and test-bed, as well as others from cancer and other studies. These developments are integrated into the genome technology, genetic analysis and information components of the Center, link into data analysis from the candidate gene analysis and SNP discovery components, and interface with the education component. The project will result in broadly applicable bioinformatics tools for gene expression profiling, molecular phenotyping and screening, for large-scale association studies in relating large-scale SNP genotyping information to clinical outcomes and conditions, and for gene interaction analysis. Additional aspects involve developments in data basing and public domain implementations of novel statistical methodologies for genomic data. At the heart of the expression studies lies the guiding concept that it is the elucidation of structure and changes in expression patterns among possibly many interacting genes that together relate to outcomes in complex human diseases, even through many genes within the subset may exhibit small or modest expression levels and changes when considered in isolation. It is thus the aggregate effect of collections of genes and their interactions that are of fundamental concern. A major research priority is thus the development of methods to relate the high-dimensional expression profiles to defined outcomes and thereby deduce relevant patterns and implicated subset of genes. A complementary priority is then the development of efficient statistical and computational methods for the analysis of association of high-throughput SNP genotype data for such subsets of genes, and for others identified via other Center methods, again guided by the view that determining factors will often involve the combination and interaction of multiple gene variants.