Mendelian Randomisation: Concepts, Opportunities, Challenges, and Future Directions

Sophie C. de Ruiter; Lena Tschiderer; Diederick E. Grobbee; Peter Willeit; Hester M. den Ruijter; A. Floriaan Schmidt; Sanne A. E. Peters

doi:10.5334/gh.1438

Mendelian Randomisation: Concepts, Opportunities, Challenges, and Future Directions

Volume 20 (2025): Issue 1

By: Sophie C. de Ruiter , Lena Tschiderer , Diederick E. Grobbee , Peter Willeit , Hester M. den Ruijter , A. Floriaan Schmidt and Sanne A. E. Peters

Open Access

|Jun 2025

Abstract

Mendelian randomisation is an approach in genetic epidemiology that uses genetic variants as instrumental variables to investigate the causal relationship between genetically proxied exposures and health outcomes. During the last years, the number of published Mendelian randomisation studies increased tremendously. There are several opportunities of Mendelian randomisation including obtaining potential causal relationships between both exogenous and endogenous exposures and outcomes and for identifying and prioritising drug-targets to inform clinical trials. However, it is also important to be aware of its challenges. This includes the reliability of results under the assumptions on instrumental variables, being aware of potential biases, the correct and critical interpretation of findings and comparison to the results of randomised controlled trials, as well as the availability of genetic data on specific subgroups. This review provides a comprehensive overview of the opportunities and challenges of Mendelian randomisation and presents key future perspectives.

