Abstract: Large genetic datasets from diverse human ancestries offer unprecedented challenges and opportunities for genetic and genomic research. They not only improve the power to identify novel genotype x phenotype associations but also could lead to novel biological and clinical insights. The largest genetic datasets are often available as summary association statistics (e.g., genetic effect estimates, standard deviation, etc), which can be analyzed in meta-analysis. Genetic effects can differ between ancestries which requires improved meta-analysis models to accommodate this heterogeneity. In this talk, we will describe approaches to integrate multi-ancestry genetic datasets of millions of individuals to detect association, assess replicability of association signals, and gain mechanistic insights on tobacco and alcohol use phenotypes and on systemic lupus erythematosus. These approaches are general and will be applicable to study other complex human diseases.
Bio: Dr. Dajiang Liu joined Penn State in 2014 as an Assistant Professor and got promoted to Associate Professor in 2018. He is Professor and Vice Chair for Research in the Department of Public Health Sciences since 2022. He serves as the Director of Artificial Intelligence and Biomedical Informatics and co-leads the Penn State College of Medicine Strategic Plan. He is also the Co-Director of the Bioinformatics and Genomics Graduate Program. Dr. Liu supervises trainees from the Bioinformatics and Genomics, Biostatistics, Biomedical Science, Epidemiology, Neuroscience PhD and MD/PhD program. At Penn State, Dr. Liu’s research, mentoring, and service activities were recognized by a Distinguished Early Stage Investigator Award, an Outstanding PostDoc Mentor Award, an Excellence in Career Mentoring Award, and a Champion of Diversity in Research Award.