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Evaluating the potential role of pleiotropy in Mendelian randomization studies
Open accessAbstract
Pleiotropy, the phenomenon of a single genetic variant influencing multiple traits, is likely widespread in the human genome. If pleiotropy arises because the single nucleotide polymorphism (SNP) influences one trait, which in turn influences another ('vertical pleiotropy'), then Mendelian randomization (MR) can be used to estimate the causal influence between the traits. Of prime focus among the many limitations to MR is the unprovable assumption that apparent pleiotropic associations are mediated by the exposure (i.e. reflect vertical pleiotropy), and do not arise due to SNPs influencing the two traits through independent pathways ('horizontal pleiotropy'). The burgeoning treasure trove of genetic associations yielded through genome wide association studies makes for a tantalizing prospect of phenome-wide causal inference. Recent years have seen substantial attention devoted to the problem of horizontal pleiotropy, and in this review we outline how newly developed methods can be used together to improve the reliability of MR.
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“This pattern is consistent with a directional influence of disease liability on longevity27,64 and is also compatible with vertical pleiotropy65.”
From Genetic trade-offs in fertility and longevity explain the maintenance of disease-associated alleles in humans (2026) · cites this paper as [65]
In the citing paper
“However, confounding of the genetic variants with the outcome in a sample can be induced by population stratification, dynastic effects and assortative mating,60 violating the second IV condition.”
From Mendelian randomization (2022) · cites this paper as [60]
In the citing paper
“If there are many correlated candidate variants in a gene region, then including all variants in a single analysis will typically result in numerical instability, as the analysis can be highly sensitive to small changes in the variant correlation matrix 62 .”
From Guidelines for performing Mendelian randomization investigations (2020) · cites this paper as [62]
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Chun, S., Casparino, A., Patsopoulos, N. A., Croteau-Chonka, D. C., Raby, B. A., De Jager, P. L., Sunyaev, S. R., & Cotsapas, C. (2017) · Nature Genetics · doi:10.1038/ng.3795 · Cited by 259
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“Modified second-order weights are also incorporated into the MR-RAPS (54) estimator.”
BackgroundMethodInfluentialSelf-citationThe meta-analysis of genome-wide association studies
Thompson, J. R., Attia, J., & Minelli, C. (2011) · Briefings in Bioinformatics · doi:10.1093/bib/bbr020 · Cited by 251
The Genetic Architecture of Gene Expression in Peripheral Blood
Lloyd‐Jones, L. R., Holloway, A., McRae, A. F., Yang, J., Small, K. S., Zhao, J., Zeng, B., Bakshi, A., Metspalu, A., Dermitzakis, M., et al. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.12.008 · Cited by 246
Sherlock: Detecting Gene-Disease Associations by Matching Patterns of Expression QTL and GWAS
He, X., Fuller, C. K., Song, Y., Meng, Q., Zhang, B., Yang, X., & Li, H. (2013) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.03.022 · Cited by 241
Smith, J. G., Luk, K., Schulz, C.-A., Engert, J. C., Do, R., Hindy, G., Rukh, G., Dufresne, L., Almgren, P., Owens, D. S., et al. (2014) · JAMA · doi:10.1001/jama.2014.13959 · Cited by 238 · cited 1×
“Extension to systematically leaving out combinations of SNPs is possible also (93).”
BackgroundDisentangling molecular relationships with a causal inference test
Millstein, J., Zhang, B., Zhu, J. W., & Schadt, E. E. (2009) · BMC Genetics · doi:10.1186/1471-2156-10-23 · Cited by 234
Gage, S. H., Jones, H. J., Burgess, S., Bowden, J., Smith, G. D., Zammit, S., & Munafo, M. R. (2016) · Psychological Medicine · doi:10.1017/s0033291716003172 · Cited by 219 · cited 1×
“If an association is obtained this would indicate that the instruments were in some way invalid (86).”
ResultSelf-citationWalker, V. M., Smith, G. D., Davies, N. M., & Martin, R. M. (2017) · International Journal of Epidemiology · doi:10.1093/ije/dyx207 · Cited by 216
Epidemiology—is it time to call it a day?
Smith, G. D., & Ebrahim, S. B. J. (2001) · International Journal of Epidemiology · doi:10.1093/ije/30.1.1 · Cited by 214 · cited 1×
“While MR offers an attractive solution to causal inference using observational or non-interventional data, it essentially replaces traditional epidemiological assumptions (9) with other assumptions (Appendix 1 and Table 1).”
