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.

Collider bias correction for multiple covariates in GWAS using robust multivariable Mendelian randomization

“…The correlation parameters in Σ j can be estimated using either null z-scores in GWAS summary data [ 23 ] or LDSC regression. [ 24 ] Throughout this paper, we approximate Σ j using null z-scores. In the context of MVMR-cML, we assume that Σ j is either known or well-estimated using GWAS summary data [ 14 ].…”

“…Hence, the non-null SNPs were independent of the null SNPs. We also drew 3000 independent null SNPs from other chromosomes, whose z-scores were used to estimate the correlations of summary statistics [ 24 ]. These 3000 null SNPs were not presented in simulation results.…”

Reverse network diffusion to remove indirect noise for better inference of gene regulatory networks

“…Furthermore, NSRGRN refines GRNs by integrating topological properties and edge importance measures ( Liu et al 2023 ). Graph-MRcML recovers direct causal network using a graph deconvolution algorithm ( Lin et al 2023 ). Classical methods like partial correlation ( Kim 2015 ) also provide alternative strategies to remove indirect influences, yet they typically rely on a linear assumption, which may not hold in cases of nonlinear variable interactions and are generally limited to low-order interactions.…”