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Mendelian randomization
Open accessAbstract
Mendelian randomization (MR) is a term that applies to the use of genetic variation to address causal questions about how modifiable exposures influence different outcomes. The principles of MR are based on Mendel’s laws of inheritance and instrumental variable estimation methods, which enable the inference of causal effects in the presence of unobserved confounding. In this Primer, we outline the principles of MR, the instrumental variable conditions underlying MR estimation and some of the methods used for estimation. We go on to discuss how the assumptions underlying an MR study can be assessed and describe methods of estimation that are robust to certain violations of these assumptions. We give examples of a range of studies in which MR has been applied, the limitations of current methods of analysis and the outlook for MR in the future. The differences between the assumptions required for MR analysis and other forms of epidemiological studies means that MR can be used as part of a triangulation across multiple sources of evidence for causal inference. Mendelian randomization is a technique for using genetic variation to examine the causal effect of a modifiable exposure on an outcome such as disease status. This Primer by Sanderson et al. explains the concepts of and the conditions required for Mendelian randomization analysis, describes key examples of its application and looks towards applying the technique to growing genomic datasets.
Overview
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TL;DR
Mendelian randomization (MR) uses genetic variants as instrumental variables to address causal questions about modifiable exposures while avoiding bias from unobserved confounding. This Primer outlines MR principles, IV assumptions, estimation and sensitivity methods, applications, limitations, and future directions.
Key findings
- MR relies on four conditions: the three instrumental variable conditions plus a point-estimate-identifying condition; a first-stage F-statistic >10 is described as a minimum criterion indicating weak instrument bias is small, though it does not prove instrument validity.
- Summary-data MR commonly uses inverse-variance weighting of variant-specific Wald ratios; pleiotropy-robust methods include weighted median, weighted mode, MR Egger, MR PRESSO, MR RAPS, MR GRAPPLE, and multivariable MR, each imposing different assumptions and often reducing power.
- In applications, MR using ALDH2 and ADH1B variants across average alcohol consumption from 4 g/week to 256 g/week found a dose-response relationship with stroke and no strong protective or detrimental effect on coronary heart disease; MR studies of HDL-C showed no protective effect on CHD, consistent with subsequent RCTs.
- An MR study of childhood and adult body size detected 295 and 557 independent SNPs, respectively; multivariable MR suggested only adult body size had an independent causal effect on CHD and type 2 diabetes, while early-life body size was more strongly inversely related to breast cancer.
Methods
- This is a Primer/review, not a new empirical study; it describes MR principles, IV conditions, individual-level two-stage least squares, summary-level inverse-variance weighting, sensitivity analyses, software packages, and extensions such as bidirectional, multivariable, mediation, and non-linear MR.
- Data discussed include individual-level and summary-level genetic association data, often from GWAS and biobanks such as UK Biobank and China Kadoorie Biobank, used in published MR applications.
- The main analysis is conceptual and methodological synthesis; no new quantitative meta-analysis or primary dataset analysis is reported.
Limitations
- The paper states that MR cannot easily correct confounding of genetic variants and the outcome from population stratification, dynastic effects, and assortative mating; selection bias, survival bias, canalization, and misspecification of the primary phenotype as GWAS grow are also highlighted.
- The paper notes that pleiotropy-robust methods are not truly robust to all types of pleiotropy, often have less statistical power and wider confidence intervals, and that consistent results across methods do not guarantee absence of bias.
- Evident from the text: as a Primer, it does not present a systematic review or exhaustive list of all MR methods, and the described applications are illustrative rather than comprehensive.
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Claims the paper makes
65 Not checked yet · 38 Own interpretation · 34 No basis given · 7 Partly supported · 4 Own method · accuracy not yet measured
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In the paper
“However, they are not sufficient to derive a point estimate of the size of the effect of the exposure on the outcome.28,29”
§ Mendelian randomization > Experimentation > Conditions required for MR estimation
What the sources say
- Supportedcites [28] Swanson, S. A., Hernán, M. A., Miller, M., Robins, J. M. & Richardson, T. S. Partial identification of the average treatment effect using instrumental variables: review of methods for binary instruments, treatments, and outcomes. J. Am. Stat. Assoc. 113, 933–947 (2018). source
The Empirical Example’s Bounds section reports a range for the average treatment effect under the IV assumptions, rather than a point estimate. The Discussion states that additional effect-homogeneity assumptions are necessary for point identification of that effect. (LLM, conf=0.92)
- Partly supportedcites [29] Staley, J. R. & Burgess, S. Semiparametric methods for estimation of a nonlinear exposure–outcome relationship using instrumental variables with application to Mendelian randomization. Genet. Epidemiol. 41, 341–352 (2017). sourceExperimental — accuracy not yet measured
The Introduction (page unavailable) says IV methods typically assume a linear exposure–outcome relationship when estimating a causal effect, indicating that estimation uses assumptions beyond the basic IV conditions. The paper does not explicitly state that the three IV conditions alone are insufficient to derive a point estimate of effect size. (LLM, conf=0.72)
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In the paper
“MR can be used to estimate the proportion of the effect of an exposure on an outcome that is mediated by an intermediate phenotype.104,105”
§ Mendelian randomization > Results > Further extensions of MR methods
What the sources say
- Supportedcites [104] Carter, A. R. et al. Mendelian randomisation for mediation analysis: current methods and challenges for implementation. Eur. J. Epidemiol. 36, 465–478 (2021). source
The Introduction’s ‘Rationale for using Mendelian randomisation in mediation analysis’ explicitly says that multivariable and two-step MR can estimate the proportion mediated. The Methods (‘Statistical analysis’; page unavailable) defines that proportion as the indirect effect divided by the total effect. The paper notes that valid estimates depend on the methods’ assumptions. (LLM, conf=0.94)
- Partly supportedcites [105] Relton, C. L. & Davey Smith, G. Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease. Int. J. Epidemiol. 41, 161–176 (2012). sourceExperimental — accuracy not yet measured
The sections “Mediation of effects of exposures on disease outcomes” and “A two-step epigenetic Mendelian randomization approach” say that two-step MR can examine whether DNA methylation mediates an exposure’s effect on disease. The paper does not say that this approach estimates the proportion of the exposure’s effect mediated by the intermediate phenotype; its discussion of estimating an amount mediated describes a conventional adjustment approach and warns that it can be biased. Page numbers are not available in our copy. (LLM, conf=0.85)
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In the paper
“Although, it is very important to bear in mind that effects of harmful exposures become less evident with increasing age because of selection bias due to the almost inevitable selection only of survivors.145”
§ Mendelian randomization > Applications > Testing causation across the life course
What the sources say
- Partly supportedcites [145] Dixon, P., Hollingworth, W., Harrison, S., Davies, N. M. & Davey Smith, G. Mendelian randomization analysis of the causal effect of adiposity on hospital costs. J. Health Econ. 70, 102300 (2020). sourceExperimental — accuracy not yet measured
The Discussion’s ‘Generalizability of findings’ section says that healthier people were more likely to participate in UK Biobank and that some selection bias is possible. It does not say that harmful exposures become less evident with age, or attribute such a pattern to the selection of survivors. The ‘Potential remaining biases’ section also says the authors cannot separate cohort effects from age effects. (LLM, conf=0.82)
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In the paper
“One colocalization method originally proposed by Plagnol et al 112 frames shared causality as the null hypothesis and rejection of this would indicate violation of IV condition 2, i.e. that there are no common causes of the instrument and the outcome.112–114”
§ Mendelian randomization > Results > Colocalization and MR
What the sources say
- Partly supportedcites [112] Wallace, C. Statistical testing of shared genetic control for potentially related traits. Genet. Epidemiol. 37, 802–813 (2013). sourceExperimental — accuracy not yet measured
The Methods section, “Approaches to Colocalisation Testing” (page unavailable), says that the Plagnol et al. method tests shared causal variation as a null hypothesis. The Results section, “The Case of Multiple Causal Variants” (page unavailable), reports that the test tends to reject that null when distinct causal variants are present. Our copy does not connect rejection to a violation of instrumental-variable condition 2 or discuss common causes of the instrument and outcome. (LLM, conf=0.86)
- Partly supportedcites [113] Pavlides, J. M. W. et al. Predicting gene targets from integrative analyses of summary data from GWAS and eQTL studies for 28 human complex traits. Genome Med. 8, 84–84 (2016). sourceExperimental — accuracy not yet measured
In Construction and content (page unavailable), the paper describes the HEIDI test as having a single shared causal variant as its null hypothesis and distinct causal variants in linkage disequilibrium as its alternative. It does not identify this test as one proposed by Plagnol et al. or state that rejecting the null establishes a violation of IV condition 2 concerning common causes of the instrument and outcome. (LLM, conf=0.85)
- Partly supportedcites [114] Hormozdiari, F. et al. Colocalization of GWAS and eQTL signals detects target genes. Am. J. Hum. Genet. 99, 1245–1260 (2016). sourceExperimental — accuracy not yet measured
The Introduction describes colocalization as determining whether GWAS and eQTL signals share a causal variant, and discusses methods for doing so. It does not say that shared causality is a null hypothesis or that rejecting it indicates a violation of instrumental-variable condition 2; searches for those terms did not find that connection. (LLM, conf=0.82)
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The capacity to use two different samples for MR analyses has dramatically broadened the scope of MR studies because when either the desired exposure or outcome of a study is rare or expensive to measure, it can be difficult to identify a dataset with data on the genetic instrument, exposure and outcome.”
