Statistical and computational analysis in whole genome sequencing studies.

全基因组测序研究中的统计和计算分析。

• 批准号: 8750827
• 负责人: Wing H. WONG
• 金额: $ 30万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-22 至 2017-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8750827
• 关键词: Abdominal Aortic Aneurysm; Address; Area; Case-Control Studies; Cohort Analysis; Computer Analysis; Computing Methodologies; Data; Data Analyses; Data Compression; Genome; Genomics; Goals; Individual; Location; Machine Learning; Medical Records; Medicine; Methods; Nature; Population; Reading; Research; Statistical Methods; Statistical Models; Testing; Variant; base; case control; computerized data processing; cost; data management; design; frontier; genome sequencing; improved; meetings; population based; programs; public health relevance

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.

描述（由申请人提供）：该项目将研究基于整个基因组测序（WGS）基因组学研究的统计和计算分析引起的几个问题。在WGS研究中的数据管理领域，我们解决了与序列读取及其相关质量分数的大规模文件传输和存储相关的成本迅速增加。我们将开发数据压缩方法，以实现超出当前标准的几个折叠的进一步压缩，并产生最小的错误。在次级分析领域，我们将开发新的统计学习方法，以改善变异质量得分重新校准并过滤不可靠的调用。这将提高WGS数据提供的关键信息的可靠性，这是指示基因组与参考和差异性质不同的位置的变体调用。我们将研究基于WGS的病例对照研究的方法。特别是，我们将开发统计模型，以启用从多种类型的变体中整合信息 获得更强大的关联测试。我们将在此目标中应用开发的方法，分析腹主动脉瘤研究的WGS数据。最后，我们将解决与人口规模WGS项目相关的新问题。最近，已经开始了几项国家计划，以生成数十万具有纵向病历的人的WGS数据。在人群量表上的这些综合数据的可用性将为基因组医学提供丰富的领域，并将对统计分析构成许多新的挑战。我们将提出其中一些新的挑战，并开发满足这些挑战所需的统计方法。