Statistical methods for genetic data

描述（由申请人提供）：在全基因组水平上进行的研究现在在现代生物学和医学中发挥着核心作用。有一个新的统计方法，可以应用于这些研究的实质性需求。拟议研究的总体目标是开发有助于理解基因组数据的统计方法和软件。特别关注的是功能基因组学，其中来自DNA微阵列和大规模基因分型的数据可用于研究大量基因如何工作以完成各种功能角色。将为这些高维数据集开发新的统计方法，尽可能考虑生物知识。遗传学在几乎每一种人类疾病中都起着作用，无论疾病本身是遗传的，还是疾病与基因活性的实质性变化有关。这项工作旨在通过为基因组学研究提供分析工具，促进对人类疾病的分子生物学和遗传基础的理解。 这种竞争性更新的特别焦点是通过（i）通过诺埃尔多变量模型在基因表达测量中借用强度，（ii）利用多种数据类型，如大规模基因分型和基因表达，建立一个框架，从相关性中剖析因果关系，和（iii）重新思考随机化和实验设计，因为它可以在这种高维基因组学设置中使用。从这项工作中，目的是提供方法，使人们能够表征基因表达变异的共同来源的基因之间的变化，以及对基因之间的特定因果关系。公共卫生相关性：基因组中的信息转移到我们细胞中的主要机制是通过基因表达。研究表明，基因表达的变化与许多重要的人类疾病有关。同时测量数千个基因表达的技术现在得到了广泛的应用。该基金将通过提供定量方法来了解基因表达变异如何在大规模上发挥作用，从而帮助了解基因表达在人类疾病中的作用。

DESCRIPTION (provided by applicant): Studies carried out at the genome-wide level now play a central role in modern biology and medicine. There is a substantial need for new statistical methods that can be applied in these studies. The overall goal of the proposed research is to develop statistical methods and software useful in understanding genomic data. The particular focus is in functional genomics, where data from DNA microarrays and large-scale genotyping can be used to study how large numbers of genes work to accomplish various functional roles. New statistical methods for these high-dimensional data sets will be developed, where biological knowledge is taken into account whenever possible. Genetics plays a role in almost every human disease, whether the disease itself is inherited or the disease is associated with a substantial change in the activity of genes. This work is aimed at contributing to the understanding of the molecular biology and genetic basis of human disease by providing analytical tools for genomics studies. The particular focus of this competitive renewal is to develop a broad framework for modeling the inter-dependence of expression levels among genes as manifested in differential expression variation and their regulatory networks, by (i) borrowing strength across the genes' expression measurements through noel multivariate models, (ii) utilizing multiple data types such as large-scale genotyping and gene expression to build a framework for dissecting causation from correlation, and (iii) rethinking randomization and experimental design as it can be utilized in this high-dimensional genomics setting. From this work, the aim is to provide methodology that allows one to characterize gene expression variation in terms of common sources of variation among genes as well as specific causal relationships among pairs of genes. PUBLIC HEALTH RELEVANCE: The primary mechanism by which information in the genome is transferred into our cells is through gene expression. It has been shown that changes in gene expression are associated with many important human diseases. Technologies that measure the expression of thousands of gene simultaneously are now in widespread use. This grant will aid in the understanding of the role of gene expression in human diseases by providing quantitative methods for understanding how expression variation is functioning on a large-scale.