§ Mendelian randomization > Experimentation > Data used for MR estimation
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In the paper
“Bias originating from confounding of the exposure and outcome should not influence the MR estimate.”
§ Mendelian randomization > Experimentation
plain- Asserted: passes — The authors put it forward as true.
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- The authors state this as an assumption: Genetic variants provide a source of exogenous variation in the exposure and can act as an instrumental variable.
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In the paper
“In MR using unrelated individuals, the similarity between the allele groups is not guaranteed as with a well-conducted RCT.”
§ Mendelian randomization > Experimentation
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In the paper
“When individual-level data are used for estimation, the statistical power of an MR analysis (or equivalently, the precision of the estimate that can be derived) increases in proportion to the sample size and the variance in the exposure explained by the genetic instruments.”
§ Mendelian randomization > Experimentation > Data used for MR estimation
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In the paper
“Further, associations between allele distribution and traits can exist at a population level owing to population stratification or assortative mating.”
§ Mendelian randomization > Experimentation
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In the paper
“Similarly, if the genetic variants used in MR influence who has available outcome data — either owing to differential survival or study participation — the MR study will be biased.31”
§ Mendelian randomization > Experimentation > Conditions required for MR estimation
What the sources say
- Not checked yetcites [31] Davey Smith, G. Epigenesis for epidemiologists: does evo-devo have implications for population health research and practice? Int. J. Epidemiol. 41, 236–247 (2012). source
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In the paper
“However, there are important additional assumptions required for causal inference and effect estimation that are different to those used in other causal effect estimation methods.”
§ Mendelian randomization > Introduction
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In the paper
“Using multiple genetic instruments in combination improves statistical power because the combination increases the total fraction of the exposure variance explained by the instruments.27,41”
§ Mendelian randomization > Experimentation > Data used for MR estimation
What the sources say
- Not checked yetcites [27] Hernán, M. A. & Robins, J. M. Instruments for causal inference: an epidemiologist’s dream? Epidemiology 17, 360–372 (2006). source
- Not checked yetcites [41] Holmes, M. V., Richardson, T. G., Ference, B. A., Davies, N. M. & Davey Smith, G. Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development. Nat. Rev. Cardiol. 18, 435–453 (2021). source
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In the paper
“Biases that compromise the interpretation of an RCT can also undermine MR studies.”
§ Mendelian randomization > Experimentation > Conditions required for MR estimation
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In the paper
“Interpretation of results from MR studies relies on four conditions.12,24”
§ Mendelian randomization > Experimentation > Conditions required for MR estimation
What the sources say
- Not checked yetcites [12] Glymour, M. M. Natural experiments and instrumental variable analyses in social epidemiology. Methods Soc. Epidemiol. 1, 429 (2006).
- Not checked yetcites [24] Palmer, T. M. et al. Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat. Methods Med. Res. 21, 223–242 (2011). source
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In the paper
“When summary data are used, the precision of the MR estimate depends on how precisely the associations between the genetic variants and the outcome have been estimated — in other words, how large the standard error of the estimated association is.”
§ Mendelian randomization > Experimentation > Data used for MR estimation
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In the paper
“IV analyses can be applied to any potential source of randomization, including intentionally designed RCTs or quasi-randomization in natural experiments.15,16”
§ Mendelian randomization > Experimentation
What the sources say
- Not checked yetcites [15] Angrist, J. D. & Krueger, A. B. Instrumental variables and the search for identification: from supply and demand to natural experiments. J. Econ. Perspect. 15, 69–85 (2001). source
- Not checked yetcites [16] Rassen, J. A., Brookhart, M. A., Glynn, R. J., Mittleman, M. A. & Schneeweiss, S. Instrumental variables I: instrumental variables exploit natural variation in nonexperimental data to estimate causal relationships. J. Clin. Epidemiol. 62, 1226–1232 (2009). source
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In the paper
“The essence of an MR design is that the association between a genetic variant (G) and an outcome (Y) can be used to test whether and by how much the exposure of interest (X) influences the outcome, provided that the genetic variant is associated with the exposure of interest and has no other source of association with the outcome.1,9”
§ Mendelian randomization > Experimentation
What the sources say
- Not checked yetcites [1] Davey Smith, G. & Ebrahim, S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int. J. Epidemiol. 32, 1–22 (2003). source
- Not checked yetcites [9] Davey Smith, G. & Ebrahim, S. Mendelian randomization: prospects, potentials, and limitations. Int. J. Epidemiol. 33, 30–42 (2004). source
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In the paper
“A potential disadvantage of the ITT estimate, for many questions of substantive interest, is that it does not give the magnitude of the effect of the exposure on the outcome — for example, of CRP on SBP in the above example. It only determines whether or not there is a causal effect.”
§ Mendelian randomization > Experimentation
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In the paper
“When multiple genetic variants can be identified that fulfill the IV conditions, they can be used to improve the statistical power of MR analyses.26,27”
§ Mendelian randomization > Experimentation > Conditions required for MR estimation
What the sources say
- Not checked yetcites [26] Davies, N. M. et al. The many weak instruments problem and Mendelian randomization. Stat. Med. 34, 454–468 (2015). source
- Not checked yetcites [27] Hernán, M. A. & Robins, J. M. Instruments for causal inference: an epidemiologist’s dream? Epidemiology 17, 360–372 (2006). source
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In the paper
“IV analyses are an alternative estimation method that can be used to derive an estimate of the causal effect of the treatment (here, CRP) on the outcome (SBP) by accounting for the size of the association between randomization and CRP.3,4,12–15”
§ Mendelian randomization > Experimentation
What the sources say
- Not checked yetcites [3] Hernán, M. A. & Robins, J. M. Causal Inference: What If (Chapman & Hall/CRC, 2020).
- Not checked yetcites [4] Greenland, S. An introduction to instrumental variables for epidemiologists. Int. J. Epidemiol. 29, 722–729 (2000). source
- Not checked yetcites [12] Glymour, M. M. Natural experiments and instrumental variable analyses in social epidemiology. Methods Soc. Epidemiol. 1, 429 (2006).
- Not checked yetcites [13] Martens, E. P., Pestman, W. R., de Boer, A., Belitser, S. V. & Klungel, O. H. Instrumental variables: application and limitations. Epidemiology 17, 260–267 (2006). source
- Not checked yetcites [14] Lousdal, M. L. An introduction to instrumental variable assumptions, validation and estimation. Emerg. Themes Epidemiol. 15, 1 (2018). source
- Not checked yetcites [15] Angrist, J. D. & Krueger, A. B. Instrumental variables and the search for identification: from supply and demand to natural experiments. J. Econ. Perspect. 15, 69–85 (2001). source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“The particular genetic variants used in the MR may also have effects on the outcome that are not due to the exposure received by the individual.23”
§ Mendelian randomization > Experimentation
What the sources say
- Not checked yetcites [23] Didelez, V., Meng, S. & Sheehan, N. A. Assumptions of IV methods for observational epidemiology. Statist. Sci. 25, 22–40 (2010).
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In the paper
“This requires an additional condition 28 known as a point-estimate identifying condition or fourth IV condition.”
§ Mendelian randomization > Experimentation > Conditions required for MR estimation
What the sources say
- Not checked yetcites [28] Swanson, S. A., Hernán, M. A., Miller, M., Robins, J. M. & Richardson, T. S. Partial identification of the average treatment effect using instrumental variables: review of methods for binary instruments, treatments, and outcomes. J. Am. Stat. Assoc. 113, 933–947 (2018). source
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In the paper
“One further method for identifying potential violations of the IV conditions when the exposure is binary or categorical is using IV inequality constraints.29,67,68”
§ Mendelian randomization > Results > Individual level data
What the sources say
- Not checked yetcites [29] Staley, J. R. & Burgess, S. Semiparametric methods for estimation of a nonlinear exposure–outcome relationship using instrumental variables with application to Mendelian randomization. Genet. Epidemiol. 41, 341–352 (2017). source
- Not checked yetcites [67] Diemer, E. W., Labrecque, J., Tiemeier, H. & Swanson, S. A. Application of the instrumental inequalities to a Mendelian randomization study with multiple proposed instruments. Epidemiology 31, 65–74 (2020). source
- Not checked yetcites [68] Yang, Q., Sanderson, E., Tilling, K., Borges, M. C. & Lawlor, D. A. Exploring and mitigating potential bias when genetic instrumental variables are associated with multiple non-exposure traits in Mendelian randomization. Preprint at medRxiv https://doi.org/10.1101/19009605 (2019). source
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In the paper
“As a general rule, if the first-stage F-statistic is greater than 10, the level of this bias is small.58,59”
§ Mendelian randomization > Results > Individual level data
What the sources say
- Not checked yetcites [58] Stock, J. H. & Yogo, M. Testing for weak instruments in linear IV regression. Report No. 0898-2937 (National Bureau of Economic Research, 2002). source
- Not checked yetcites [59] Brumpton, B. et al. Within-family studies for Mendelian randomization: avoiding dynastic, assortative mating, and population stratification biases. Nat. Commun. 11, 1–13 (2020). source
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In the paper
“Multivariable MR can be used as an approach to address pleiotropic violations of the IV conditions.”