References

Holland PW. Statistics and causal inference. Journal of the American Statistical Association. 1986;81(396):945–960. DOI: 10.1080/01621459.1986.10478354
Open DOI Search in Google Scholar Back to article
Staiger DO, Stock JH. Instrumental variables regression with weak instruments. National Bureau of Economic Research Cambridge, Mass., USA; 1994. DOI: 10.3386/t0151
Open DOI Search in Google Scholar Back to article
Katan MB. Apolipoprotein E isoforms, serum cholesterol, and cancer. The Lancet. 1986;327:507–508. DOI: 10.1016/S0140-6736(86)92972-7
Open DOI Search in Google Scholar Back to article
Davey Smith G, Ebrahim S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? International Journal of Epidemiology. 2003;32(1):1–22. DOI: 10.1093/ije/dyg070
Open DOI Search in Google Scholar Back to article
Schmidt AF, Finan C, Gordillo-Marañón M, Asselbergs FW, Freitag DF, Patel RS, et al. Genetic drug target validation using Mendelian randomisation. Nature Communications. 2020;11(1):3255. DOI: 10.1038/s41467-020-16969-0
Open DOI Search in Google Scholar Back to article
Schmidt AF, Hingorani AD, Finan C. Human genomics and drug development. Cold Spring Harb Perspect Med. 2022;12(2):a039230. DOI: 10.1101/cshperspect.a039230
Open DOI Search in Google Scholar Back to article
Burgess S, Thompson SG. Mendelian randomization: methods for using genetic variants in causal estimation. CRC Press; 2015.
Search in Google Scholar Back to article
Yuan S, Lin A, He QQ, Burgess S, Larsson SC. Circulating interleukins in relation to coronary artery disease, atrial fibrillation and ischemic stroke and its subtypes: A two-sample Mendelian randomization study. Int J Cardiol. 2020;313:99–104. DOI: 10.1016/j.ijcard.2020.03.053
Open DOI Search in Google Scholar Back to article
Yuan S, Mason AM, Burgess S, Larsson SC. Genetic liability to insomnia in relation to cardiovascular diseases: a Mendelian randomisation study. Eur J Epidemiol. 2021;36(4):393–400. DOI: 10.1007/s10654-021-00737-5
Open DOI Search in Google Scholar Back to article
Wang L, Zhang X, Meng X, Koskeridis F, Georgiou A, Yu L, et al. Methodology in phenome-wide association studies: a systematic review. Journal of Medical Genetics. 2021;58(11):720–728. DOI: 10.1136/jmedgenet-2021-107696
Open DOI Search in Google Scholar Back to article
Zheng J, Haberland V, Baird D, Walker V, Haycock PC, Hurle MR, et al. Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases. Nature Genetics. 2020;52(10):1122–1131. DOI: 10.1038/s41588-020-0682-6
Open DOI Search in Google Scholar Back to article
Trubetskoy V, Pardiñas AF, Qi T, Panagiotaropoulou G, Awasthi S, Bigdeli TB, et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature. 2022;604(7906):502–508. DOI: 10.1038/s41586-022-04434-5
Open DOI Search in Google Scholar Back to article
Campos AI, García-Marín LM, Byrne EM, Martin NG, Cuéllar-Partida G, Rentería ME. Insights into the aetiology of snoring from observational and genetic investigations in the UK Biobank. Nature Communications. 2020;11(1):817. DOI: 10.1038/s41467-020-14625-1
Open DOI Search in Google Scholar Back to article
Larsson SC, Burgess S. Appraising the causal role of smoking in multiple diseases: A systematic review and meta-analysis of Mendelian randomization studies. EBioMedicine. 2022;82:104154. DOI: 10.1016/j.ebiom.2022.104154
Open DOI Search in Google Scholar Back to article
Vithayathil M, Carter P, Kar S, Mason AM, Burgess S, Larsson SC. Body size and composition and risk of site-specific cancers in the UK Biobank and large international consortia: A mendelian randomisation study. PLoS Med. 2021;18(7):e1003706. DOI: 10.1371/journal.pmed.1003706
Open DOI Search in Google Scholar Back to article
Collaboration ERF. C-reactive protein concentration and risk of coronary heart disease, stroke, and mortality: an individual participant meta-analysis. The Lancet. 2010;375(9709):132–140. DOI: 10.1016/S0140-6736(09)61717-7
