III: Medium: Collaborative Research: Toward Robust and Scalable Discovering of Significant Associations in Massive Genetic Data

III：媒介：合作研究：在海量遗传数据中稳健且可扩展地发现显着关联

• 批准号: 1162369
• 负责人: Wei Wang
• 金额: $ 49.96万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1162369&HistoricalAwards=false
• 关键词: III; Medium; Collaborative; Research; Toward

A fundamental challenge in life sciences is the characterization of genetic factors that underlie phenotypic differences. Thanks to the advanced sequencing technologies, an enormous amount of genetic variants have been identified and cataloged. Such data hold great potential to understand how genes affect phenotypes and contribute to the susceptibility to environmental stimulus. However, the existing computational methods for analyzing and interpreting the high-throughput genetic data are still in their infancy. The objective of this project is to systematically investigate the computational and statistical principles in modeling and discovering genetic basis of complex phenotypes. The proposed research provides answers to the following fundamental questions in genetic association study: (1) How to effectively and efficiently assess statistical significance of the findings? (2) How to account for the relatedness between samples in genetic association study? (3) How to accurately capture possible interactions between multiple genetic factors and their joint contribution to phenotypic variation? In particular, the team will develop a multi-layer indexing structure for robust and scalable multiple testing correction, a general phylogenetic tree based framework to account for local population structure, and an ensemble learning approach for studying joint effect of multiple genetic factors.The research provides a computational framework for large scale genotype-phenotype association study. The outcome includes novel methods for addressing sample relatedness, capturing confounding factors, and controlling multiple testing errors which are widely applicable for many common data mining tasks including frequent pattern mining, multitask learning, and ensemble learning among others. Collectively, the theoretic framework and algorithms will provide the research community much better tools to dissect complex relationships between genotypes and phenotypes, and gain deeper understanding of the roles of environmental stimuli.The proposed research directly involves applications in large scale genome-wide association study. Additional applications exist for biologists in their study of gene-gene interactions, metabolic pathways and protein-protein interaction networks. Beyond the applications proposed here, the algorithms can find wide applications in other areas of biology as well as other scientific disciplines. The methods will be evaluated thoroughly by both simulation and real data collected from yeast, mouse, and human. Early versions of the applications will be made available to the biological community through a web-based server to evaluate efficacy of the methods and to apply them to a broader set of problems. The research findings and methods will be integrated into graduate and undergraduate instruction. The team already offer classes in computational biology and data-mining where the proposed tools will aid students in comprehending abstract concepts and data relations. They will also continue their commitment to supporting multidisciplinary educational experiences, and service to the research community, as well and proving research opportunities for undergraduate students.

生命科学的一个基本挑战是表征表型差异背后的遗传因素。由于先进的测序技术，大量的遗传变异已经被识别和编目。这些数据对于理解基因如何影响表型以及如何影响对环境刺激的敏感性具有巨大的潜力。然而，现有的用于分析和解释高通量遗传数据的计算方法仍处于起步阶段。本项目的目标是系统地研究复杂表型的建模和发现遗传基础的计算和统计原理。本研究回答了遗传关联研究中的以下基本问题：（1）如何有效地评估研究结果的统计学意义？(2)遗传关联研究中样本间的相关性如何解释？(3)如何准确捕捉多个遗传因子之间可能的相互作用及其对表型变异的共同贡献？特别是，该团队将开发一个多层索引结构，用于强大和可扩展的多重检验校正，一个通用的基于系统发育树的框架来解释当地种群结构，以及一个集成学习方法来研究多个遗传因素的联合作用。该研究为大规模基因型-表型关联研究提供了一个计算框架。结果包括解决样本相关性，捕获混杂因素和控制多个测试错误的新方法，这些方法广泛适用于许多常见的数据挖掘任务，包括频繁模式挖掘，多任务学习和集成学习等。这些理论框架和算法将为研究群体提供更好的工具来剖析基因型和表型之间的复杂关系，并加深对环境刺激作用的理解，直接应用于大规模全基因组关联研究。生物学家在基因-基因相互作用、代谢途径和蛋白质-蛋白质相互作用网络的研究中还有其他应用。除了这里提出的应用之外，这些算法还可以在生物学的其他领域以及其他科学学科中找到广泛的应用。这些方法将通过模拟和从酵母、小鼠和人类收集的真实的数据进行彻底评估。这些应用程序的早期版本将通过一个网络服务器提供给生物界，以评估这些方法的效力，并将其应用于更广泛的问题。 研究结果和方法将被整合到研究生和本科教学。该团队已经提供计算生物学和数据挖掘课程，其中提出的工具将帮助学生理解抽象概念和数据关系。他们还将继续致力于支持多学科的教育经验，并为研究界服务，以及为本科生提供研究机会。