Title: Causal Mediation Analysis Leveraging Multiple Types of Summary Statistics Data,
Abstract: 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.