学术报告报告题目：Mendelian Randomization for Causal inference of heritable phenotypic risk factors
报 告人：王静姝助理教授（芝加哥大学）
报告时间：2022年9月30日（星期五）10:00-11:00
报告地点：腾讯会议，会议ID：555-758-6901
会议链接：https://meeting.tencent.com/p/5557586901
邀 请人：何志坚教授
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数学学院
2022年9月28日
报告摘要：Mendelian randomization (MR) is a method of exploiting genetic variation to unbiasedly estimate a causal effect in the presence of unmeasured confounding. In MR, natural genetic variations are used as instrumental variables to perform causal inference on the effect of heritable risk factors. Because of its convenience, MR has been widely used in epidemiology and other related areas of population science. However, the phenomenon that “all genes affect every complex trait” complicates Mendelian Randomization (MR) studies as most genetic variants will then be invalid instruments. In the talk, I’ll discuss a series of developments using a new comprehensive framework that we developed for MR that can deal with pervasive horizontal pleiotropy, weak genetic instruments, and the temporal relationship between the risk factors and disease progression. I’ll also illustrate a few case studies at the end of the talk.
报告人简介：Jingshu Wang is currently an assistant professor in statistics at the University of Chicago. Her main research interest is in developing statistical methods for cutting-edge bio-technologies and genetic problems. She currently works on problems in single-cell omics, Mendelian Randomization and structural variation in the 3D genome. Her research also includes developing general statistical methodology in causal inference and hypotheses testing that arise from new challenges in genetics and public health. More details: https://jingshuw.org/