Simmrd: An Open-source Tool to Perform Simulations in Mendelian Randomization

Overview

Journal Genet Epidemiol

Specialties Genetics
Public Health

Date 2024 Jan 24

PMID 38263619

Authors

Noah Lorincz-Comi

Yihe Yang

Xiaofeng Zhu

Affiliations

Soon will be listed here.

Abstract

Mendelian randomization (MR) has become a popular tool for inferring causality of risk factors on disease. There are currently over 45 different methods available to perform MR, reflecting this extremely active research area. It would be desirable to have a standard simulation environment to objectively evaluate the existing and future methods. We present simmrd, an open-source software for performing simulations to evaluate the performance of MR methods in a range of scenarios encountered in practice. Researchers can directly modify the simmrd source code so that the research community may arrive at a widely accepted framework for researchers to evaluate the performance of different MR methods.

References

LeBlanc M, Zuber V, Thompson W, Andreassen O, Frigessi A, Andreassen B . A correction for sample overlap in genome-wide association studies in a polygenic pleiotropy-informed framework. BMC Genomics. 2018; 19(1):494. PMC: 6019513. DOI: 10.1186/s12864-018-4859-7. View

Qi G, Chatterjee N . A comprehensive evaluation of methods for Mendelian randomization using realistic simulations and an analysis of 38 biomarkers for risk of type 2 diabetes. Int J Epidemiol. 2021; 50(4):1335-1349. PMC: 8562333. DOI: 10.1093/ije/dyaa262. View

Hartwig F, Davey Smith G, Bowden J . Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol. 2017; 46(6):1985-1998. PMC: 5837715. DOI: 10.1093/ije/dyx102. View

Burgess S, Davies N, Thompson S . Bias due to participant overlap in two-sample Mendelian randomization. Genet Epidemiol. 2016; 40(7):597-608. PMC: 5082560. DOI: 10.1002/gepi.21998. View

Zhu X, Li X, Xu R, Wang T . An iterative approach to detect pleiotropy and perform Mendelian Randomization analysis using GWAS summary statistics. Bioinformatics. 2020; 37(10):1390-1400. PMC: 8208738. DOI: 10.1093/bioinformatics/btaa985. View

Schmidt A, Dudbridge F . Mendelian randomization with Egger pleiotropy correction and weakly informative Bayesian priors. Int J Epidemiol. 2017; 47(4):1217-1228. PMC: 6124638. DOI: 10.1093/ije/dyx254. View

Liu L, Zeng P, Xue F, Yuan Z, Zhou X . Multi-trait transcriptome-wide association studies with probabilistic Mendelian randomization. Am J Hum Genet. 2021; 108(2):240-256. PMC: 7895847. DOI: 10.1016/j.ajhg.2020.12.006. View

Burgess S, Butterworth A, Thompson S . Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013; 37(7):658-65. PMC: 4377079. DOI: 10.1002/gepi.21758. View

Wang L, Gao B, Fan Y, Xue F, Zhou X . Mendelian randomization under the omnigenic architecture. Brief Bioinform. 2021; 22(6). DOI: 10.1093/bib/bbab322. View

10.

Boehm F, Zhou X . Statistical methods for Mendelian randomization in genome-wide association studies: A review. Comput Struct Biotechnol J. 2022; 20:2338-2351. PMC: 9123217. DOI: 10.1016/j.csbj.2022.05.015. View

11.

Bowden J, Davey Smith G, Haycock P, Burgess S . Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016; 40(4):304-14. PMC: 4849733. DOI: 10.1002/gepi.21965. View

12.

Xu S, Wang P, Fung W, Liu Z . A novel penalized inverse-variance weighted estimator for Mendelian randomization with applications to COVID-19 outcomes. Biometrics. 2022; 79(3):2184-2195. PMC: 9538932. DOI: 10.1111/biom.13732. View

13.

Yuan Z, Liu L, Guo P, Yan R, Xue F, Zhou X . Likelihood-based Mendelian randomization analysis with automated instrument selection and horizontal pleiotropic modeling. Sci Adv. 2022; 8(9):eabl5744. PMC: 8890724. DOI: 10.1126/sciadv.abl5744. View

14.

Xue H, Shen X, Pan W . Constrained maximum likelihood-based Mendelian randomization robust to both correlated and uncorrelated pleiotropic effects. Am J Hum Genet. 2021; 108(7):1251-1269. PMC: 8322939. DOI: 10.1016/j.ajhg.2021.05.014. View

15.

Slob E, Burgess S . A comparison of robust Mendelian randomization methods using summary data. Genet Epidemiol. 2020; 44(4):313-329. PMC: 7317850. DOI: 10.1002/gepi.22295. View

16.

Newcombe P, Conti D, Richardson S . JAM: A Scalable Bayesian Framework for Joint Analysis of Marginal SNP Effects. Genet Epidemiol. 2016; 40(3):188-201. PMC: 4817278. DOI: 10.1002/gepi.21953. View

17.

Burgess S, Foley C, Allara E, Staley J, Howson J . A robust and efficient method for Mendelian randomization with hundreds of genetic variants. Nat Commun. 2020; 11(1):376. PMC: 6969055. DOI: 10.1038/s41467-019-14156-4. View

18.

Richmond R, Davey Smith G . Mendelian Randomization: Concepts and Scope. Cold Spring Harb Perspect Med. 2021; 12(1). PMC: 8725623. DOI: 10.1101/cshperspect.a040501. View

19.

Lin Z, Xue H, Pan W . Robust multivariable Mendelian randomization based on constrained maximum likelihood. Am J Hum Genet. 2023; 110(4):592-605. PMC: 10119150. DOI: 10.1016/j.ajhg.2023.02.014. View

20.

Su Y, Xu S, Ma Y, Yin P, Hao X, Zhou J . A Modified Debiased Inverse-Variance Weighted Estimator in Two-Sample Summary-Data Mendelian Randomization. Stat Med. 2024; 43(29):5484-5496. DOI: 10.1002/sim.10245. View