Open DOI Search in Google Scholar Back to article
Danesh J, Pepys MB. C-reactive protein and coronary disease: is there a causal link? Am Heart Assoc. 2009;120(101):2036–2039. DOI: 10.1161/CIRCULATIONAHA.109.907212
Open DOI Search in Google Scholar Back to article
Collaboration CRPCHDG. Association between C reactive protein and coronary heart disease: mendelian randomisation analysis based on individual participant data. BMJ. 2011;15(342):d548. DOI: 10.1136/bmj.d548
Open DOI Search in Google Scholar Back to article
Zacho J, Tybjærg-Hansen A, Jensen JS, Grande P, Sillesen H, Nordestgaard BG. Genetically elevated C-reactive protein and ischemic vascular disease. New England Journal of Medicine. 2008;359(18):1897–1908. DOI: 10.1056/NEJMoa0707402
Open DOI Search in Google Scholar Back to article
van der Bom JG, de Maat MP, Bots ML, Haverkate F, De Jong P, Hofman A, et al. Elevated plasma fibrinogen: cause or consequence of cardiovascular disease? Arteriosclerosis, Thrombosis, and Vascular Biology. 1998;18(4):621–625. DOI: 10.1161/01.ATV.18.4.621
Open DOI Search in Google Scholar Back to article
Porcu E, Rüeger S, Lepik K, Santoni FA, Reymond A, Kutalik Z. Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits. Nature Communications. 2019;10(1):3300. DOI: 10.1038/s41467-019-10936-0
Open DOI Search in Google Scholar Back to article
Khankari NK, Keaton JM, Walker VM, Lee KM, Shuey MM, Clarke SL, et al. Using Mendelian randomisation to identify opportunities for type 2 diabetes prevention by repurposing medications used for lipid management. EBioMedicine. 2022;80:104038. DOI: 10.1016/j.ebiom.2022.104038
Open DOI Search in Google Scholar Back to article
Richardson TG, Hemani G, Gaunt TR, Relton CL, Davey Smith G. A transcriptome-wide Mendelian randomization study to uncover tissue-dependent regulatory mechanisms across the human phenome. Nature Communications. 2020;11(1):185. DOI: 10.1038/s41467-019-13921-9
Open DOI Search in Google Scholar Back to article
Taylor K, Davey Smith G, Relton CL, Gaunt TR, Richardson TG. Prioritizing putative influential genes in cardiovascular disease susceptibility by applying tissue-specific Mendelian randomization. Genome Medicine. 2019;11:1–15. DOI: 10.1186/s13073-019-0613-2
Open DOI Search in Google Scholar Back to article
Xu X, Eales JM, Akbarov A, Guo H, Becker L, Talavera D, et al. Molecular insights into genome-wide association studies of chronic kidney disease-defining traits. Nature Communications. 2018;9(1):4800. DOI: 10.1038/s41467-018-07260-4
Open DOI Search in Google Scholar Back to article
Wu X, Jiang L, Qi H, Hu C, Jia X, Lin H, et al. Brain tissue-and cell type-specific eQTL Mendelian randomization reveals efficacy of FADS1 and FADS2 on cognitive function. Translational Psychiatry. 2024;14(1):77. DOI: 10.1038/s41398-024-02784-4
Open DOI Search in Google Scholar Back to article
Schmidt AF, Finan C, Gordillo-Marañón M, Asselbergs FW, Freitag DF, Patel RS, et al. Genetic drug target validation using Mendelian randomisation. Nature Communications. 2020;11(1):1–12. DOI: 10.1038/s41467-020-16969-0
Open DOI Search in Google Scholar Back to article
King EA, Davis JW, Degner JF. Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval. PLoS Genetics. 2019;15(12):e1008489. DOI: 10.1371/journal.pgen.1008489
Open DOI Search in Google Scholar Back to article
Ochoa D, Karim M, Ghoussaini M, Hulcoop DG, McDonagh EM, Dunham I. Human genetics evidence supports two-thirds of the 2021 FDA-approved drugs. Nat Rev Drug Discov. 2022;21(8):551. DOI: 10.1038/d41573-022-00120-3
Open DOI Search in Google Scholar Back to article
Plenge RM, Scolnick EM, Altshuler D. Validating therapeutic targets through human genetics. Nature Reviews Drug Discovery. 2013;12(8):581–594. DOI: 10.1038/nrd4051
