MR-Corr2: a two-sample Mendelian randomization method that accounts for correlated horizontal pleiotropy using correlated instrumental variants

Abstract

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 (GWASs), 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 (γ) and horizontal pleiotropy (α) 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-Corr2, that uses the orthogonal projection to reparameterize the bivariate normal distribution for γ and α, 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-Corr2 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-Corr2 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. Availability The developed R package and code to reproduce all the results are available at https://github.com/QingCheng0218/MR.Corr2. Supplementary information Supplementary data are available at Bioinformatics online.

Mendelian randomization accounting for complex correlated horizontal pleiotropy while elucidating shared genetic etiology

“…We compared MR-CUE with nine other methods, including CAUSE 13 , GRAPPLE 15 ,cML-MA 14 , RAPS 6 , IVW 18 , MR-Egger 5 , MRMix 12 , MR-Clust 10 , MR-LDP 8 . In existing literature, other methods including BESIDE-MR 19 , JAM-MR 20 , Berzuini’s method 21 , and MR-Corr 2 22 have also been proposed to account for either UHP or CHP. For cML-MA, we evaluated its performance using its default setting, cML-MA-BIC-DP.…”

Mendelian randomization analysis of 37 clinical factors and coronary artery disease in East Asian and European populations

“…We performed Cochran’s Q test to examine heterogeneity between populations. Four UVMR methods were applied: the MR-Corr method [ 37 ], the IVW method [ 38 ], the Bayesian weighted Mendelian randomization (BWMR) method [ 39 ], and the robust adjusted profile score (RAPS) method [ 40 ]. MR-Corr is designed to address the correlated horizontal pleiotropy issue [ 37 ].…”

“…We performed Steiger’s directionality test based on all IVs to confirm the bi-directional causal relationships between hemoglobin (Hb) and CAD [ 41 ]. MR analyses were conducted using the MR.Corr2 [ 37 ], TwoSampleMR [ 42 ], BWMR [ 39 ], and mr.raps [ 40 ] R packages.…”

MRBEE: A novel bias-corrected multivariable Mendelian Randomization method

“…which, in the absence of UHP and CHP, is asymptotically equivalent to maximizing a likelihood function that accounts for measurement error in the GWAS estimates (as in, e.g., MR-CUE [16]). The terms σ W β j wα j and Σ W β j W β j are estimated using methods in [18,50] (also used by MR-Corr [19], MR-CUE [16],…”

“…UHP bias is addressed by methods such as MRPRESSO/IMRP [20, 22], MR-Median [23], MR-Robust [24], and MR-Mix [15]. Methods designed specifically to additionally address CHP bias include CAUSE [7], MR-CUE [16], and MR-Corr [19], which each assume the IV set is a mixture of CHP and otherwise valid IVs. CHP can be considered to result from the omission of additional exposures in MR, but these methods cannot accommodate multiple exposures and so must make complex statistical adjustments based on additional assumptions that may not be satisfied in practice.…”