BackgroundPrins, B. P., Abbasi, A., Wong, A., Vaez, A., Nolte, I., Franceschini, N., Stuart, P. E., Guterriez Achury, J., Mistry, V., Bradfield, J. P., et al. (2016) · PLOS Medicine · doi:10.1371/journal.pmed.1001976 · Cited by 199 · cited 5×
“Estimating the causal influence of CRP on CHD (69,70) is likely to be quite susceptible to this problem, using all 20 variants from Dehghan et al.”
MethodInfluentialFrom expression QTLs to personalized transcriptomics
Montgomery, S. B., & Dermitzakis, E. T. (2011) · Nature Reviews Genetics · doi:10.1038/nrg2969 · Cited by 194
Corbin, L. J., Richmond, R. C., Wade, K. H., Burgess, S., Bowden, J., Smith, G. D., & Timpson, N. J. (2016) · Diabetes · doi:10.2337/db16-0418 · Cited by 184
Self-citationMendelian randomisation in cardiovascular research: an introduction for clinicians
Bennett, D., & Holmes, M. V. (2017) · Heart · doi:10.1136/heartjnl-2016-310605 · Cited by 181 · cited 1×
“have appeared recently that relate to the scope of MR (10,11), emerging methods (11,12), applications to drug discovery (13– 15), and comparisons to other causal methods (16–18).”
BackgroundNoyce, A. J., Kia, D. A., Hemani, G., Nicolas, A., Price, T. R., Pablo‐Fernández, E. D., Haycock, P., Lewis, P. A., Foltynie, T., Smith, G. D., et al. (2017) · PLoS Medicine · doi:10.1371/journal.pmed.1002314 · Cited by 174 · cited 1×
“a particular exposure is being tested against a particular outcome) a common strategy (92) is to begin with the simplest model, the fixed effects IVW, which has the highest statistical power when all assumptions are met.”
BackgroundSelf-citationTaylor, A. E., Davies, N. M., Ware, J. J., VanderWeele, T., Smith, G. D., & Munafò, M. R. (2014) · Economics & Human Biology · doi:10.1016/j.ehb.2013.12.002 · Cited by 166 · cited 1×
“It is important to note that grouping by an exogenous variable like sex (88) is safer than potentially endogenous covariates because it avoids the possibility of collider bias (89).”
BackgroundSelf-citationHodgkin, J. (1998) · The International Journal of Developmental Biology · doi:10.1387/ijdb.9654038 · Cited by 164 · cited 1×
“in this review, but it has also been termed mediated pleiotropy (106), type II pleiotropy (107), secondary pleiotropy (108),”
BackgroundPlasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysis.
White, J., Sofat, R., Hemani, G., Shah, T., Engmann, J., Dale, C., Shah, S., Kruger, F. A., Giambartolomei, C., Swerdlow, D. I., et al. (2016) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(15)00386-1 · Cited by 132 · cited 1×
“after accounting for directional pleiotropy in the MR-Egger model (58).”
MethodSelf-citationFortune, M. D., Guo, H., Burren, O. S., Schofield, E. C., Walker, N. M., Ban, M., Sawcer, S. J., Bowes, J., Worthington, J., Barton, A. C., et al. (2015) · Nature Genetics · doi:10.1038/ng.3330 · Cited by 130 · cited 2×
“The second alternative mechanism is through distinct causal variants (24), where a SNP exhibits a”
BackgroundPleiotropy-robust Mendelian randomization.
van Kippersluis, H., & Rietveld, C. A. (2018) · International Journal of Epidemiology · doi:10.1093/ije/dyx002 · Cited by 125
On Instrumental Variables Estimation of Causal Odds Ratios
Vansteelandt, S., Bowden, J., Babanezhad, M., & Goetghebeur, E. J. T. (2011) · Statistical Science · doi:10.1214/11-sts360 · Cited by 116 · cited 1×
“Heterogeneity in this case is due to the non-collapsibility of the odds ratio as a summary measure, meaning that each SNP is estimating a slightly different causal parameter (52); • The samples used to estimate the SNP-exposure and SNPoutcome associations are not homogeneous e.”
BackgroundSelf-citationAlcohol intake and cardiovascular risk factors: A Mendelian randomisation study
Cho, Y., Shin, S.-Y., Won, S., Relton, C. L., Smith, G. D., & Shin, M. (2015) · Scientific Reports · doi:10.1038/srep18422 · Cited by 109 · cited 3×
“including the alcohol example, with a perfect no-relevance point (87,91).”
