Fast and Robust Methods for Large Scale Genotype Phenotype Association Study

 描述(申请人提供)：生命科学中的一个基本挑战是表型差异背后的遗传因素的表征。由于先进的测序技术，大量的基因变异已经被识别和编目。这些数据具有很大的潜力来理解基因如何影响表型，并有助于对环境刺激的敏感性。然而，现有的分析和解释高通量基因数据的计算方法还处于初级阶段。 我们建议系统地研究在建模和发现复杂表型的遗传基础方面的计算和统计原理。这项拟议的研究为基因关联研究中的以下基本问题提供了答案：(1)如何有效和高效地评估结果的统计意义？(2)如何解释 在遗传关联研究中样本之间的关联性？(3)如何准确捕捉多个遗传因素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高效算法，以实现准确和 稳健的显着性评估，考虑了当地的种群结构和多种遗传因素的共同作用。 拟议的计算工具将纳入广泛科学界采用的共同应用框架下的软件包。

 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.