Statistical and computational analysis in whole genome sequencing studies.

描述（由申请人提供）：本项目将研究基于全基因组测序（WGS）的基因组学研究中产生的几个统计和计算分析问题。在WGS研究的数据管理领域，我们解决了与序列读取的大量文件的传输和存储相关的快速增加的成本及其相关的质量分数。我们将开发数据压缩方法，以实现比当前标准多几倍的进一步压缩，同时最小化产生的错误。在二次分析方面，我们将开发新的统计学习方法，以改进变体质量分数重新校准并过滤掉不可靠的呼叫。这将提高WGS数据提供的关键信息的可靠性，这些信息是指示基因组与参考基因组不同的位置和差异性质的变体调用。我们将研究基于WGS的病例对照研究方法。特别是，我们将开发统计模型来集成来自多种变体类型的信息

DESCRIPTION (provided by applicant): This project will investigate several issues arising from the statistical and computational analysis of whole genome sequencing (WGS) based genomics studies. In the area of data management in WGS studies, we address the rapidly increasing cost associated with the transfer and storage of the massive files for the sequence reads and their associated quality scores. We will develop data compression methods to achieve a further compression of several folds beyond current standards, with minimal incurred errors. In the area of secondary analysis, we will develop new statistical learning methods to improve variant quality score recalibration and to filter out unreliable calls. This will improve te reliability of the key information provided by the WGS data, which are the variants calls indicating the locations where the genome differs from the reference and the nature of the differences. We will study methods for case-control studies based on WGS. In particular, we will develop statistical models to enable the integrating of information from multiple types of variants to obtain more powerful tests of association. We will apply the methods developed in this aim to the analysis of WGS data from a study on abdominal aortic aneurysm. Finally, we will address selected new questions associated with population scale WGS projects. Several national programs have recently been initiated to generate WGS data for hundreds of thousands of individuals with longitudinal medical records. The availability of this comprehensive data on a population scale will open up a rich frontier for genome medicine and will pose many new challenges for statistical analysis. We will formulate some of these new challenges and develop the statistical methods needed to meet these challenges.