Open DOI Search in Google Scholar Back to article
Voight BF, Peloso GM, Orho-Melander M, Frikke-Schmidt R, Barbalic M, Jensen MK, et al. Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study. The Lancet. 2012;380(9841):572–580. DOI: 10.1016/S0140-6736(12)60312-2
Open DOI Search in Google Scholar Back to article
Pushpakom S, Iorio F, Eyers PA, Escott KJ, Hopper S, Wells A, et al. Drug repurposing: progress, challenges and recommendations. Nature Reviews Drug Discovery. 2019;18(1):41–58. DOI: 10.1038/nrd.2018.168
Open DOI Search in Google Scholar Back to article
Huang J, Huffman JE, Huang Y, Do Valle I, Assimes TL, Raghavan S, et al. Genomics and phenomics of body mass index reveals a complex disease network. Nature Communications. 2022;13(1):7973. DOI: 10.1038/s41467-022-35553-2
Open DOI Search in Google Scholar Back to article
Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, et al. Guidelines for performing Mendelian randomization investigations: update for summer 2023. Wellcome Open Res. 2019;4:186. DOI: 10.12688/wellcomeopenres.15555.1
Open DOI Search in Google Scholar Back to article
Lawlor DA, Harbord RM, Sterne JA, Timpson N, Davey Smith G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Statistics in Medicine. 2008;27(8):1133–1163. DOI: 10.1002/sim.3034
Open DOI Search in Google Scholar Back to article
Verbanck M, Chen C-Y, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nature Genetics. 2018;50(5):693–698. DOI: 10.1038/s41588-018-0099-7
Open DOI Search in Google Scholar Back to article
Sheehan NA, Meng S, Didelez V. Mendelian randomisation: a tool for assessing causality in observational epidemiology. Genetic Epidemiology. 2011;153–166. DOI: 10.1007/978-1-60327-416-6_12
Open DOI Search in Google Scholar Back to article
VanderWeele TJ, Tchetgen EJT, Cornelis M, Kraft P. Methodological challenges in mendelian randomization. Epidemiology. 2014;25(3):427–435. DOI: 10.1097/EDE.0000000000000081
Open DOI Search in Google Scholar Back to article
Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017;32(5):377–389. DOI: 10.1007/s10654-017-0255-x
Open DOI Search in Google Scholar Back to article
Swanson SA, Robins JM, Miller M, Hernán MA. Selecting on treatment: a pervasive form of bias in instrumental variable analyses. American Journal of Epidemiology. 2015;181(3):191–197. DOI: 10.1093/aje/kwu284
Open DOI Search in Google Scholar Back to article
Canan C, Lesko C, Lau B. Instrumental variable analyses and selection bias. Epidemiology. 2017;28(3):396–398. DOI: 10.1097/EDE.0000000000000639
Open DOI Search in Google Scholar Back to article
Vansteelandt S, Dukes O, Martinussen T. Survivor bias in Mendelian randomization analysis. Biostatistics. 2018;19(4):426–443. DOI: 10.1093/biostatistics/kxx050
Open DOI Search in Google Scholar Back to article
Burgess S, Butterworth A, Malarstig A, Thompson SG. Use of Mendelian randomisation to assess potential benefit of clinical intervention. BMJ. 2012;345:e7325. DOI: 10.1136/bmj.e7325
Open DOI Search in Google Scholar Back to article
Evangelou E, Warren HR, Mosen-Ansorena D, Mifsud B, Pazoki R, Gao H, et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nature Genetics. 2018;50(10):1412–1425. DOI: 10.1038/s41588-018-0205-x
Open DOI Search in Google Scholar Back to article
Gill D, Dib MJ, Cronje HT, Karhunen V, Woolf B, Gagnon E, et al. Common pitfalls in drug target Mendelian randomization and how to avoid them. BMC Med. 2024;22(1):473. DOI: 10.1186/s12916-024-03700-9
Open DOI Search in Google Scholar Back to article
Magi R, Lindgren CM, Morris AP. Meta-analysis of sex-specific genome-wide association studies. Genetic Epidemiology. 2010;34(8):846–853. DOI: 10.1002/gepi.20540
Open DOI Search in Google Scholar Back to article
Bernabeu E, Canela-Xandri O, Rawlik K, Talenti A, Prendergast J, Tenesa A. Sex differences in genetic architecture in the UK Biobank. Nature Genetics. 2021;53(9):1283–1289. DOI: 10.1038/s41588-021-00912-0
