An improved Graph-MRcML algorithm for causal network inference with Mendelian randomization

Music City Center 

Understanding causal networks among multiple traits is crucial 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 causal relationships among traits are unknown, reconstructing the underlying causal network remains a significant challenge. The recently proposed Graph-MRcML method addresses this by estimating pairwise causal effects using a robust bidirectional MR approach and applying network deconvolution to infer direct causal relationships. While empirically effective, certain theoretical limitations remain in its formulation.
In this study, we first clarify the underlying model with cycles and the relationship between the effects estimated by MR and the causal network. Then we introduce an improved version of Graph-MRcML, incorporating a more rigorous IV screening procedure to enhance the recovery of causal networks. Through extensive simulations, we demonstrate that the new method achieves higher accuracy and exhibits improved statistical properties. We further validate its practical utility by applying it to a dataset of 15 traits, showcasing its effectiveness in real-world applications.

Causal network, directed cyclic graph, direct causal effect, horizontal pleiotropy, total causal effect