Combining Mendelian randomization and network deconvolution for inference of causal networks with GWAS summary data

Abstract

Mendelian randomization (MR) has been increasingly applied for causal inference with observational data by using genetic variants as instrumental variables (IVs). However, the current practice of MR has been largely restricted to investigating the total causal effect between two traits, while it would be useful to infer the direct causal effect between any two of many traits (by accounting for indirect or mediating effects through other traits). For this purpose we propose a two-step approach: we first apply an extended MR method to infer (i.e. both estimate and test) a causal network of total effects among multiple traits, then we modify a graph deconvolution algorithm to infer the corresponding network of direct effects. Simulation studies showed much better performance of our proposed method than existing ones. We applied the method to 17 large-scale GWAS summary datasets (with median N = 256879 and median #IVs = 48) to infer the causal networks of both total and direct effects among 11 common cardiometabolic risk factors, 4 cardiometabolic diseases (coronary artery disease, stroke, type 2 diabetes, atrial fibrillation), Alzheimer's disease and asthma, identifying some interesting causal pathways. We also provide an R Shiny app (https://zhaotongl.shinyapps.io/cMLgraph/) for users to explore any subset of the 17 traits of interest.

Fig 1. A causal model for the exposure X and the outcome Y.

Fig 2. Estimated direct causal graphs for 6 traits.

Fig 3. Estimates of the causal effect θ with 0% invalid IVs across 1000 replicates.

From left to right correspond to 0%, 50% and 100% overlapping samples. Top panel: θ = 0 and bottom panel: θ = 0.2.

Fig 4. Empirical type-I error and power in the presence of 30% invalid IVs with correlated pleiotropy.

X-axis represents different proportions of sample overlap (0%, 50% and 100%). Left: θ = 0 (type-I error) and right: θ = 0.2 (power).

Fig 5. The estimated total (A) and direct (B) causal graphs for the 11 risk factors and 6 diseases.

The edges in green represent positive effects and those in red are negative ones. The nodes in blue are diseases and those in orange are risk factors. The dark-solid edges are identified at the Bonferroni-adjusted significance level, while the light-colored ones are marginally significant at a less stringent level of 6.5e-3.