Open DOI Search in Google Scholar Back to article
Shifman S, Johannesson M, Bronstein M, Chen SX, Collier DA, Craddock NJ, et al. Genome-wide association identifies a common variant in the reelin gene that increases the risk of schizophrenia only in women. PLoS Genetics. 2008;4(2):e28. DOI: 10.1371/journal.pgen.0040028
Open DOI Search in Google Scholar Back to article
Döring A, Gieger C, Mehta D, Gohlke H, Prokisch H, Coassin S, et al. SLC2 A9 influences uric acid concentrations with pronounced sex-specific effects. Nature Genetics. 2008;40(4):430–436. DOI: 10.1038/ng.107
Open DOI Search in Google Scholar Back to article
Lindgren CM, Heid IM, Randall JC, Lamina C, Steinthorsdottir V, Qi L, et al. Genome-wide association scan meta-analysis identifies three Loci influencing adiposity and fat distribution. PLoS Genetics. 2009;5(6):e1000508.
Search in Google Scholar Back to article
Lumish HS, O’Reilly M, Reilly MP. Sex differences in genomic drivers of adipose distribution and related cardiometabolic disorders: opportunities for precision medicine. Arteriosclerosis, Thrombosis, and Vascular Biology. 2020;40(1):45–60. DOI: 10.1161/ATVBAHA.119.313154
Open DOI Search in Google Scholar Back to article
Aragam KG, Jiang T, Goel A, Kanoni S, Wolford BN, Atri DS, et al. Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants. Nature Genetics. 2022;54:1803–1815. DOI: 10.1038/s41588-022-01233-6
Open DOI Search in Google Scholar Back to article
Adlam D, Berrandou TE, Georges A, Nelson CP, Giannoulatou E, Henry J, et al. Genome-wide association meta-analysis of spontaneous coronary artery dissection identifies risk variants and genes related to artery integrity and tissue-mediated coagulation. Nature Genetics. 2023;55(6):964–972. DOI: 10.1038/s41588-023-01410-1
Open DOI Search in Google Scholar Back to article
Sakkers TR, Mokry M, Civelek M, Erdmann J, Pasterkamp G, Benavente ED, et al. Sex differences in the genetic and molecular mechanisms of coronary artery disease. Atherosclerosis. 2023; 384:117279. DOI: 10.1016/j.atherosclerosis.2023.117279
Open DOI Search in Google Scholar Back to article
Suhre K, Arnold M, Bhagwat AM, Cotton RJ, Engelke R, Raffler J, et al. Connecting genetic risk to disease end points through the human blood plasma proteome. Nature Communications. 2017;8(1):14357. DOI: 10.1038/ncomms14357
Open DOI Search in Google Scholar Back to article
Emilsson V, Ilkov M, Lamb JR, Finkel N, Gudmundsson EF, Pitts R, et al. Co-regulatory networks of human serum proteins link genetics to disease. Science. 2018;361(6404):769–773. DOI: 10.1126/science.aaq1327
Open DOI Search in Google Scholar Back to article
Sun BB, Maranville JC, Peters JE, Stacey D, Staley JR, Blackshaw J, et al. Genomic atlas of the human plasma proteome. Nature. 2018;558(7708):73–79. DOI: 10.1038/s41586-018-0175-2
Open DOI Search in Google Scholar Back to article
Benson MD, Yang Q, Ngo D, Zhu Y, Shen D, Farrell LA, et al. Genetic architecture of the cardiovascular risk proteome. Circulation. 2018;137(11):1158–1172. DOI: 10.1161/CIRCULATIONAHA.117.029536
Open DOI Search in Google Scholar Back to article
Yao C, Chen G, Song C, Keefe J, Mendelson M, Huan T, et al. Genome-wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease. Nature Communications. 2018;9(1):1–11. DOI: 10.1038/s41467-018-06231-z
Open DOI Search in Google Scholar Back to article
Zhernakova DV, Le TH, Kurilshikov A, Atanasovska B, Bonder MJ, Sanna S, et al. Individual variations in cardiovascular-disease-related protein levels are driven by genetics and gut microbiome. Nature Genetics. 2018;50(11):1524–1532. DOI: 10.1038/s41588-018-0224-7
Open DOI Search in Google Scholar Back to article