BackgroundBowden, J., Fabiola Del Greco, M., Minelli, C., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/159442 · Cited by 102
Self-citationThe Brisbane Systems Genetics Study: Genetical Genomics Meets Complex Trait Genetics
Powell, J. E., Henders, A. K., McRae, A. F., Caracella, A., Smith, S., Wright, M. J., Whitfield, J. B., Dermitzakis, E. T., Martin, N. G., Visscher, P. M., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0035430 · Cited by 101 · cited 1×
“Typically, the instruments for an exposure are readily available through publications, the GWAS catalog (3), or other resources (20,67,102) in which reliable and reproducible associations are reported.”
BackgroundUsing Multivariable Mendelian Randomization to Disentangle the Causal Effects of Lipid Fractions
Burgess, S., Freitag, D. F., Khan, H., Gorman, D. N., & Thompson, S. G. (2014) · PLoS ONE · doi:10.1371/journal.pone.0108891 · Cited by 95
An analysis of the “pleiotropic” effects of a new lethal mutation in the rat ( Mus norvegicus )
Grüneberg, H. (1938) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.1938.0017 · Cited by 94 · cited 1×
“spurious pleiotropy (109), and in some literature it is not considered to be pleiotropy at all.”
BackgroundSpiller, W., Slichter, D., Bowden, J., & Davey Smith, G. (2018) · International Journal of Epidemiology · doi:10.1093/ije/dyy204 · Cited by 90 · cited 2×
“including the alcohol example, with a perfect no-relevance point (87,91).”
BackgroundSelf-citationHemani, G., Tilling, K. M., & Smith, G. D. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1007149 · Cited by 85
Self-citationle Cessie, S., Debeij, J., Rosendaal, F. R., Cannegieter, S. C., & Vandenbroucke, J. P. (2012) · Epidemiology · doi:10.1097/ede.0b013e318254f5de · Cited by 84
The Genetic Architecture of Gene Expression in Peripheral Blood
Lloyd‐Jones, L. R., Holloway, A., McRae, A. F., Yang, J., Small, K. S., Zhao, W., Zeng, B., Bakshi, A., Metspalu, A., Dermitzakis, M., et al. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.01.026 · Cited by 76 · cited 2×
“as gene expression (19), DNA methylation (20) and protein levels (21) where there is typically a strong genetic association nearby the genomic location of the variable, typically referred Box 1.”
BackgroundMisclassification of the mediator matters when estimating indirect effects
Blakely, T. A., McKenzie, S., & Carter, K. N. (2013) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2012-201813 · Cited by 74 · cited 1×
“erroneous results under some levels of confounding or reverse causation (42), but it is unlikely to lead to the same problems as the heterogeneity-based outlier removal methods.”
BackgroundThe challenging interpretation of instrumental variable estimates under monotonicity
Swanson, S. A., & Hernán, M. A. (2018) · International Journal of Epidemiology · doi:10.1093/ije/dyx038 · Cited by 71 · cited 1×
“in the two-sample setting the causal relationship between the exposure and the outcome is different in each of the samples (53,54).”
BackgroundRichardson, T. G., Zheng, J., Smith, G. D., Timpson, N. J., Gaunt, T. R., Relton, C. L., & Hemani, G. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.09.003 · Cited by 71 · cited 1×
“then test the reverse causal effect (110); if not”
BackgroundSelf-citationBarbeira, A. N., Dickinson, S. P., Bonazzola, R., Zheng, J., Wheeler, H. E., Torres, J. M., Torstenson, E. S., Shah, K. P., Garcia, T., Edwards, T. L., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03621-1 · Cited by 69 · cited 1×
“S-PrediXcan (32) adopts a similar strategy of using an”
MethodUnbiased estimation of odds ratios: combining genomewide association scans with replication studies
Bowden, J., & Dudbridge, F. (2009) · Genetic Epidemiology · doi:10.1002/gepi.20394 · Cited by 61 · cited 1×
“This is particularly problematic when combined with selection bias (78), where the discovery”
BackgroundSelf-citationMendelian randomization with Egger pleiotropy correction and weakly informative Bayesian priors
Schmidt, A. F., & Dudbridge, F. (2017) · International Journal of Epidemiology · doi:10.1093/ije/dyx254 · Cited by 57 · cited 1×
“Schmidt and Dudbrudge (98) put forward a similar idea in the Bayesian MR-Egger estimator (BMRE), in which prior beliefs about the extent of directional pleiotropy can be used to average between IVW and MR-Egger estimates.”
