Implementing MR-PRESSO and GCTA-GSMR for Pleiotropy Assessment in Mendelian Randomization Studies from a Practitioner's Perspective

Overview

Journal Genet Epidemiol

Specialties Genetics
Public Health

Date 2019 May 3

PMID 31045282

Citations 141

Authors

Jue-Sheng Ong

Stuart Macgregor

Affiliations

Soon will be listed here.

Abstract

With the advent of very large scale genome-wide association studies (GWASs), the promise of Mendelian randomization (MR) has begun to be fulfilled. However, whilst GWASs have provided essential information on the single nucleotide polymorphisms (SNPs) associated with modifiable risk factors needed for MR, the availability of large numbers of SNP instruments raises issues of how best to use this information and how to deal with potential problems such as pleiotropy. Here we provide commentary on some of the recent advances in the MR analysis, including an overview of the different genetic architectures that are being uncovered for a variety of modifiable risk factors and how users ought to take that into consideration when designing MR studies.

Citing Articles

The causal effects of inflammatory bowel disease on its ocular manifestations: A Mendelian randomization study.

Luo L, Tang X, Xu J, Bao Y, Hu X, Zhong X PLoS One. 2025; 20(3):e0316437.

PMID: 40072972 PMC: 11902285. DOI: 10.1371/journal.pone.0316437.

Genetically Elevated Selenoprotein S Levels and Risk of Stroke: A Two-Sample Mendelian Randomization Analysis.

He Y, Liu Y, Meng H, Sun J, Rui Y, Tian X Int J Mol Sci. 2025; 26(4).

PMID: 40004116 PMC: 11855697. DOI: 10.3390/ijms26041652.

Chronic pharyngitis and cervical spondylosis risk: A bidirectional Mendelian randomization study.

Li Y, Li X, Yu X, Xu Y, Su Y, Guo L Medicine (Baltimore). 2025; 104(8):e41678.

PMID: 39993108 PMC: 11856965. DOI: 10.1097/MD.0000000000041678.

Causal relationship between educational attainment and the occurrence of venous thromboembolism.

Guo S, Tan S, Qin S, Xu D, Su H, Chen X BMC Med Genomics. 2025; 18(1):28.

PMID: 39920705 PMC: 11803988. DOI: 10.1186/s12920-025-02092-w.

Causal relationship between 412 gut microbiota, 1,400 blood metabolites, and diabetic nephropathy: a randomized Mendelian study.

Cao B, Zhang C, Wang Z, Wang Y Front Endocrinol (Lausanne). 2025; 15:1450428.

PMID: 39897958 PMC: 11782027. DOI: 10.3389/fendo.2024.1450428.

References

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

Pierce B, Burgess S . Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators. Am J Epidemiol. 2013; 178(7):1177-84. PMC: 3783091. DOI: 10.1093/aje/kwt084. View

Ong J, Macgregor S . Implementing MR-PRESSO and GCTA-GSMR for pleiotropy assessment in Mendelian randomization studies from a practitioner's perspective. Genet Epidemiol. 2019; 43(6):609-616. PMC: 6767464. DOI: 10.1002/gepi.22207. View

Zhu Z, Zheng Z, Zhang F, Wu Y, Trzaskowski M, Maier R . Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat Commun. 2018; 9(1):224. PMC: 5768719. DOI: 10.1038/s41467-017-02317-2. View

Hebbring S . The challenges, advantages and future of phenome-wide association studies. Immunology. 2013; 141(2):157-65. PMC: 3904236. DOI: 10.1111/imm.12195. View

Davey Smith G, Hemani G . Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014; 23(R1):R89-98. PMC: 4170722. DOI: 10.1093/hmg/ddu328. View

Zheng J, Baird D, Borges M, Bowden J, Hemani G, Haycock P . Recent Developments in Mendelian Randomization Studies. Curr Epidemiol Rep. 2017; 4(4):330-345. PMC: 5711966. DOI: 10.1007/s40471-017-0128-6. 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

Zhu Z, Zhang F, Hu H, Bakshi A, Robinson M, Powell J . Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016; 48(5):481-7. DOI: 10.1038/ng.3538. View

10.

Burgess S, Timpson N, Ebrahim S, Davey Smith G . Mendelian randomization: where are we now and where are we going?. Int J Epidemiol. 2015; 44(2):379-88. DOI: 10.1093/ije/dyv108. View

11.

Bowden J, Del Greco M F, Minelli C, Zhao Q, Lawlor D, Sheehan N . Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption. Int J Epidemiol. 2018; 48(3):728-742. PMC: 6659376. DOI: 10.1093/ije/dyy258. View

12.

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

13.

Lawlor D . Commentary: Two-sample Mendelian randomization: opportunities and challenges. Int J Epidemiol. 2016; 45(3):908-15. PMC: 5005949. DOI: 10.1093/ije/dyw127. View

14.

Burgess S, Scott R, Timpson N, Davey Smith G, Thompson S . Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur J Epidemiol. 2015; 30(7):543-52. PMC: 4516908. DOI: 10.1007/s10654-015-0011-z. View

15.

Brion M, Shakhbazov K, Visscher P . Calculating statistical power in Mendelian randomization studies. Int J Epidemiol. 2013; 42(5):1497-501. PMC: 3807619. DOI: 10.1093/ije/dyt179. View

16.

Bowden J, Davey Smith G, Burgess S . Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015; 44(2):512-25. PMC: 4469799. DOI: 10.1093/ije/dyv080. View

17.

Verbanck M, Chen C, Neale B, Do R . Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018; 50(5):693-698. PMC: 6083837. DOI: 10.1038/s41588-018-0099-7. View

18.

Vilhjalmsson B, Yang J, Finucane H, Gusev A, Lindstrom S, Ripke S . Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores. Am J Hum Genet. 2015; 97(4):576-92. PMC: 4596916. DOI: 10.1016/j.ajhg.2015.09.001. View

19.

Cornelis M, Byrne E, Esko T, Nalls M, Ganna A, Paynter N . Genome-wide meta-analysis identifies six novel loci associated with habitual coffee consumption. Mol Psychiatry. 2014; 20(5):647-656. PMC: 4388784. DOI: 10.1038/mp.2014.107. View

20.

Liu M, Jiang Y, Wedow R, Li Y, Brazel D, Chen F . Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat Genet. 2019; 51(2):237-244. PMC: 6358542. DOI: 10.1038/s41588-018-0307-5. View