§ Mendelian randomization > Results > Further extensions of MR methods
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In the paper
“If the above conditions are met, the estimates obtained from multivariable MR will be a direct effect of each exposure included on the outcome, given the other exposures included in the estimation.74”
§ Mendelian randomization > Results > Further extensions of MR methods
What the sources say
- Not checked yetcites [74] Chen, L., Davey Smith, G., Harbord, R. M. & Lewis, S. J. Alcohol intake and blood pressure: a systematic review implementing a Mendelian randomization approach. PLoS Med. 5, e52 (2008). source
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In the paper
“Estimation of causal effects using individual level data is most commonly implemented with some version of two-stage least squares (2SLS) estimation (alternative methods include likelihood approaches common in structural equation modelling)54.”
§ Mendelian randomization > Results > Individual level data
What the sources say
- Not checked yetcites [54] Cole, S. R. et al. Illustrating bias due to conditioning on a collider. Int. J. Epidemiol. 39, 417–420 (2009). source
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In the paper
“These tests, such as the Sargan test,66 leverage the expectation that if all proposed IVs are valid, they should deliver identical IV effect estimates.”
§ Mendelian randomization > Results > Individual level data
What the sources say
- Not checked yetcites [66] Glymour, M. M., Tchetgen Tchetgen, E. J. & Robins, J. M. Credible Mendelian randomization studies: approaches for evaluating the instrumental variable assumptions. Am. J. Epidemiol. 175, 332–339 (2012). source
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In the 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.”
§ Mendelian randomization > Results > Individual level data
What the sources say
- Not checked yetcites [60] Hemani, G., Bowden, J. & Davey Smith, G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum. Mol. Genet. 27, R195–R208 (2018). source
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In the paper
“These issues are increasingly important to consider as the sample sizes used in GWAS are increasing, making it more likely that a primary phenotype has been misspecified”
§ Mendelian randomization > Results > Summary level data
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In the paper
“With individual level data and a continuous exposure, non-linear MR can be applied to estimate whether the causal effect of the exposure on the outcome varies across different levels of the exposure.30,108”
§ Mendelian randomization > Results > Further extensions of MR methods
What the sources say
- Not checked yetcites [30] Tyrrell, J. et al. Genetic predictors of participation in optional components of UK Biobank. Nat. Commun. 12, 886 (2021). source
- Not checked yetcites [108] Sun, Y.-Q. et al. Body mass index and all cause mortality in HUNT and UK Biobank studies: linear and non-linear mendelian randomisation analyses. BMJ 364, l1042 (2019). source
plainconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“Another form of pleiotropy arises when the exposure for the MR estimation is misspecified and genetic variants associated with a confounder are used as instruments for the exposure under investigation (see Fig. 3f).”
§ Mendelian randomization > Results > Summary level data
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In the paper
“Violations of the third IV condition can be caused by pleiotropy, where genetic variants have effects on multiple phenotypes.61,62”
§ Mendelian randomization > Results > Individual level data
What the sources say
- Not checked yetcites [61] Davey Smith, G. & Hemani, G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum. Mol. Genet. 23, R89–R98 (2014). source
- Not checked yetcites [62] Burgess, S., Swanson, S. A. & Labrecque, J. A. Are Mendelian randomization investigations immune from bias due to reverse causation? Eur. J. Epidemiol. 36, 253–257 (2021). source
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In the paper
“Recent methods such as sisVIVE71 and adaptive LASSO72 provide MR estimates that are robust to pleiotropy under certain assumptions.”
§ Mendelian randomization > Results > Individual level data
What the sources say
- Not checked yetcites [71] Windmeijer, F., Farbmacher, H., Davies, N. & Davey Smith, G. On the use of the lasso for instrumental variables estimation with some invalid instruments. J. Am. Stat. Assoc. 114, 1339–1350 (2019). source
- Not checked yetcites [72] Jiang, L. et al. Constrained instruments and their application to Mendelian randomization with pleiotropy. Genet. Epidemiol. 43, 373–401 (2019). source
plain- Asserted: passes — The authors put it forward as true.
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- The authors state this as an assumption: sisVIVE and adaptive LASSO assume that multiple IVs are available and that a majority or plurality of the proposed IVs are valid.
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In the paper
“If the IV conditions are satisfied, there should be no association between genetic variants for alcohol consumption and the outcome under consideration among women in the previous example.”
§ Mendelian randomization > Results > Individual level data
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In the paper
“Colocalization analysis can be used to determine whether two traits share causal variants in a single genetic region, without prior knowledge of which variant is causal for either trait.”
§ Mendelian randomization > Results > Colocalization and MR
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In the paper
“The proposed genetic instrument should be unassociated with the outcome in the environmental setting where it is not associated with the exposure unless there are pleiotropic pathways from the genetic variant to the outcome.”
§ Mendelian randomization > Results > Individual level data
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In the paper
“However if any SNPs violate IV conditions 2 or 3 (if any of the component SNPs influence the outcome through a mechanism other than the exposure of interest) then the allele score will also violate that condition.”
§ Mendelian randomization > Results > Individual level data
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In the paper
“In either of these situations, controlling for such a covariate could bias the MR effect estimate.55–57”
§ Mendelian randomization > Results > Individual level data
What the sources say
- Not checked yetcites [55] Munafò, M. R., Tilling, K., Taylor, A. E., Evans, D. M. & Davey Smith, G. Collider scope: when selection bias can substantially influence observed associations. Int. J. Epidemiol. 47, 226–235 (2018). source
- Not checked yetcites [56] Hernán, M. A., Hernández-Díaz, S. & Robins, J. M. A structural approach to selection bias. Epidemiology 15, 615–625 (2004). source
- Not checked yetcites [57] Staiger, D. & Stock, J. H. Instrumental variables regression with weak instruments. Report No. 0898-2937 (National Bureau of Economic Research, 1994). source
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In the paper
“Consequently, consistent results across a range of methods is not a guarantee that results are free from bias.”
§ Mendelian randomization > Results > Summary level data
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In the paper
“This, together with the tendency for neighbouring genetic variants to be correlated owing to LD, could lead to the violation of IV condition 2 where different neighbouring variants happen to be causally associated to the exposure and outcome through different pathways (Fig 6a).”
§ Mendelian randomization > Results > Colocalization and MR
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In the paper
“Additionally, linkage disequilibrium (LD) [G] means that the effects of neighbouring genetic variants can introduce additional associations between the variant of interest — and thus the exposure it relates to — and the outcome, creating a bias analogous to that caused by pleiotropy.”
§ Mendelian randomization > Results > Individual level data
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In the paper
“Bias due to overfitting is a concern when summary level data are used for estimation if the effect of the SNP on the exposure is in a dataset that overlaps with the dataset used to estimate the SNP−outcome association.”
§ Mendelian randomization > Experimentation > Instrument Selection
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In the paper
“Steiger filtering attempts to correct for this misspecification by removing SNPs that explain more variation in the outcome than the exposure.94”
§ Mendelian randomization > Results > Summary level data
What the sources say
- Not checked yetcites [94] Brown, B. C. & Knowles, D. A. Welch-weighted Egger regression reduces false positives due to correlated pleiotropy in Mendelian randomization. Am. J. Hum. Genet. 108, 2319–2335 (2021). source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“When many SNPs are used as IVs, each with a very small effect, this can create a non-trivial bias towards the conventional effect estimate, known as weak instrument bias43.”
§ Mendelian randomization > Experimentation > Instrument Selection
What the sources say
- Not checked yetcites [43] Burgess, S., Davies, N. M. & Thompson, S. G. Bias due to participant overlap in two-sample Mendelian randomization. Genet. Epidemiol. 40, 597–608 (2016). source
plaindirect- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“The first IV condition can be tested using a first-stage F-statistic [G], which tests the association between the SNPs and the exposure.”
§ Mendelian randomization > Results > Individual level data
plaincorrelational- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“When using individual data, overlap between the dataset used for instrument discovery and the dataset used for estimation can introduce a bias known as ‘winners curse’.”
§ Mendelian randomization > Experimentation > Instrument Selection
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“If all of the IV conditions are satisfied, this estimate will differ from a univariable MR estimate where all or part of the effect of the exposure on the outcome acts through a mediating phenotype included in the multivariable MR estimation.104”
§ Mendelian randomization > Results > Further extensions of MR methods
What the sources say
- Not checked yetcites [104] Carter, A. R. et al. Mendelian randomisation for mediation analysis: current methods and challenges for implementation. Eur. J. Epidemiol. 36, 465–478 (2021). source
plaindirect- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“With individual-level data, it is possible to test for interactions between two exposures using MR.”
§ Mendelian randomization > Results > Further extensions of MR methods
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This explicit use of LD means colocalization can be used to check for the violation of IV condition 2 in the form shown in Fig 6a (and Box 2).”
§ Mendelian randomization > Results > Colocalization and MR
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In the paper
“It should be noted that this approach can have low power compared to the inclusion of an interaction term in a 2SLS regression.111”
§ Mendelian randomization > Results > Further extensions of MR methods
What the sources say
- Not checked yetcites [111] Plagnol, V., Smyth, D. J., Todd, J. A. & Clayton, D. G. Statistical independence of the colocalized association signals for type 1 diabetes and RPS26 gene expression on chromosome 12q13. Biostatistics 10, 327–334 (2009). source
plaindirect- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Unlike in MR with multiple IVs, the majority of multiple neighbouring genetic variants considered in this analysis are expected to be associated with either trait solely through LD with one or a small number of causal variants in the region.”
