Novel Methods to Jointly Analyze Pleiotropic Genetic Effects on Multiple Traits Integrating Epigenomics and Genomics Data
Principal Investigator: Dr Jin Liu
Approved Research ID: 30186
Approval date: June 1st 2017
A rigorous statistical method will be developed to modulate the relationships among multiple GWAS or GWAS with multiple traits, and a scalable algorithm will be developed to handle tons of data from UK biobank. The proposed method will have significant clinical and public health impact. Investigation of the findings in this study may contribute to the elucidation of biological pathways and novel biomarkers underlying complex traits/diseases which will help individuals' prevention, diagnosis and treatment of genetic diseases and thus improve population health of our society. Genetic studies have been developed rapidly over past twenty years, including genome-wide association studies (GWAS). UK biobank data provides us a chance to better understand the mechanism behind diseases with a very large sample size. In this research, we aim at developing statistical models to jointly analyze multiple GWAS or GWAS with multiple traits. The computationally efficient algorithms will be developed to handle multiple GWAS. In this study, we plan to use the full cohort in UK Biobank.