BackgroundMendelian randomization incorporating uncertainty about pleiotropy
Thompson, J. R., Minelli, C., Bowden, J., Fabiola Del Greco, M., Gill, D., Jones, E. M., Shapland, C. Y., & Sheehan, N. A. (2017) · Statistics in Medicine · doi:10.1002/sim.7442 · Cited by 44 · cited 2×
“(2017) (97) applied this idea to MR, using a Bayesian approach to average across three nested models—no pleiotropy (IVW fixed effects), balanced random pleiotropy (IVW random effects) and”
MethodSelf-citationWang, L., & Michoel, T. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005703 · Cited by 38
Koestler, D. C., Chalise, P., Cicek, M. S., Cunningham, J. M., Armasu, S. M., Larson, M. C., CHIEN, J. R., Block, M. S., Kalli, K. R., Sellers, T. A., et al. (2014) · BMC Medical Genomics · doi:10.1186/1755-8794-7-8 · Cited by 36
Pleiotropy-robust Mendelian RandomizationPreprint
van Kippersluis, H., & Rietveld, C. A. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/072603 · Cited by 27
Davies, N. M., Dickson, M., Smith, G. D., Windmeijer, F., & van den Berg, G. J. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/250068 · Cited by 17
Consequences of natural perturbations in the human plasma proteomePreprint
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Fortune, M. D., Guo, H., Burren, O. S., Schofield, E. C., Walker, N. M., Ban, M., Sawcer, S. J., Bowes, J., Worthington, J., Barton, A. C., et al. (2015) · Nature Genetics · doi:10.1038/ng0815-962c · Cited by 2
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Cited by
Sorted by citations
Sanderson, E., Glymour, M. M., Holmes, M. V., Kang, H., Morrison, J., Munafò, M. R., Palmer, T., Schooling, C. M., Wallace, C., Zhao, Q., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00092-5 · Cited by 2,229
Guidelines for performing Mendelian randomization investigations
Burgess, S., Smith, G. D., Davies, N. M., Dudbridge, F., Gill, D., Glymour, M. M., Hartwig, F. P., Holmes, M. V., Minelli, C., Relton, C. L., et al. (2019) · Wellcome Open Research · doi:10.12688/wellcomeopenres.15555.1 · Cited by 1,828
Meta‐analysis and Mendelian randomization: A review
Bowden, J., & Holmes, M. V. (2019) · Research Synthesis Methods · doi:10.1002/jrsm.1346 · Cited by 1,683
“Unfortunately, due to the sheer number of variants that can now be easily included in such MR analyses, often with limited knowledge of their functional role, it is increasingly likely that some do not meet the IV assumptions due to a phenomenon known as horizontal pleiotropy.(14) This occurs when a SNP affects the ou…”
BackgroundSelf-citationGuidelines for performing Mendelian randomization investigations
Burgess, S., Davey Smith, G., Davies, N. M., Dudbridge, F., Gill, D., Glymour, M. M., Hartwig, F. P., Holmes, M. V., Minelli, C., Relton, C. L., et al. (2020) · Wellcome Open Research · doi:10.12688/wellcomeopenres.15555.2 · Cited by 1,273
Mendelian randomisation for mediation analysis: current methods and challenges for implementation
Carter, A. R., Sanderson, E., Hammerton, G., Richmond, R. C., Davey Smith, G., Heron, J., Taylor, A. E., Davies, N. M., & Howe, L. D. (2021) · European Journal of Epidemiology · doi:10.1007/s10654-021-00757-1 · Cited by 1,215
“Bias due to pleiotropy has been discussed extensively in the literature [41, 42].”
Background
Metadata
- Authors
- Hemani, G., Bowden, J., & Davey Smith, G.
- Affiliations
- University of Bristol (GB)
- MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol
- Corresponding author
- Hemani, Gibran
- Venue
- Human Molecular Genetics
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-05-16
- Volume
- 27
- Issue
- R2
- Pages
- R195-R208
- ISSN
- 0964-6906 (ISSN-L)
- 1460-2083
- PMC
- PMC6061876
- OpenAlex
- W2810852503
- Cited by
- 1,496
- References
- 113
- Citations by year
- 2026: 129 · 2025: 266 · 2024: 422 · 2023: 209 · 2022: 169 · 2021: 131 · 2020: 101 · 2019: 62 · 2018: 7
- FWCI
- 10.71
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Human Molecular Genetics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Bristol Research (University of Bristol) (submittedVersion, cc-by)
- Funders
- Wellcome Trust (208806/Z/17/Z)
- Medical Research Council (MC_UU_12013/1)
- 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 › Genetics › Genetics and Physical Performance
- Keywords
- pleiotropy, Mendelian randomization, horizontal pleiotropy, single nucleotide polymorphisms, causal inference, phenome-wide association study, genetic variants
- MeSH terms
- Animals, Humans, Phenotype, Genetic Variation, Reproducibility of Results, Genome, Human, Polymorphism, Single Nucleotide, Genome-Wide Association Study, Mendelian Randomization Analysis, Genetic Pleiotropy
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