§ Mendelian randomization > Results > Colocalization and MR
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Additionally, these estimation methods will not necessarily identify violations of any IV conditions that are not due to pleiotropy of the nature interrogated by the method.”
§ Mendelian randomization > Results > Summary level data
hedged- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Each of these methods imposes strong assumptions on the nature of the pleiotropy.”
§ Mendelian randomization > Results > Summary level data
plain- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“A final important source of bias in MR, and indeed all studies of observational data, is selection bias.64,65”
§ Mendelian randomization > Results > Individual level data
What the sources say
- Not checked yetcites [64] Hughes, R. A., Davies, N. M., Davey Smith, G. & Tilling, K. Selection bias when estimating average treatment effects using one-sample instrumental variable analysis. Epidemiology 30, 350–357 (2019). source
- Not checked yetcites [65] Sargan, J. D. The estimation of economic relationships using instrumental variables. Econometrica 26, 393–415 (1958). source
plain- Asserted: passes — The authors put it forward as true.
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Although multiple exposures can be included in a multivariable MR, there must be at least as many genetic variants or scores included as instruments as there are exposures.”
§ Mendelian randomization > Results > Further extensions of MR methods
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Another type of bias that can arise in MR studies that cannot be easily corrected for is selection bias64 In an MR study, an example of selection bias would be if an individual’s exposure and outcome values affected their participation.57”
§ Mendelian randomization > Limitations and Optimizations
What the sources say
- Not checked yetcites [64] Hughes, R. A., Davies, N. M., Davey Smith, G. & Tilling, K. Selection bias when estimating average treatment effects using one-sample instrumental variable analysis. Epidemiology 30, 350–357 (2019). source
- Not checked yetcites [57] Staiger, D. & Stock, J. H. Instrumental variables regression with weak instruments. Report No. 0898-2937 (National Bureau of Economic Research, 1994). source
plainconditional- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“An important limitation of MR studies is potential confounding of the genetic variants and the outcome (violation of the second IV condition; see Box 2).”
§ Mendelian randomization > Limitations and Optimizations
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: Genetic variants are generally fixed at conception.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, using a genetic instrument for treatment as part of a drug-target MR means that the underlying magnitude of the effect on which potential genetic effect modifiers are investigated is very small and thus very large sample sizes are needed to identify effects.”
§ Mendelian randomization > Applications > Testing treatment response factors
plaindirect- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: The underlying magnitude of the effect in drug-target MR is very small.
In the paper
“This approach benefits from lack of confounding by indication, compared to a conventional pharmacoepidemiology study design.151”
§ Mendelian randomization > Applications > Testing treatment response factors
What the sources say
- Not checked yetcites [151] Munafò, M. R. et al. A manifesto for reproducible science. Nat. Hum. Behav. 1, 0021 (2017). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Polygenic scores did not identify any effect-modifying genetic groups; however, a single variant (rs162724) proximal to the glutamate receptor gene GRM7 and previously associated with major depressive disorder was found to potentially be of interest.”
§ Mendelian randomization > Applications > Testing treatment response factors
What the sources say
- Not checked yetcites [148] Kyriacou, D. N. & Lewis, R. J. Confounding by indication in clinical research. JAMA 316, 1818–1819 (2016). source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“MR studies have provided accumulating evidence against the observational results above.136–140”
§ Mendelian randomization > Applications > Cholesterol and coronary heart disease
What the sources say
- Not checked yetcites [136] Do, R. et al. Common variants associated with plasma triglycerides and risk for coronary artery disease. Nat. Genet. 45, 1345–1352 (2013). source
- Not checked yetcites [137] Holmes, M. V. et al. Mendelian randomization of blood lipids for coronary heart disease. Eur. Heart J. 36, 539–550 (2015). source
- Not checked yetcites [138] Holmes, M. V. & Davey Smith, G. REVEALing the effect of CETP inhibition in cardiovascular disease. Nat. Rev. Cardiol. 14, 635–636 (2017). source
- Not checked yetcites [139] Barter, P. J. et al. Effects of torcetrapib in patients at high risk for coronary events. N. Engl. J. Med. 357, 2109–2122 (2007). source
- Not checked yetcites [140] Riaz, H. et al. Effects of high-density lipoprotein targeting treatments on cardiovascular outcomes: a systematic review and meta-analysis. Eur. J. Prev. Cardiol. 26, 533–543 (2019). source
plain- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“One solution that can account for confounding owing to dynastic effects and assortative mating is the use of family data to conduct the MR analyses.167,168”
§ Mendelian randomization > Limitations and Optimizations
What the sources say
- Not checked yetcites [167] Taylor, A. E. et al. Exploring the association of genetic factors with participation in the Avon Longitudinal Study of Parents and Children. Int. J. Epidemiol. 47, 1207–1216 (2018). source
- Not checked yetcites [168] Fry, A. et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am. J. Epidemiol. 186, 1026–1034 (2017). source
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This indicates that the association observed in the more traditional observational studies was likely to have occurred owing to confounding.”
§ Mendelian randomization > Applications > Cholesterol and coronary heart disease
hedgedconditional- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“For example, there is clear evidence from both MR and the natural experiment of an increase in the school leaving age that an increase in the number of years in education has a causal protective effect on health behaviours such as smoking.156–160”
§ Mendelian randomization > Reproducibility and data deposition
What the sources say
- Not checked yetcites [156] Tillmann, T. et al. Education and coronary heart disease: mendelian randomisation study. BMJ 358, j3542 (2017). source
- Not checked yetcites [157] Davies, N. M., Dickson, M., Davey Smith, G., Windmeijer, F. & van den Berg, G. J. The causal effects of education on adult health, mortality and income: evidence from Mendelian randomization and the raising of the school leaving age. Preprint at SSRN https://doi.org/10.2139/ssrn.3390179 (2019). source
- Not checked yetcites [158] Baldwin, J., Pingault, J.-B., Schoeler, T., Sallis, H. M. & Munafo, M. R. Protecting against researcher bias in secondary data analysis: challenges and solutions. Eur. J. Epidemiol. 37, 1–10 (2022). source
- Not checked yetcites [159] Sallis, H. Triangulation protocol; intergenerational effects of parental substance use on child substance use and mental health outcomes. Preprint at https://osf.io/s6jv4/ (2021).
- Not checked yetcites [160] Hartwig, F. P., Davies, N. M. & Davey Smith, G. Bias in Mendelian randomization due to assortative mating. Genet. Epidemiol. 42, 608–620 (2018). source
boosteddirect- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
In the paper
“A key limiting factor for within-family MR is the lack of available data and the low power of these studies as a result; however, a GWAS of family data for a range of phenotypes has recently been published, enabling further within-family MR in the future.169”
§ Mendelian randomization > Limitations and Optimizations
What the sources say
- Not checked yetcites [169] Pirastu, N. et al. Genetic analyses identify widespread sex-differential participation bias. Nat. Genet. 53, 663–671 (2021). source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, it is hard to differentiate whether failure to reject the null hypothesis indicates that IV condition 2 is satisfied or a lack of power in the colocalization test.”
§ Mendelian randomization > Results > Colocalization and MR
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When these phenotypes are partially determined by genetic variants, this will also induce an association between those genetic variants and participation.”
§ Mendelian randomization > Limitations and Optimizations
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Within-family MR using sibling pairs will also account for any factors acting at a population level that affect siblings equally, such as population stratification.”
§ Mendelian randomization > Limitations and Optimizations
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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In the paper
“Polygenic scores did not identify any effect-modifying genetic groups; however, a single variant (rs162724) proximal to the glutamate receptor gene GRM7 and previously associated with major depressive disorder was found to potentially be of interest.”
§ Mendelian randomization > Applications > Testing treatment response factors
What the sources say
- Not checked yetcites [148] Kyriacou, D. N. & Lewis, R. J. Confounding by indication in clinical research. JAMA 316, 1818–1819 (2016). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This can be done in conventional pharmacogenetic studies and RCTs, although the risk of bias in the former and the sample size constraints of the latter mean that neither provide a reliable means of assessing interactions between an individual’s genotype and treatment response.”
§ Mendelian randomization > Applications > Testing treatment response factors
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If these variants are used as instruments for CRP or vitamin D, they will produce highly misleading results.”
§ Mendelian randomization > Outlook
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Adiposity is a confounder of the observational association of CRP and vitamin D with health outcomes.
None of the sources cited for this claim have been checked yet.
In the paper
“For example, a large-effect genetic variant influencing alcohol consumption that has been of considerable value in MR studies of the effects of alcohol75,130 is only prevalent in East Asian populations.”
§ Mendelian randomization > Outlook
What the sources say
- Not checked yetcites [75] Spiller, W., Hartwig, F. P., Sanderson, E., Davey Smith, G. & Bowden, J. Interaction-based Mendelian randomization with measured and unmeasured gene-by-covariate interactions. Preprint at medRxiv https://doi.org/10.1101/2020.07.27.20162909 (2020). source
- Not checked yetcites [130] Goldstein, J. L. & Brown, M. S. A century of cholesterol and coronaries: from plaques to genes to statins. Cell 161, 161–172 (2015). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, MR studies usually rely on GWAS of the initial development of disease for their outcome data.”