Richardson TG, Zheng J, Smith GD, Timpson NJ, Gaunt TR, Relton CL, et al. Mendelian randomization analysis identifies CpG sites as putative mediators for genetic influences on cardiovascular disease risk. The American Journal of Human Genetics. 2017;101(4):590–602. DOI: 10.1016/j.ajhg.2017.09.003
Open DOI Search in Google Scholar Back to article
Reay WR, Cairns MJ. Advancing the use of genome-wide association studies for drug repurposing. Nature Reviews Genetics. 2021;22(10):658–671. DOI: 10.1038/s41576-021-00387-z
Open DOI Search in Google Scholar Back to article
Mahajan A, Taliun D, Thurner M, Robertson NR, Torres JM, Rayner NW, et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nature Genetics. 2018;50(11):1505–1513. DOI: 10.1038/s41588-018-0241-6
Open DOI Search in Google Scholar Back to article
Chen J, Spracklen CN, Marenne G, Varshney A, Corbin LJ, Luan J, et al. The trans-ancestral genomic architecture of glycemic traits. Nature Genetics. 2021;53(6):840–860. DOI: 10.1038/s41588-021-00852-9
Open DOI Search in Google Scholar Back to article
Ntzani EE, Liberopoulos G, Manolio TA, Ioannidis JP. Consistency of genome-wide associations across major ancestral groups. Human Genetics. 2012;131:1057–1071. DOI: 10.1007/s00439-011-1124-4
Open DOI Search in Google Scholar Back to article
Peterson RE, Kuchenbaecker K, Walters RK, Chen C-Y, Popejoy AB, Periyasamy S, et al. Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. Cell. 2019;179(3):589–603. DOI: 10.1016/j.cell.2019.08.051
Open DOI Search in Google Scholar Back to article
Gurdasani D, Carstensen T, Fatumo S, Chen G, Franklin CS, Prado-Martinez J, et al. Uganda genome resource enables insights into population history and genomic discovery in Africa. Cell. 2019;179(4):984–1002. e36. DOI: 10.1016/j.cell.2019.10.004
Open DOI Search in Google Scholar Back to article
Assimes T, Tcheandjieu C, Zhu X, Hilliard A, Clarke S, Napolioni V, et al. A large-scale multi-ethnic genome-wide association study of coronary artery disease. Nature Medicine. 2022;28:1679–1692. DOI: 10.1038/s41591-022-01891-3
Open DOI Search in Google Scholar Back to article
Ishigaki K, Sakaue S, Terao C, Luo Y, Sonehara K, Yamaguchi K, et al. Multi-ancestry genome-wide association analyses identify novel genetic mechanisms in rheumatoid arthritis. Nature Genetics. 2022;54(11):1640–1651. DOI: 10.1038/s41588-022-01213-w
Open DOI Search in Google Scholar Back to article
Pervjakova N, Moen G-H, Borges M-C, Ferreira T, Cook JP, Allard C, et al. Multi-ancestry genome-wide association study of gestational diabetes mellitus highlights genetic links with type 2 diabetes. Human Molecular Genetics. 2022;31(19):3377–3391. DOI: 10.1093/hmg/ddac050
Open DOI Search in Google Scholar Back to article
Oliva M, Muñoz-Aguirre M, Kim-Hellmuth S, Wucher V, Gewirtz AD, Cotter DJ, et al. The impact of sex on gene expression across human tissues. Science. 2020;369(6509):eaba3066.
Search in Google Scholar Back to article
Melé M, Ferreira PG, Reverter F, DeLuca DS, Monlong J, Sammeth M, et al. The human transcriptome across tissues and individuals. Science. 2015;348(6235):660–665. DOI: 10.1126/science.aaa0355
Open DOI Search in Google Scholar Back to article
Hartman RJ, Mokry M, Pasterkamp G, den Ruijter HM. Sex-dependent gene co-expression in the human body. Scientific Reports. 2021;11(1):18758. DOI: 10.1038/s41598-021-98059-9
Open DOI Search in Google Scholar Back to article

Articles in this issue

DOI: https://doi.org/10.5334/gh.1438 | Journal eISSN: 2211-8179

Journal RSS Feed

Language: English

Submitted on: May 12, 2025

Accepted on: May 27, 2025

Published on: Jun 17, 2025

Published by: Ubiquity Press

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

Mendelian randomisation,

© 2025 Sophie C. de Ruiter, Lena Tschiderer, Diederick E. Grobbee, Peter Willeit, Hester M. den Ruijter, A. Floriaan Schmidt, Sanne A. E. Peters, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 20 (2025): Issue 1