First speaker: Quan Sun
Title: Recent Developments of Statistical Genetics in the Era of Genome-Wide Association Studies (GWAS)
Abstract: Genome-wide association studies (GWAS) have been widely used in genetic studies to discover genetic variants that are associated with complex diseases or traits. The standard practice of GWAS usually starts with genotyping, followed by genotype imputation, association tests and downstream functional follow-up analysis. In this talk, I’ll give a brief overview of some recent projects in our lab related to the last three aspects of GWAS and post-GWAS analysis, including both methodological and applied projects. Specifically, I’ll mainly talk about a recently published method titled “MagicalRsq: Machine-learning-based genotype imputation quality calibration” where we proposed MagicalRsq, a machine-learning-based method that integrates variant-level imputation and population genetics statistics, to provide a better calibrated imputation quality metric. Our new metric could improve the squared Pearson correlation with truth by 8% – 92% and save 105 – 106 genetic variants compared to the traditional quality estimate.
Second speaker: Weifang Liu
Title: Statistical Methods for Studying the 3D Genome from Chromosome Conformation Capture Data
Abstract: The human genome has a complex and dynamic three-dimensional (3D) organization, which plays a critical role in gene regulation and genome function. Advanced genomics technologies together with powerful computational methods have enabled comprehensive characterization of regulatory DNA interactions and substantially improved our understanding of the 3D genome architecture. This talk will start with an introduction to chromatin organization features and relevant technologies, followed by an overview of a hidden Markov model-based method to identify chromatin interactions. Some examples will be shown to illustrate the critical capability of 3D genome organization in linking GWAS variants to their target genes and prioritizing relevant tissues or cell types.