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.