时 间：2025年5月26日9（周一）15：00 – 16：00
地 点：理科大楼A1714室
报告人：葛淑菲上海科技大学数学科学研究所助理教授
主持人：王亚平 华东师范大学教授
摘 要：
Imaging genetics aims to uncover the hidden relationship between imaging quantitative traits (QTs) and genetic markers (e.g. single nucleotide polymorphism (SNP)), and brings valuable insights into the pathogenesis of complex diseases, such as cancers and cognitive disorders. However, most linear models in imaging genetics didn't explicitly model the inner relationship among QTs, which might miss some potential efficiency gains from borrowing information across brain regions. In this work, we developed a novel Bayesian regression framework for identifying significant associations between QTs and genetic markers while explicitly modelling spatial dependency between QTs, with the main contributions as follows. Firstly, we developed a spatial-correlated multitask linear mixed-effects model to model dependencies between QTs. We took full advantage of the dependent structure of brain imaging-derived QTs by introducing a population-level mixed effects term. Secondly, we implemented the model in the Bayesian framework and derived a Markov chain Monte Carlo (MCMC) algorithm to do model inference. Further, we incorporated the MCMC samples with the Cauchy combination test to examine the association between SNPs and QTs, which avoided computationally intractable multi-test issues. The simulation studies indicated improved power of our proposed model compared to classic models where inner dependencies of QTs were not modeled.
报告人简介：
Shufei Ge is an assistant professor at the Institute of Mathematical Sciences, ShanghaiTech University, where she has been a faculty member since Sep. of 2020. Before that, she received her Ph.D. in statistics at Simon Fraser University, Canada. Her research interest involves Bayesian statistics, statistical machine learning methods and computational biology.