Methods to Study the Genetic Architecture and Clinical Consequence of Complex Traits Using Sequence Data from Large Datasets

摘 要：
Next generation sequencing offers unprecedented opportunities to advance our understanding on the genetic architecture and clinical consequence of complex traits. Many large scale genetic studies are being implemented, which aggregates hundreds of thousands or even millions of samples. These large datasets pose great challenges in developing efficient methods for these datasets. In this talk, we first discuss a few methods to enable efficient association analysis that can scale well with biobank scale datasets. Next, we discuss a method to robustly infer causal relations between biomarkers and diseases, and illustrate the application of these methods to understand the causal impact and pharmacogenetics properties of lipid levels from an exome-wide study with 300,000 samples.
个人简介：
Dajiang Liu is an assistant professor from Penn State University College of Medicine. His research focuses on statistical genetics, the genetics of addiction and the functional biology of X chromosome inactivation. He has developed many methods for sequence-based association analysis. His methods and tools have been broadly applied in numerous large scale high profile studies for lipid levels, smoking addictions, diabetes etc. Dr. Liu currently co-leads the GWAS and Sequencing Consortia of Alcohol and Nicotine Addiction. He is the principal investigator on multiple NIH grants, including an R01 grant on statistical genetic methods development for sequence data and an R21 grant on the genetics of nicotine addiction. He is very interested in working with colleagues, students and postdocs in these directions.