Automating Mendelian randomization through machine learning to construct a putative causal map of the human phenome
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A major application for genome-wide association studies (GWAS) has been the emerging field of causal inference using Mendelian randomization (MR), where the causal effect between a pair of traits can be estimated using only summary level data. MR depends on SNPs exhibiting vertical pleiotropy, where the SNP influences an outcome phenotype only through an exposure phenotype. Issues arise when this assumption is violated due to SNPs exhibiting horizontal pleiotropy. We demonstrate that across a range of pleiotropy models, instrument selection will be increasingly liable to selecting invalid instruments as GWAS sample sizes continue to grow. Methods have been developed in an attempt to protect MR from different patterns of horizontal pleiotropy, and here we have designed a mixture-of-experts machine learning framework (MR-MoE 1.0) that predicts the most appropriate model to use for any specific causal analysis, improving on both power and false discovery rates. Using the approach, we systematically estimated the causal effects amongst 2407 phenotypes. Almost 90% of causal estimates indicated some level of horizontal pleiotropy. The causal estimates are organised into a publicly available graph database ( http://eve.mrbase.org ), and we use it here to highlight the numerous challenges that remain in automated causal inference.
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Jamieson, E., Korologou‐Linden, R., Wootton, R. E., Guyatt, A. L., Battram, T., Burrows, K., Gaunt, T. R., Tobin, M. D., Munafo, M. R., Smith, G. D., et al. (2020) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2020.01.015 · Cited by 58 · see citing works
Self-citationLawlor, D. A., Wade, K. H., Borges, M. C., Palmer, T., Hartwig, F. P., Hemani, G., & Bowden, J. (2019) · doi:10.31219/osf.io/6yzs7 · Cited by 50 · see citing works
Self-citationMendelian Randomization analysis of the causal effect of adiposity on hospital costs
Dixon, P., Hollingworth, W., Harrison, S., Davies, N. M., & Davey Smith, G. (2020) · Journal of Health Economics · doi:10.1016/j.jhealeco.2020.102300 · Cited by 48 · see citing works
Self-citationMachine learning applications for therapeutic tasks with genomics data
Huang, K., Xiao, C., Glass, L. M., Critchlow, C. W., Gibson, G. C., & Sun, J. (2021) · Patterns · doi:10.1016/j.patter.2021.100328 · Cited by 46 · see citing works
Walker, V. M., Zheng, J., Gaunt, T. R., & Smith, G. D. (2022) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-122120-024910 · Cited by 43 · see citing works
Self-citationUnhealthy Behaviours and Risk of Parkinson’s Disease: A Mendelian Randomisation Study
Heilbron, K., Jensen, M. P., Bandrés‐Ciga, S., Fontanillas, P., Blauwendraat, C., Nalls, M. A., Singleton, A., Smith, G. D., Cannon, P., & Noyce, A. J. (2021) · Journal of Parkinson s Disease · doi:10.3233/jpd-202487 · Cited by 43 · see citing works
Self-citationAssociations between alcohol use and accelerated biological ageing
Bøstrand, S. M. K., Vaher, K., de Nooij, L., Harris, M. A., Cole, J. H., Cox, S. R., Marioni, R. E., McCartney, D. L., Walker, R. M., McIntosh, A. M., et al. (2021) · Addiction Biology · doi:10.1111/adb.13100 · Cited by 41 · see citing works
Plotnikov, D., & Guggenheim, J. A. (2019) · Ophthalmic and Physiological Optics · doi:10.1111/opo.12596 · Cited by 36 · see citing works
Galan, D., Perry, B. I., Warrier, V., Davidson, C. C., Stupart, O., Easton, D. F., Khandaker, G. M., & Murray, G. K. (2022) · Scientific Reports · doi:10.1038/s41598-022-19214-4 · Cited by 32 · see citing works
Artificial intelligence for dementia genetics and omics
Bettencourt, C., Skene, N., Bandres‐Ciga, S., Anderson, E., Winchester, L. M., Foote, I. F., Schwartzentruber, J., Botia, J. A., Nalls, M., Singleton, A., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13427 · Cited by 30 · see citing works
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The Effects of Fatty Acids on Inflammatory Bowel Disease: A Two-Sample Mendelian Randomization Study
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Caution against examining the role of reverse causality in Mendelian Randomization
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How humans can contribute to Mendelian randomization analyses.
