A0331
Title: MR2G: A novel framework for causal network inference using GWAS summary data
Authors: Haoran Xue - City University of Hong Kong (Hong Kong) [presenting]
Abstract: 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 poses additional challenges for causal network inference. 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. 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.