§ Mendelian randomization > Outlook
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This type of survival bias will affect observational studies of the same research question in similarly aged populations, so is not an obvious explanation for discrepancies between MR and conventional results.”
§ Mendelian randomization > Limitations and Optimizations
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is plausible that in many cases, factors that cause a disease do not relate to its progression once it is established.”
§ Mendelian randomization > Outlook
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It can also restrict the scope of MR studies, as some forms of genetic variation are restricted to particular populations.”
§ Mendelian randomization > Outlook
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Canalization can occur when polymorphic phenotypes expressed during fetal development lead to the development of compensating pathways to mitigate the effects of that expression. 1,178,179”
§ Mendelian randomization > Limitations and Optimizations
What the sources say
- Not checked yetcites [1] Davey Smith, G. & Ebrahim, S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int. J. Epidemiol. 32, 1–22 (2003). source
- Not checked yetcites [178] Gu, Z. et al. Role of duplicate genes in genetic robustness against null mutations. Nature 421, 63–66 (2003). source
- Not checked yetcites [179] Hemani, G. et al. Automating Mendelian randomization through machine learning to construct a putative causal map of the human phenome. Preprint at bioRxiv https://doi.org/10.1101/173682 (2017). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Such compensation would potentially limit the ability of MR to identify the causal effect of the change in the exposure as the effect of a genetically induced change from conception would differ to the effect of a change in later life.”
§ Mendelian randomization > Limitations and Optimizations
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Indeed, many such papers simply report MR estimates obtained from applying open-access software to open-access data and in these cases the analyses have, in essence, already been conducted by automated tools — an observation detailed in a recent preprint article.182”
§ Mendelian randomization > Outlook
What the sources say
- Not checked yetcites [182] Paternoster, L., Tilling, K. & Davey Smith, G. Genetic epidemiology and Mendelian randomization for informing disease therapeutics: conceptual and methodological challenges. PLoS Genet. 13, e1006944 (2017). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 54
- Probably not a substantial claim. 23
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“However, population stratification, dynastic effects and assortative mating all induce bias by creating an artefactual relation at the population level between the genetic variants and the outcome, violating the second IV condition.65,163–166”
§ Mendelian randomization > Limitations and Optimizations
What the sources say
- Not checked yetcites [65] Sargan, J. D. The estimation of economic relationships using instrumental variables. Econometrica 26, 393–415 (1958). source
- Partly supportedcites [163] Minică, C. C., Boomsma, D. I., Dolan, C. V., de Geus, E. & Neale, M. C. Empirical comparisons of multiple Mendelian randomization approaches in the presence of assortative mating. Int. J. Epidemiol. 49, 1185–1193 (2020). sourceExperimental — accuracy not yet measured
The Introduction (page unavailable) says cross-trait assortative mating, dynastic effects and population structure may induce genetic correlation between height and education, and defines the second IV condition as independence of the instrument from confounders. The Discussion (page unavailable) also identifies bias from dynastic effects. The source supports these as potential sources of bias, but does not state that all three invariably induce bias through an artefactual population-level variant–outcome relation. (LLM, conf=0.84)
- Not checked yetcites [164] Davies, N. M. et al. Within family Mendelian randomization studies. Hum. Mol. Genet. 28, R170–R179 (2019). source
- Partly supportedcites [165] Minică, C. C., Dolan, C. V., Boomsma, D. I., de Geus, E. & Neale, M. C. Extending causality tests with genetic instruments: an integration of Mendelian randomization with the classical twin design. Behav. Genet. 48, 337–349 (2018). sourceExperimental — accuracy not yet measured
The Discussion (page unavailable) says assortative mating may violate the model’s assumptions and describes paths from the instrument to the outcome through confounders. Our copy does not identify population stratification or dynastic effects as sources of this bias, nor does it establish that all three mechanisms create the claimed population-level relation or violate the second IV condition. (LLM, conf=0.86)
- Not checked yetcites [166] Howe, L. J. et al. Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects. Nat. Genet. (in the press).
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Genetic variants are generally fixed at conception.
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Triangulation of evidence from multiple methodologies — using different methodologies that are subject to different sources and directions of potential bias — can help to identify bias in MR studies.6,7,155 Alignment of results across these different methodologies can improve confidence in an initial causal interpretation.”
§ Mendelian randomization > Reproducibility and data deposition
What the sources say
- Supportedcites [6] Munafò, M. R., Higgins, J. P. & Davey Smith, G. Triangulating evidence through the inclusion of genetically informed designs. Cold Spring Harb. Perspect. Med. 11, a040659 (2021). source
Split into 2 atoms — 2 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
- Not checked yetcites [7] Lawlor, D. A., Tilling, K. & Davey Smith, G. Triangulation in aetiological epidemiology. Int. J. Epidemiol. 45, 1866–1886 (2017).
- Partly supportedcites [155] Davies, N. M. et al. Multivariable two-sample Mendelian randomization estimates of the effects of intelligence and education on health. eLife 8, e43990 (2019). sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Papers reporting two-sample MR have grown rapidly over recent years and now constitute a large majority of published studies.8,81”
§ Mendelian randomization > Outlook
What the sources say
- Supportedcites [8] Richmond, R. C. & Davey Smith, G. Mendelian randomization: concepts and scope. Cold Spring Harb. Perspect. Med. https://doi.org/10.1101/cshperspect.a040501 (2022). source
The Conclusions describe a rapid expansion of MR publications, predominantly using the two-sample approach. The Figure 7 caption also identifies a count of one- and two-sample MR papers from 2011–2020, consistent with the claim’s time frame. (LLM, conf=0.88)
- Partly supportedcites [81] Bowden, J. et al. Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the radial plot and radial regression. Int. J. Epidemiol. 47, 1264–1278 (2018). sourceExperimental — accuracy not yet measured
The Background describes two-sample summary-data MR as a study design “gaining in popularity,” supporting the claim that its use has grown. It does not say that growth was rapid or that two-sample MR constitutes a large majority of published studies. (LLM, conf=0.88)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
In the paper
“MR can be applied using any genetic variation that satisfies the requirements of an IV,5 although it is most often implemented using single nucleotide polymorphisms (SNPs).”
§ Mendelian randomization > Introduction
What the sources say
- Not checked yetcites [5] Zuccolo, L. & Holmes, M. V. Commentary: Mendelian randomization-inspired causal inference in the absence of genetic data. Int. J. Epidemiol. 46, 962–965 (2017).
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Using genetic variants in this way, MR avoids bias from unobserved confounding of the exposure and outcome.”
§ Mendelian randomization > Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
- The authors state this as an assumption: Genetic variants provide a source of exogenous variation in the exposure and can act as an instrumental variable.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Genetic variants typically only explain a small proportion of the variation in the relevant phenotype; as a result, low statistical power and imprecise effect estimates are common in MR studies and well-powered studies usually require large datasets.”
§ Mendelian randomization > Experimentation > Data used for MR estimation
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“MR was first proposed using the family data where the difference in alleles between siblings is random, however data limitations mean most MR is conducted using data on unrelated individuals.22”
§ Mendelian randomization > Experimentation
What the sources say
- Not checked yetcites [22] Swanson, S. A., Tiemeier, H., Ikram, M. A. & Hernán, M. A. Nature as a trialist? Deconstructing the analogy between Mendelian randomization and randomized trials. Epidemiology 28, 653–659 (2017). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“IV analysis was first proposed a century ago and is an approach to causal inference that uses an IV, or “instrument” — which is related to the exposure but not the outcome of interest other than through its association with the exposure — to make causal effect estimates in the presence of unobserved confounding of the exposure and the outcome.”
§ Mendelian randomization > Introduction
What the sources say
- Not checked yetcites [2] Angrist, J. D., Imbens, G. W. & Rubin, D. B. Identification of causal effects using instrumental variables. J. Am. Stat. Assoc. 91, 444–455 (1996). source
- Not checked yetcites [3] Hernán, M. A. & Robins, J. M. Causal Inference: What If (Chapman & Hall/CRC, 2020).
- Not checked yetcites [4] Greenland, S. An introduction to instrumental variables for epidemiologists. Int. J. Epidemiol. 29, 722–729 (2000). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In RCTs, randomly assigned treatment therefore evaluates the effect of treatment on the outcome, whereas in MR, a genetic variant is treated as a naturally occurring form of randomization.”
§ Mendelian randomization > Experimentation
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“MR is especially appealing because it relies on different assumptions to conventional observational studies and therefore circumvents some of their common biases.8”
§ Mendelian randomization > Introduction
What the sources say
- Not checked yetcites [8] Richmond, R. C. & Davey Smith, G. Mendelian randomization: concepts and scope. Cold Spring Harb. Perspect. Med. https://doi.org/10.1101/cshperspect.a040501 (2022). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Causal effect estimates from MR can be evaluated within a triangulation of evidence framework, which involves interpreting findings alongside results from complementary approaches that rely on different assumptions.”
§ Mendelian randomization > Introduction
What the sources say
- Not checked yetcites [6] Munafò, M. R., Higgins, J. P. & Davey Smith, G. Triangulating evidence through the inclusion of genetically informed designs. Cold Spring Harb. Perspect. Med. 11, a040659 (2021). source
- Not checked yetcites [7] Lawlor, D. A., Tilling, K. & Davey Smith, G. Triangulation in aetiological epidemiology. Int. J. Epidemiol. 45, 1866–1886 (2017).
