Optimal tests for weak, sparse, and complex signals with application to genetic association studies

适用于遗传关联研究的弱、稀疏和复杂信号的最佳测试

• 批准号: 1309960
• 负责人: Zheyang Wu
• 金额: $ 11万
• 依托单位: Worcester Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-15 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1309960&HistoricalAwards=false
• 关键词: Optimal; tests; weak; sparse; complex

Detection of sparse and weak signals is a key for analyzing big data in many fields. Recent statistical research has made celebrated theoretical progress in revealing the detectability boundaries under the Gaussian means model and an idealized linear regression model. Detectability boundary illustrates the border in the two-dimensional phase space of signal sparsity and weakness, below which the signals are asymptotically too weak and sparse to be detectable by any statistical methods. Certain statistics are optimal for these models in the sense that they reach the boundary (i.e., the least requirements) for reliable signal detection. However, there are significant gaps between these theoretical models and practical meaningful models. In this project, the investigators extend statistical theory to handle weak, sparse, correlated, and interactive signals under the framework of generalized linear models. The investigators develop optimal testing procedures to address the realistic data features in genome-wide association studies and next-generation sequence studies. Statistical theory and methodology development for the detection of weak and sparse signals is foundational for analyzing big data. The goal of this project is to extend statistical theoretical study to address complex signals that are correlated and interactively influential to quantitative or categorical responses. This study is of great interest in data science and is critical to many applications. For example, one perplexing problem of current genetic studies is the missing heritability of complex traits even after many genetic factors have been identified. The proposed work specifically addresses the features of those hidden disease genes yet to be discovered. Unlike some genetic studies based on heuristic arguments, this research combines the power of rigorous statistical theory, first-hand practices in the field, and cutting-edge data from genome-wide association studies and next-generation sequence studies. The proposed project is highly promising in the hunt for the missing heritability. Highly improved gene-detection techniques will help to identify more causative genes of complex human diseases, which will lead to the elucidation of disease pathogenesis and design of targeted therapeutics, thus have a far-reaching impact on improving quality of life.

稀疏微弱信号的检测是许多领域分析大数据的关键。最近的统计研究在揭示高斯均值模型和理想线性回归模型下的可检测性边界方面取得了显著的理论进展。可检测性边界说明了信号稀疏和微弱的二维相空间中的边界，在该边界以下，信号渐近地太弱和稀疏而不能通过任何统计方法检测。某些统计量对于这些模型是最佳的，因为它们到达边界（即，最低要求）进行可靠的信号检测。然而，这些理论模型与实际意义的模型之间存在着很大的差距。在这个项目中，研究人员将统计理论扩展到广义线性模型框架下处理弱，稀疏，相关和交互信号。研究人员开发最佳的测试程序，以解决全基因组关联研究和下一代序列研究中的现实数据特征。用于检测微弱和稀疏信号的统计理论和方法的发展是分析大数据的基础。该项目的目标是扩展统计理论研究，以解决复杂的信号相关和相互影响的定量或分类响应。这项研究在数据科学中具有重要意义，对许多应用至关重要。例如，当前遗传学研究的一个令人困惑的问题是，即使在许多遗传因素已经被确定之后，复杂性状的遗传力仍然缺失。这项拟议中的工作专门针对那些尚未发现的隐藏疾病基因的特征。与一些基于启发式论点的遗传研究不同，这项研究结合了严格的统计理论，该领域的第一手实践以及来自全基因组关联研究和下一代序列研究的前沿数据。该项目在寻找缺失的遗传性方面非常有希望。基因检测技术的高度发展将有助于发现更多的人类复杂疾病的致病基因，从而有助于阐明疾病的发病机制和设计靶向治疗方案，对提高生活质量产生深远的影响。