Datasets used in the benchmarking study of MR methods

We conducted a benchmarking analysis of 16 summary-level data-based MR methods for causal inference with five real-world genetic datasets, focusing on three key aspects: type I error control, the accuracy of causal effect estimates, replicability, and power. The datasets used in the MR benchmarking study can be downloaded here: "dataset-GWASATLAS-negativecontrol.zip": the GWASATLAS dataset for evaluation of type I error control in confounding scenario (a): Population stratification "dataset-NealeLab-negativecontrol.zip": the Neale Lab dataset for evaluation of type I error control in confounding scenario (a): Population stratification; "dataset-PanUKBB-negativecontrol.zip": the Pan UKBB dataset for evaluation of type I error control in confounding scenario (a): Population stratification; "dataset-Pleiotropy-negativecontrol": the dataset used for evaluation of type I error control in confounding scenario (b): Pleiotropy; "dataset-familylevelconf-negativecontrol.zip": the dataset used for evaluation of type I error control in confounding scenario (c): Family-level confounders; "dataset_ukb-ukb.zip": the dataset used for evaluation of the accuracy of causal effect estimates; "dataset-LDL-CAD_clumped.zip": the dataset used for evaluation of replicability and power; Each of the datasets contains the following files: "Tested Trait pairs": the exposure-outcome trait pairs to be analyzed; "MRdat" refers to the summary statistics after performing IV selection (p-value < 5e-05) and PLINK LD clumping with a clumping window size of 1000kb and an r^2 threshold of 0.001. "bg_paras" are the estimated background parameters "Omega" and "C" which will be used for MR estimation in MR-APSS. Note: The formatted dataset after quality control can be accessible at our GitHub website (https://github.com/YangLabHKUST/MRbenchmarking). The details on quality control of GWAS summary statistics, formatting GWASs, and LD clumping for IV selection can be found on the MR-APSS software tutorial on the MR-APSS website (https://github.com/YangLabHKUST/MR-APSS). R code for running MR methods is also available at https://github.com/YangLabHKUST/MRbenchmarking.