Fast and Robust Methods for Large Scale Genotype Phenotype Association Study

大规模基因型表型关联研究的快速稳健方法

• 批准号: 9272965
• 负责人: Wei Wang
• 金额: $ 8.54万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9272965
• 关键词: Accounting; Address; Adopted; Affect; Algorithms; Area; Bioinformatics; Biological Sciences; Cataloging; Catalogs; Communities; Complex; Computational Biology; Computer Simulation; Computer software; Computing Methodologies; Data; Development; Diagnostic; Foundations; Gene Expression; Genes; Genetic; Genetic Marker Expression; Genetic Markers; Genotype; Health; Information Networks; Joints; Machine Learning; Maps; Measures; Methods; Mining; Modeling; Mus; Noise; Performance; Phenotype; Phylogeny; Pilot Projects; Population; Predisposition; Property; Publications; Quantitative Trait Loci; Research; Research Institute; Sampling; Scientist; Speed; Stimulus; Structure; Technology; Testing; Trees; Variant; base; computerized tools; data mining; experience; genetic association; genetic variant; human disease; improved; infancy; innovation; interest; novel; tool; trait

 DESCRIPTION (provided by applicant): 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. We propose 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, we will develop data structures and efficient algorithms for accurate and robust significance assessment that account for local population structure and joint effect of multiple genetic factors. The proposed computational tools will be integrated into software packages under common application framework adopted by the broad scientific community.

 描述（由申请人提供）：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。 我们建议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)如何解释 遗传关联研究中样本之间的相关性？(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，我们将开发数据结构和有效的算法， 鲁棒显著性评估，其考虑了局部群体结构和多个遗传因子的联合效应。 拟议的计算工具将被整合到软件包中，并在广泛的科学界采用的通用应用框架下使用。