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Self-citationBrown, B. C., & Knowles, D. A. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.10.006 · Cited by 15 · see citing works
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Simons, P. I. H. G., Cornelissen, M. E. B., Valkenburg, O., Onland‐Moret, N. C., van der Schouw, Y. T., Stehouwer, C. D. A., Burgess, S., & Brouwers, M. C. G. J. (2021) · Clinical Endocrinology · doi:10.1111/cen.14593 · Cited by 14 · see citing works
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Cheng, J., Deng, Y., & Yu, J. (2023) · Journal of Neurochemistry · doi:10.1111/jnc.15937 · Cited by 13 · see citing works
Causal relationship between particulate matter 2.5 and diabetes: two sample Mendelian randomization
Kim, J. M., Kim, E., Song, D. K., Kim, Y., Lee, J. H., & Ha, E. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1164647 · Cited by 11 · see citing works
Distilling causality between physical activity traits and obesity via Mendelian randomization
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Self-citationCho, Y., Haycock, P., Sanderson, E., Gaunt, T. R., Zheng, J., Morris, A. P., Smith, G. D., & Hemani, G. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/476085 · Cited by 9 · see citing works
Self-citationBowden, J., Spiller, W., Fabiola Del Greco, M., Sheehan, N. A., Thompson, J. R., Minelli, C., & Smith, G. D. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/200378 · Cited by 12 · see citing works
Self-citationXu, M., Liu, D., Tan, Y., He, J., & Zhou, B. (2024) · Scientific Reports · doi:10.1038/s41598-023-50459-9 · Cited by 10 · see citing works
Triangulating evidence in health sciences with Annotated Semantic Queries
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Self-citationZou, J., Talluri, R., & Shete, S. (2024) · PLoS ONE · doi:10.1371/journal.pone.0293510 · Cited by 7 · see citing works
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The rapid growth in Mendelian randomization studies
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Self-citationYu, K., Li, Z., Shi, W., Zhao, Z., & Li, Y. (2024) · International Journal of Clinical Pharmacy · doi:10.1007/s11096-024-01754-6 · Cited by 5 · see citing works
Associations between alcohol use and accelerated biological ageingPreprint
Bøstrand, S. M. K., Vaher, K., de Nooij, L., Harris, M. A., Cole, J. H., Cox, S. R., Marioni, R. E., McCartney, D. L., Walker, R. M., McIntosh, A. M., et al. (2020) · medRxiv · doi:10.1101/2020.11.24.20237156 · Cited by 5 · see citing works
Raising the bar for publication of Mendelian randomisation studies in Diabetologia
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Big knowledge from big data in functional genomics
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Computational Tools for Causal Inference in GeneticsPreprint
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Self-citationTrans-ancestral Genome Wide Association Study of Sporadic and Recurrent MiscarriagePreprint
Reynoso, A., Nandakumar, P., Shi, J., Bielenberg, J., Holmes, M. V., & Aslibekyan, S. W. (2024) · medRxiv · doi:10.1101/2024.03.20.24304624 · Cited by 4 · see citing works
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Self-citationFang, Y.-W., Sen, S., Pathak, G., Burmeister, M., & Richmond-Rakerd, L. (2026) · npj Mental Health Research · doi:10.1038/s44184-026-00204-7 · Cited by 1 · see citing works
Exploring the role of circulating proteins in multiple myeloma risk: a Mendelian randomization study
Lee, M. A., Burley, K., Hazelwood, E., Moore, S. E., Lewis, S. J., & Goudswaard, L. J. (2025) · Scientific Reports · doi:10.1038/s41598-025-86222-5 · Cited by 1 · see citing works
Integrating Mendelian randomization and literature-mined evidence for breast cancer risk factors
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Genetic influence of meningioma on cisplatin resistance: a Mendelian randomization analysis
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A phenome-wide approach to identify causal risk factors for deep vein thrombosisPreprint
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Self-citationEstimating the causal effect of hearing loss on Alzheimer’s disease: a Mendelian randomisation studyPreprint
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Collider bias from selecting disease samples distorts causal inferences
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Genetically proxied therapeutic prolyl-hydroxylase inhibition and cardiovascular risk
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Childhood Maltreatment Causally Associated with Osteoarthritis: A Mendelian Randomization StudyPreprint
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- Authors
- Hemani, G., Bowden, J., Haycock, P., Zheng, J., Davis, O., Flach, P., Gaunt, T., & Smith, G. D.
- Affiliations
- University of Bristol (GB)
- ORCID
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2017-08-10
- OpenAlex
- W2744463954
- Cited by
- 85
- References
- 55
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 11 · 2023: 9 · 2022: 10 · 2021: 10 · 2020: 11 · 2019: 11 · 2018: 12 · 2017: 3
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Funders
- Cancer Research UK (C18281/, C18281 / A19169, A20138, A19169, C52724/A20138)
- Medical Research Council (MC_UU_12013, MC UU 12013/8, MCUU12013/1, MR/N501906/1)
- Engineering and Physical Sciences Research Council (EP/K031910/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- Mendelian randomization, horizontal pleiotropy, instrument selection, mixture of experts, causal effect estimation, false discovery rate
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