91自拍 学术报告[2026]026号

(高水平大学建设系列报告1285号)

报告题目：MR2G: A novel framework for causal network inference using GWAS summary data

报告人：薛浩然 助理教授（香港城市大学）

报告时间：2026年4月10日10:30-11:30

报告地点：校友广场307

报告内容：Inferring a causal network among multiple traits is essential for unraveling complex biological relationships and informing interventions. Mendelian randomization (MR) has emerged as a powerful tool for causal inference, utilizing genetic variants as instrumental variables (IVs) to estimate causal effects. However, when the directions of causal relationships among traits are unknown, reconstructing the underlying causal network becomes challenging. In particular, the presence of cycles or feedback loops, which are common in biological systems, poses additional challenges for causal network inference, and remains largely under-studied with standard MR approaches and existing IV-based network inference methods. To address these issues, we introduce MR2G, a new statistical framework that enables robust inference of causal networks, including those with cycles, directly from GWAS summary statistics. MR2G is built on a formally defined recursive causal graph model that rigorously links direct causal effects to MR estimands. It recovers a biologically interpretable causal network from pairwise MR effect estimates, while incorporating a network-informed IV screening strategy to reduce pleiotropic bias and improve robustness. Through realistic simulations, MR2G demonstrates superior accuracy and robustness in recovering complex causal structures, including those involving feedback loops. We apply MR2G to GWAS summary statistics for six complex diseases and nine cardiometabolic risk factors. MR2G not only recovers well-established causal pathways but also uncovers multiple feedback relationships, highlighting its utility in disentangling complex and biologically plausible causal networks from large-scale genetic data.

报告人简历： 薛浩然，现为香港城市大学生物统计系助理教授。在此之前，他曾在明尼苏达大学获得博士学位并进行博士后研究工作，并于中国科学技术大学获得本科学位。薛浩然的研究方向主要包括因果推断，统计学习方法及理论，统计遗传学，他的研究成果广泛发表在知名统计学以及遗传学期刊。

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91自拍

2026年4月2日