MR-Corr(2): a two-sample Mendelian randomization method that accounts for correlated horizontal pleiotropy using correlated instrumental variants

Motivation: Mendelian randomization (MR) is a valuable tool to examine the causal relationships between health risk factors and outcomes from observational studies. Along with the proliferation of genome-wide association studies, a variety of two-sample MR methods for summary data have been developed to account for horizontal pleiotropy (HP), primarily based on the assumption that the effects of variants on exposure (gamma) and HP (alpha) are independent. In practice, this assumption is too strict and can be easily violated because of the correlated HP. Results: To account for this correlated HP, we propose a Bayesian approach, MR-Corr(2), that uses the orthogonal projection to reparameterize the bivariate normal distribution for gamma and alpha, and a spike-slab prior to mitigate the impact of correlated HP. We have also developed an efficient algorithm with paralleled Gibbs sampling. To demonstrate the advantages of MR-Corr(2) over existing methods, we conducted comprehensive simulation studies to compare for both type-I error control and point estimates in various scenarios. By applying MR-Corr(2) to study the relationships between exposure-outcome pairs in complex traits, we did not identify the contradictory causal relationship between HDL-c and CAD. Moreover, the results provide a new perspective of the causal network among complex traits.