- Not checked yetcites [8] Richmond, R. C. & Davey Smith, G. Mendelian randomization: concepts and scope. Cold Spring Harb. Perspect. Med. https://doi.org/10.1101/cshperspect.a040501 (2022). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“MR studies — especially two-sample studies using previously published summary-level genetic association data — provide a rapid and affordable approach to evaluating causal questions.”
§ Mendelian randomization > Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The first three of these conditions are commonly referred to as the conditions for a valid instrumental variable and are required for any IV analysis to test whether the exposure has a causal effect on the outcome.”
§ Mendelian randomization > Experimentation > Conditions required for MR estimation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The rationale of MR studies parallels that of RCTs in which randomization influences the treatment received by participants, is not associated with any confounders of the treatment and outcome and has no other plausible mechanism to influence health outcomes other than through treatment (see Fig. 1).”
§ Mendelian randomization > Experimentation
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The above example highlights an important difference between RCTs and MR: RCTs estimate the effect of a particular intervention or treatment over the timeframe of the study, whereas MR estimates the lifetime effects.21”
§ Mendelian randomization > Experimentation
What the sources say
- Not checked yetcites [21] Morris, T. T., Heron, J., Sanderson, E., Davey Smith, G. & Tilling, K. Interpretation of Mendelian randomization using one measure of an exposure that varies over time. Preprint at medRxiv https://doi.org/10.1101/2021.11.18.21266515 (2021). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Two methods, MR GxE and MR GENIUS, have extended and formalised these concepts and enable the estimation of causal effects in more general settings.”
§ Mendelian randomization > Results > Individual level data
What the sources say
- Not checked yetcites [76] Spiller, W., Slichter, D., Bowden, J. & Davey Smith, G. Detecting and correcting for bias in Mendelian randomization analyses using gene-by-environment interactions. Int. J. Epidemiol. 48, 702–712 (2019). source
- Not checked yetcites [77] Tchetgen Tchetgen, E. J., Sun, B. & Walter, S. The GENIUS approach to robust Mendelian randomization inference. Stat. Sci. 36, 443–464 (2019).
- Not checked yetcites [78] Burgess, S., Dudbridge, F. & Thompson, S. G. Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods. Stat. Med. 35, 1880–1906 (2016). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“An alternative approach is to split the allele scores for each exposure into high and low values and compare outcomes across the resulting four groups by dividing participants up based on their score for each exposure, mimicking a 2x2 factorial randomised trial.”
§ Mendelian randomization > Results > Further extensions of MR methods
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The bias in this situation is equivalent to that caused by pleiotropy (see Fig. 3) and although it is unlikely that this pattern will arise at many independent genetic locations in MR studies with multiple IVs, it should be a consideration in single-IV studies.”
§ Mendelian randomization > Results > Colocalization and MR
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although this involves an implicit assumption of directionality in its interpretation, the test is not dependent on this assumption and indeed a single pleiotropic variant would satisfy the statistical definition of a shared causal variants (Fig 6b).”
§ Mendelian randomization > Results > Colocalization and MR
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“As with individual level data analysis, IV conditions need to be assessed for any summary-data MR.”
§ Mendelian randomization > Results > Summary level data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A number of different methods are available to correct for horizontal pleiotropy — a violation of the third IV condition — under different assumptions about the causal structure of that pleiotropy.”
§ Mendelian randomization > Results > Summary level data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Although the second and third IV conditions cannot be proven to be true, they can sometimes be disproven.”
§ Mendelian randomization > Results > Individual level data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, a few methods should be selected based on the most plausible assumptions for the application in question and used alongside an IVW MR estimation to perform a sensitivity analysis; this can determine how robust MR results are to the assumption that genetic variants have no pleiotropic effects on the outcome under different alternative specifications.”
§ Mendelian randomization > Results > Summary level data
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“None of the methods described above are truly robust to all types of pleiotropy and each imposes different assumptions on the nature of the pleiotropy and how the pleiotropic effects are accounted for.”
§ Mendelian randomization > Results > Summary level data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Results from bidirectional MR studies should be interpreted with caution. Evidence of an effect in both directions could indicate a true bidirectional relationship between the exposures or be a product of bias from horizontal pleiotropic effects in the variants, misspecification of the primary phenotype, or a violation of the second IV condition owing to confounding of genetic variants and outcome”
§ Mendelian randomization > Results > Further extensions of MR methods
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Traditionally, summary-data MR is visualized as a scatter plot plotting associations of the variant and exposure against associations of the variant and outcome (Fig 4a, b); however, this can limit the identification of outliers.”
§ Mendelian randomization > Results > Summary level data
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Standard MR only provides a single effect estimate, which may not be informative if the effect of the exposure varies in a non-linear way — for example, a dose-response curve.”
§ Mendelian randomization > Results > Further extensions of MR methods
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Weighted scores provide increased instrument strength and power, although there are cases in which the unweighted approach is preferred — for example, if the definition of the exposure in the discovery dataset differs from the exposure variable in the estimation data.”
§ Mendelian randomization > Results > Individual level data
What the sources say
- Not checked yetcites [26] Davies, N. M. et al. The many weak instruments problem and Mendelian randomization. Stat. Med. 34, 454–468 (2015). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, many methods have less statistical power than conventional MR, leading to very wide confidence intervals.”
§ Mendelian randomization > Results > Summary level data
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The inclusion of these additional terms decreases the statistical power for detecting an effect and should be limited to a single interaction.”
§ Mendelian randomization > Results > Further extensions of MR methods
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Non-linear MR has supported this, although it has also suggested that the J-shape could be caused by the relationship between BMI and mortality risk differing for ever-smokers [G] and never-smokers.109”
§ Mendelian randomization > Results > Further extensions of MR methods
What the sources say
- Not checked yetcites [109] North, T.-L. et al. Using genetic instruments to estimate interactions in Mendelian randomization studies. Epidemiology 30, e33–e35 (2019). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In bidirectional MR, two MR analyses are conducted on the same pair of phenotypes by reversing the exposure and the outcome. This method can be used to establish the direction of effect between two variables.”
§ Mendelian randomization > Results > Further extensions of MR methods
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Multiple, correlated exposures can be included in a multivariable MR; however, including multiple exposures can reduce power and potentially instrument strength and thus the benefit of adding extra exposures must be considered carefully.”
§ Mendelian randomization > Results > Further extensions of MR methods
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Ever larger GWAS have now provided evidence that hundreds of genetic variants may be associated with many human phenotypes.”
§ Mendelian randomization > Results > Colocalization and MR
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Multivariable MR is an extension of standard MR that includes multiple exposures, predicted by a set of genetic variants used as instruments.”
§ Mendelian randomization > Results > Further extensions of MR methods
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“If many SNPs that each only have a small effect on the exposure are being used, combining them into a single score can increase the power of the analysis and reduce the risk of bias from many weak instruments.27”
§ Mendelian randomization > Results > Individual level data
What the sources say
- Not checked yetcites [27] Hernán, M. A. & Robins, J. M. Instruments for causal inference: an epidemiologist’s dream? Epidemiology 17, 360–372 (2006). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“This correlation between genetic variants and the outcome can potentially affect most (or all) of the genetic variants used as instruments; it is therefore not easy to correct for with current MR methods as most assume that the majority of genetic variants satisfy all of the IV conditions.61”
§ Mendelian randomization > Limitations and Optimizations
What the sources say
- Not checked yetcites [61] Davey Smith, G. & Hemani, G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum. Mol. Genet. 23, R89–R98 (2014). source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Pre-registration of study protocols has not been widely adopted in observational epidemiology, although it could in principle be applied and help protect against bias, such as publication bias against null results or findings that do not fit with the anticipated conclusion.161,162”
§ Mendelian randomization > Reproducibility and data deposition
What the sources say
- Not checked yetcites [161] Brumpton, B. et al. Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses. Nat. Commun. 11, 3519 (2020). source
- Not checked yetcites [162] Morris, T. T., Davies, N. M., Hemani, G. & Davey Smith, G. Population phenomena inflate genetic associations of complex social traits. Sci. Adv. 6, eaay0328 (2020). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition to physiological consequences, body weight has social consequences such increasing exposure to stigma and discrimination and these MR analyses include the consequences of all such mechanisms for hospitalization costs.”
§ Mendelian randomization > Applications > Estimation of healthcare costs
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Within-family MR requires data from either pairs of siblings or mother–father–child trios and allows for estimation of causal effects using MR after family-level structure has been taken into account.164,167”
§ Mendelian randomization > Limitations and Optimizations
What the sources say
- Not checked yetcites [164] Davies, N. M. et al. Within family Mendelian randomization studies. Hum. Mol. Genet. 28, R170–R179 (2019). source
- Not checked yetcites [167] Taylor, A. E. et al. Exploring the association of genetic factors with participation in the Avon Longitudinal Study of Parents and Children. Int. J. Epidemiol. 47, 1207–1216 (2018). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, available evidence from MR methods that facilitate estimation in the presence of unobserved confounding — assuming no selection bias — do not support the conclusion that the consumption of a moderate amount of alcohol may lower vascular disease risk and identify alcohol consumption as a factor linked to increased likelihood of ischemic stroke”
§ Mendelian randomization > Applications > Estimation when trials are unfeasible
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
- The authors state this as an assumption: The conclusion about alcohol consumption and vascular disease risk assumes no selection bias.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Open research can increase the robustness of data through allowing greater scrutiny of data and increased error detection by researchers and the wider research community.”
