The Omnicausal Model Reveals the Highly Polyfactorial Nature of Complex Diseases

Abstract

Mendelian randomization (MR) is a human genetics method for inferring causal relationships between risk factors and diseases. A common focus of MR studies has been on the causal inference of a single risk factor on a single disease. This has led to the successful discovery of numerous causal risk factors for disease. However, it remains unclear how much each causal risk factor contributes to disease collectively. Here, we introduce the concept of “causality explained,” that provides an estimator of the causal variance explained by a phenome-wide set of risk factors on complex diseases to assess how much causality can be potentially explained. The model is based on principal component regression which is a multivariate linear regression based on principal component analysis. In complement, we propose the “polyfactorial index” to assess the trajectory of causality explained as risk factors are sequentially added into the model, to characterize the causal architecture for a complex disease. We demonstrate that our model correctly assesses the causality explained and causal architecture in simulations across a wide range of parameters. To build our model, we used a phenome-wide set of 222 traits from the UK Biobank compared to a set of 5 known risk factors for coronary artery disease. We observed that the phenome-wide set explains more than 45% of causality compared to 28.76% for the set of known risk factors. In addition, we tested our approach on 13 complex diseases and showed that the phenome-wide set can explain between 27% for anorexia to 80% for schizophrenia, with ever increasing trajectories of causality explained. We propose the “omnicausal model,” which posits that a large number of risk factors individually explains a small amount to disease but collectively explain most of the causal variance. We distinguished core and peripheral causal factors that explain respectively a larger and a smaller part of causal variance. This approach provides insights into the underlying causal architecture between risk factors and disease.