Causal Mediation Analysis Leveraging Multiple Types of Summary Statistics Data
This work addresses the problem of interpreting GWAS results for researchers in genetics and bioinformatics, offering a practical solution with incremental improvements over existing methods.
The paper tackled the challenge of identifying causal genes from GWAS summary statistics without individual-level data, and demonstrated that their approach accurately redeems causal genes even in the presence of non-causal trails.
Summary statistics of genome-wide association studies (GWAS) teach causal relationship between millions of genetic markers and tens and thousands of phenotypes. However, underlying biological mechanisms are yet to be elucidated. We can achieve necessary interpretation of GWAS in a causal mediation framework, looking to establish a sparse set of mediators between genetic and downstream variables, but there are several challenges. Unlike existing methods rely on strong and unrealistic assumptions, we tackle practical challenges within a principled summary-based causal inference framework. We analyzed the proposed methods in extensive simulations generated from real-world genetic data. We demonstrated only our approach can accurately redeem causal genes, even without knowing actual individual-level data, despite the presence of competing non-causal trails.