Statistical Methods for the Genomic Analysis of Gene Expression Data

描述（由申请人提供）：随着DNA测序、基因分型、基因注释、表达谱和其他高通量测量技术的进步，可行的科学问题的范围正在扩大，并且通常涉及从许多来源收集的数据。因此，从事统计学和生物学工作的统计学家面临着为涉及多种类型数据的调查开发分析方法的挑战。一个重要的例子涉及研究，以确定基因表达的遗传基础（eQTL作图研究），其中相关的数据包括表达测量，遗传图谱，信息的共调节的成绩单，和转录因子及其结合位点。另一组研究涉及生理系统的遗传调控。这些数据包括时程表达测量，生理变量，和信息的转录共调节。表达QTL定位和时程微阵列研究提供了对正常和患病生物系统的基本见解。它们被用来识别调节基因，深入了解调节机制，并确定可以修改调节以促进或维持有益反应的方法。如果没有有效的统计方法，这些研究中的生物医学信息的完整程度就无法实现。本提案的目标是开发、评估和传播统计方法，以解决这两类研究中的问题。这一目标将通过3个具体目标来实现：开发识别映射转录本及其映射的基因组位置的方法;将这些方法扩展到稀疏图和密集图的情况;以及开发识别表达和影响协变量的时间路径的方法。成功完成拟议的研究将大大改善这两个重要类别的基因组研究的统计方法。

DESCRIPTION (provided by applicant): With advances in DNA sequencing, genotyping, gene annotation,expression profiling, and other high throughput measurement technologies, viable scientific questions are widening in scope and often involve data collected from numerous sources. Statisticians working at the interface of statistics and biology are consequently faced with the challenge of developing analytic methods for investigations involving numerous types of data. 1 important example concerns studies to identify the genetic basis of gene expression (eQTL mapping studies), where relevant data consists of expression measurements, genetic maps, information on co-regulation of transcripts, and on transcription factors and their binding sites. Another collection of studies addresses genetic regulation of physiological systems. The data includes time course expression measurements, physiological variables, and information on co-regulation of transcripts. Expression QTL mapping and time course microarray studies provide fundamental insight into both normal and diseased biological systems. They are being used to identify regulatory genes, to gain insight into the mechanisms of regulation, and to determine ways in which regulation can be modified to promote or maintain a beneficial response. The full extent of biomedical information in these studies cannot be realized without effective statistical methods. The goal of this proposal is to develop, evaluate, and disseminate statistical methods to address questions from these 2 types of studies. This goal will be accomplished through 3 specific aims: the development of methods to identify mapping transcripts and the genomic locations to which they map; the extension of these methods to the cases of both sparse maps and dense maps; and the development of methods to identify temporal paths of expression and affecting covariates. Successful completion of the proposed research will result in substantially improved statistical methods for these 2 important categories of genomic studies.