§ Mendelian randomization > Reproducibility and data deposition
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Within MR, using methods that make different assumptions (such as those regarding pleiotropy) and are therefore subject to different sources and directions of potential bias can support this approach, although the least plausible assumption may be shared by many methods, reducing the potential independent insight to be gained from comparing studies.”
§ Mendelian randomization > Reproducibility and data deposition
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The least plausible assumption may be shared by many methods.
None of the sources cited for this claim have been checked yet.
In the paper
“Alternatively, Bayesian frameworks for colocalization analysis consider GWAS summary statistics for both traits across multiple SNPs in the region around the IV and assess either the evidence that each variant is jointly causal115 or consider shared causal variants as one of five competing hypotheses.116”
§ Mendelian randomization > Results > Colocalization and MR
What the sources say
- Not checked yetcites [115] Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014). source
- Not checked yetcites [116] Wallace, C. Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses. PLoS Genet. 16, e1008720 (2020). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, it must be noted that MR studies are themselves not free from issues of bias or lack of power; evidence from MR studies for the presence or absence of an effect should be compared with results from studies with other potential sources of bias.”
§ Mendelian randomization > Applications > Cholesterol and coronary heart disease
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Using genetic variants associated with higher BMI as instruments in an individual-level MR study to estimate the effect of BMI on hospitalization costs,147 the authors found that higher BMI increased hospital costs with little evidence for non-linearity in this effect.”
§ Mendelian randomization > Applications > Estimation of healthcare costs
What the sources say
- Not checked yetcites [147] Holmes, M. V. Human genetics and drug development. N. Engl. J. Med. 380, 1076–1079 (2019). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This suggests that for children that are overweight, losing weight in their adulthood can still effectively lower risk of T2D and CAD and in this case a metabolically unhealthy childhood can potentially be offset by healthy lifestyle approaches adopted in adulthood.”
§ Mendelian randomization > Applications > Testing causation across the life course
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Study participation has shown to be heritable and is influenced by a number of different traits, and large studies such as the UK Biobank have been shown to have high levels of selection in those who participate.31,170–172”
§ Mendelian randomization > Limitations and Optimizations
What the sources say
- Not checked yetcites [31] Davey Smith, G. Epigenesis for epidemiologists: does evo-devo have implications for population health research and practice? Int. J. Epidemiol. 41, 236–247 (2012). source
- Not checked yetcites [170] Smit, R. A., Trompet, S., Dekkers, O. M., Jukema, J. W. & le Cessie, S. Survival bias in Mendelian randomization studies: a threat to causal inference. Epidemiology 30, 813 (2019). source
- Not checked yetcites [171] Schooling, C. M. et al. Use of multivariable Mendelian randomization to address biases due to competing risk before recruitment. Front. Genet. 11, 610852 (2020). source
- Not checked yetcites [172] Vansteelandt, S., Dukes, O. & Martinussen, T. Survivor bias in Mendelian randomization analysis. Biostatistics 19, 426–443 (2017). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Gene expression and protein are often instrumented with a single genetic variant and so colocalization can be particularly useful in MR studies of these exposures;42 in these settings colocalization can be used to attempt to falsify the second IV condition.”
§ Mendelian randomization > Results > Colocalization and MR
What the sources say
- Not checked yetcites [42] Bound, J., Jaeger, D. A. & Baker, R. M. Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak. J. Am. Stat. Assoc. 90, 443–450 (1995).
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Indeed, where data already exists, MR studies are relatively cheap to conduct — particularly compared to a large RCT — and can provide additional evidence that can be used to direct which studies are worth follow up with RCTs.”
§ Mendelian randomization > Applications > Cholesterol and coronary heart disease
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“An example is the J-shaped association between alcohol consumption and cardiovascular disease; there is now consensus that this apparent protective effect of moderate levels of consumption is artefactual, as discussed above.130”
§ Mendelian randomization > Reproducibility and data deposition
What the sources say
- Not checked yetcites [130] Goldstein, J. L. & Brown, M. S. A century of cholesterol and coronaries: from plaques to genes to statins. Cell 161, 161–172 (2015). source
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the context of epidemiological research, one area of concern is that findings may be replicated in settings with nearly identical sources of bias. Data with such replication provide little independent confirmation of the initial result and thus even highly consistent replicated findings may not reflect true causal effects.”
§ Mendelian randomization > Reproducibility and data deposition
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“For example, although MR studies have shown that smoking causes lung cancer186, this is not useful therapeutically following the onset of the disease as smoking cessation is not a useful treatment once lung cancer has developed.”
§ Mendelian randomization > Outlook
What the sources say
- Not checked yetcites [186] Davey Smith, G., Paternoster, L. & Relton, C. When will Mendelian randomization become relevant for clinical practice and public health? JAMA 317, 589–591 (2017). source
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Smoking cessation is not a useful treatment once lung cancer has developed.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although the increasing size of GWAS datasets appears to be positive for MR studies, it can also introduce problems; smaller and smaller effect sizes are being identified as significant in GWAS and it is increasingly likely that such variants affect the trait of interest through an upstream phenotype that might in turn influence the outcomes under investigation.”
§ Mendelian randomization > Outlook
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“When initially presented, it was concluded that “[MR] offers a more robust approach to understanding the effect of some modifiable exposures on health outcomes than does much conventional observational epidemiology”1 and that where possible, RCTs should follow to establish the effects of interventions. This conclusion remains unchanged, although moving towards formal triangulation of all pertinent”
§ Mendelian randomization > Outlook
What the sources say
- Not checked yetcites [1] Davey Smith, G. & Ebrahim, S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int. J. Epidemiol. 32, 1–22 (2003). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“The situation with MR is now moving towards the one seen in the meta-analysis literature, with the mass production of redundant, misleading, and conflicted publications.183”
§ Mendelian randomization > Outlook
What the sources say
- Not checked yetcites [183] Zhou, W. et al. Causal relationships between body mass index, smoking and lung cancer: univariable and multivariable Mendelian randomization. Int. J. Cancer 148, 1077–1086 (2021). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“These are relatively easy to conduct — perhaps too easy — and they can contain clear errors as discussed and demonstrated elsewhere.81”
§ Mendelian randomization > Outlook
What the sources say
- Not checked yetcites [81] Bowden, J. et al. Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the radial plot and radial regression. Int. J. Epidemiol. 47, 1264–1278 (2018). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For example, as the GWAS of CRP and vitamin D increased in size, multiple variants that primarily influence adiposity were identified — with adiposity being a confounder of the observational association of these exposures with health outcomes.”
§ Mendelian randomization > Outlook
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Adiposity is a confounder of the observational association of CRP and vitamin D with health outcomes.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Finally, MR uses genetic variants that are fixed across the life course to estimate the lifetime effects of the exposure of interest. This introduces a potential limitation in the form of canalization, which refers to a natural tendency for the suppression of phenotypic variation among individuals despite contrasting genotypes.”
§ Mendelian randomization > Limitations and Optimizations
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: MR uses genetic variants that are fixed across the life course to estimate the lifetime effects of the exposure of interest.
In the paper
“This means that although MR has been a powerful tool for confirming or discovering factors that cause disease, it does not often identify therapeutic targets.185”
§ Mendelian randomization > Outlook
What the sources say
- Not checked yetcites [185] Kim, Y.-I. Role of folate in colon cancer development and progression. J. Nutr. 133, 3731S–3739S (2003). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“For example, the onset and progression of Crohn’s disease are associated with different genetic variants, indicating that different risk factors play a role in onset and development.187”
§ Mendelian randomization > Outlook
What the sources say
- Not checked yetcites [187] Ye, T., Shao, J. & Kang, H. Debiased inverse-variance weighted estimator in two-sample summary-data Mendelian randomization. Ann. Stat. 49, 2079–2100 (2021).
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This can influence the generalizability of MR findings and exacerbate existing inequity in medical research.”
§ Mendelian randomization > Outlook
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“As most contemporary MR studies rely on available GWAS data, they unfortunately suffer from considerable bias with respect to representativeness of populations according to geography and ancestry.184”
§ Mendelian randomization > Outlook
What the sources say
- Not checked yetcites [184] Lee, J. C. et al. Genome-wide association study identifies distinct genetic contributions to prognosis and susceptibility in Crohn’s disease. Nat. Genet. 49, 262–268 (2017). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Most contemporary MR studies rely on available GWAS data.
In the paper
“All forms of selection biases could bias MR estimates and so careful assessment of the potential for selection into the sample or samples used in an MR study is important.65”
§ Mendelian randomization > Limitations and Optimizations
What the sources say
- Not checked yetcites [65] Sargan, J. D. The estimation of economic relationships using instrumental variables. Econometrica 26, 393–415 (1958). source
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“In addition, most studies recruit survivors of the original birth cohorts. This means all participants must have survived in order to observe whether they get the outcome of interest. Selection of participants on surviving their genetic make-up and the outcome of interest or a competing risk of the outcome effectively applies covariable adjustment on survival into the estimates.173–175”
§ Mendelian randomization > Limitations and Optimizations
What the sources say
- Not checked yetcites [173] Hernán, M. A. Invited commentary: selection bias without colliders. Am. J. Epidemiol. 185, 1048–1050 (2017). source
- Not checked yetcites [174] Mahmoud, O., Dudbridge, F., Davey Smith, G., Munafo, M. & Tilling, K. Slope-Hunter: a robust method for index-event bias correction in genome-wide association studies of subsequent traits. Nat. Commun. (in the press).
