Statistical methods for high-throughput genomics

高通量基因组学的统计方法

• 批准号: 435666-2013
• 负责人: Liang, Kun
• 金额: $ 1.17万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=657573
• 关键词: Statistical; methods; throughput; genomics

High-throughput genomics technologies (e.g. microarray and sequencing technology) enable genome-wide evaluation of genes expression and their regulatory mechanism. Such technologies have revolutionized the fields of biology and medicine in recent years, but there remains an urgent need for the development of efficient and rigorous statistical methods to analyze the resultant large and complex datasets. As the new sequencing data are inherently discrete, we first develop powerful multiple testing methods that are suitable for discrete data. A second problem arises when multiple sets of genes are tested for differential expression across conditions, which is a common practice in high-throughput genomic data analysis. Because gene sets can share genes, the test results should satisfy certain logical constraints. We will develop computationally efficient statistical methods for multiple testing of these logically constrained hypotheses. As sequencing technology emerges as the primary tool for investigating genome-wide regulation events, it becomes increasingly important to develop statistical methodology for error rate estimation and control for genomic event detection. Finally, as more and more high-throughput genomic data accumulate, we will develop methods for joint analysis of multiple datasets to improve the sensitivity and specificity of genomic discoveries.****The proposed research will have a significant impact on the analysis of high-throughput genomic data that leads to more powerful and better quality genomic regulatory element detection and more reproducible scientific discoveries. The applications of our research results can advance understanding of the genetic basis of many human diseases including cancer, rheumatic diseases, and developmental disorders. Furthermore, the statistical theory and methodology to be developed have an important impact on a wide range of scientific applications including statistical genetics (quantitative trait locus (QTL), linkage disequilibrium, proteomics, etc.) and engineering.**********

高通量基因组学技术(如微阵列和测序技术)能够在全基因组范围内评估基因表达及其调控机制。近年来，这类技术给生物学和医学领域带来了革命性的变化，但仍然迫切需要开发高效和严格的统计方法来分析由此产生的庞大而复杂的数据集。由于新的测序数据本质上是离散的，我们首先开发了适用于离散数据的强大的多重测试方法。第二个问题是，当测试多组基因在不同条件下的差异表达时，这是高通量基因组数据分析中的常见做法。因为基因集合可以共享基因，所以测试结果应该满足一定的逻辑约束。我们将开发计算高效的统计方法，对这些逻辑约束假设进行多次测试。随着测序技术成为研究全基因组调控事件的主要工具，开发用于基因组事件检测的错误率估计和控制的统计方法变得越来越重要。最后，随着越来越多的高通量基因组数据的积累，我们将开发多个数据集的联合分析方法，以提高基因组发现的敏感性和特异性。*所提出的研究将对高通量基因组数据的分析产生重大影响，从而导致更强大、更高质量的基因组调控元件检测和更具重复性的科学发现。我们的研究成果的应用可以促进对许多人类疾病的遗传基础的理解，包括癌症、风湿性疾病和发育障碍。此外，即将开发的统计理论和方法对包括统计遗传学(数量性状基因座(QTL)、连锁不平衡、蛋白质组学等)在内的广泛科学应用具有重要影响。和工程。*