- Not checked yetcites [175] Waddington, C. H. Canalization of development and the inheritance of acquired characters. Nature 150, 563–565 (1942). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Most studies recruit survivors of the original birth cohorts.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The resulting bias can be accounted for through multivariable MR if the upstream factor is known; however, in many cases it is not and thus bias will remain undetected.”
§ Mendelian randomization > Outlook
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Adiposity is a confounder of the observational association of CRP and vitamin D with health outcomes.
In the paper
“This form of selection bias is likely to be particularly problematic for studies of harmful exposures on disease outcomes that occur in later life and will be least evident in studies where the exposure does not affect survival to recruitment.176”
§ Mendelian randomization > Limitations and Optimizations
What the sources say
- Not checked yetcites [176] Debat, V. & David, P. Mapping phenotypes: canalization, plasticity and developmental stability. Trends Ecol. Evol. 16, 555–561 (2001). source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Importantly, case-only study designs may be severely compromised by collider bias[[G] 56,64, which must be taken into account in data analysis185.”
§ Mendelian randomization > Outlook
What the sources say
- Not checked yetcites [56] Hernán, M. A., Hernández-Díaz, S. & Robins, J. M. A structural approach to selection bias. Epidemiology 15, 615–625 (2004). source
- Not checked yetcites [64] Hughes, R. A., Davies, N. M., Davey Smith, G. & Tilling, K. Selection bias when estimating average treatment effects using one-sample instrumental variable analysis. Epidemiology 30, 350–357 (2019). source
- Not checked yetcites [185] Kim, Y.-I. Role of folate in colon cancer development and progression. J. Nutr. 133, 3731S–3739S (2003). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper relies on this without support.
There is an urgent need for MR tools because many causal questions in health research cannot be adequately answered with conventional observational study designs or are not amenable to evaluation with RCTs for logistical or ethical reasons.
“There is an urgent need for these tools as many causal questions in health research cannot be adequately answered with conventional observational study designs or are not amenable to evaluation with RCT’s for logistical or ethical reasons.”
The authors state this as an assumption.
Genetic variants provide a source of exogenous variation in the exposure and can act as an instrumental variable.
“MR is based on the assumption that genetic variants provide a source of such exogenous variation in the exposure and can therefore act as an instrumental variable.1”
The authors state this as an assumption.
The relationship between the exposure and the outcome is linear across different values of the exposure for the vast majority of MR estimation methods.
“Additionally, the vast majority of MR estimation methods (with non-linear MR30 being the notable exception) impose the assumption that the relationship between the exposure and the outcome is linear across different values of the exposures.”
The authors state this as an assumption.
Changes in genetic variation are equivalent in their effects to changes in the exposure through environmental or pharmaceutical manipulation (gene-environment equivalence).
“Finally, data used in MR additionally require the assumption that changes in genetic variation are equivalent in their effects to changes in the exposure through environmental or pharmaceutical manipulation — a concept known as gene-environment equivalence.32”
The authors state this as an assumption.
For two-sample MR estimation, the two samples are from the same underlying population, or more narrowly that the association between the genetic variants and exposure is the same in both samples.
“An important assumption for two-sample MR estimation is that the two samples are from the same underlying population, or more narrowly that the association between the genetic variants and exposure is the same in both samples, although that exposure may not be measured or reported in the sample included in the outcome dataset.39”
The authors state this as an assumption.
Overidentification tests rely on the assumption that each IV estimates the same causal effect.
“They also rely on the assumption that each IV estimates the same causal effect, which may not be true for complex traits where different genetic variants potentially act as genetic instruments for different aspects of the trait.”
The authors state this as an assumption.
sisVIVE and adaptive LASSO assume that multiple IVs are available and that a majority or plurality of the proposed IVs are valid.
“These methods assume that multiple IVs are available and that a majority or plurality of the proposed IVs are valid.”
The authors state this as an assumption.
For inverse-variance weighted estimation, the genetic variants are independent of each other.
“One important assumption for IVW estimation is that the genetic variants are independent of each other.41”
The authors state this as an assumption.
MR Egger analysis assumes that across all instruments, the magnitude of the pleiotropic effect is unrelated to the strength of the association between the genetic variant and the phenotype of interest (the InSIDE assumption).
“MR Egger analysis assumes that across all instruments, the magnitude of the pleiotropic effect is unrelated to the strength of the association between the genetic variant and the phenotype of interest (known as the InSIDE assumption)”
The authors state this as an assumption.
Multivariable Mendelian randomization assumes that pleiotropic pathways operate through known and well-measured phenotypes that are also included in the estimation.
“multivariable MR assumes pleiotropic pathways operate through known and well-measured phenotypes that are also included in the estimation.”
The authors state this as an assumption.
For multivariable Mendelian randomization, each exposure must be robustly predicted by the instruments, conditional on the other exposures included in the estimation (MVIV1).
“each exposure must be robustly predicted by the instruments, conditional on the other exposures included in the estimation (Multivariable instrumental variable condition 1, or MVIV1)”
The authors state this as an assumption.
For multivariable Mendelian randomization, there must be no confounders of the outcome and any of the instruments (MVIV2).
“there must be no confounders of the outcome and any of instruments (MVIV2)”
The authors state this as an assumption.
For multivariable Mendelian randomization, none of the instruments can have an effect on the outcome that does not act through at least one of the exposures (MVIV3).
“none of the instruments can have an effect on the outcome that doesn’t act through at least one of the exposures (MVIV3)”
The authors state this as an assumption.
Bayesian colocalization strategies assume that summary data exist for multiple variants in a region, with sufficient density such that any causal variant or variants for an outcome and exposure are likely to be included in the set of variants studied.
“the latter assume summary data exist for multiple variants in a region, with sufficient density such that any causal variant or variants for an outcome and exposure are likely to be included in the set of variants studied.”
The authors state this as an assumption.
The conclusion about alcohol consumption and vascular disease risk assumes no selection bias.
“assuming no selection bias”
The paper relies on this without support.
Genetic variants are generally fixed at conception.
“As genetic variants are generally fixed at conception, it is not intuitively clear how confounding of the instrument and the outcome can occur in MR studies.”
The paper relies on this without support.
Most studies recruit survivors of the original birth cohorts.
“In addition, most studies recruit survivors of the original birth cohorts.”
The paper relies on this without support.
MR uses genetic variants that are fixed across the life course to estimate the lifetime effects of the exposure of interest.
“Finally, MR uses genetic variants that are fixed across the life course to estimate the lifetime effects of the exposure of interest.”
The paper relies on this without support.
Most contemporary MR studies rely on available GWAS data.
“As most contemporary MR studies rely on available GWAS data, they unfortunately suffer from considerable bias with respect to representativeness of populations according to geography and ancestry.184”
The paper relies on this without support.
Adiposity is a confounder of the observational association of CRP and vitamin D with health outcomes.
“with adiposity being a confounder of the observational association of these exposures with health outcomes.”
The paper relies on this without support.
Smoking cessation is not a useful treatment once lung cancer has developed.
“as smoking cessation is not a useful treatment once lung cancer has developed.”
The paper relies on this without support.
The underlying magnitude of the effect in drug-target MR is very small.
“using a genetic instrument for treatment as part of a drug-target MR means that the underlying magnitude of the effect on which potential genetic effect modifiers are investigated is very small”
The paper relies on this without support.
The least plausible assumption may be shared by many methods.
“although the least plausible assumption may be shared by many methods”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Mendelian randomization (MR) uses germline genetic proxies as instruments to evaluate the causality for lifelong modifiable exposures with disease, and thus overcome some of the limitations of traditional observational studies12.”
From Genetic insights into causal effects of lipids and lipid-modifying targets on calcific aortic valve stenosis: a Mendelian randomized study (2025) · cites this paper as [12]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
§ Mendelian randomization > Results > Further extensions of MR methods
“If genetic variants known to associate with dementia influence hearing loss and genetic variants known to associate with hearing loss do not influence dementia risk, this suggests that hearing loss is a causal determinant of dementia.”
§ Mendelian randomization > Results > Further extensions of MR methods
“a bidirectional MR would first conduct an MR analysis of the effect of liability to dementia on hearing and then for the effect of hearing on dementia.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Mendelian randomization > Results > Summary level data
“The IVW estimate can equivalently be obtained by regressing the genetic variant-outcome association, πl∧, on the genetic variant-exposure association, π^l, (without an intercept)”
§ Mendelian randomization > Results > Summary level data
“MR estimation with summary level data requires estimates of π^l, the estimated effect of genetic variant l on the exposure with variance σx,l2, and Γl∧, the estimated effect of genetic variant l on the outcome with variance σy,l2.”
Citations
Cites
1 retracted
Dementia prevention, intervention, and care: 2020 report of the Lancet Commission
Livingston, G., Huntley, J., Sommerlad, A., Ames, D., Ballard, C., Banerjee, S., Brayne, C., Burns, A., Cohen-Mansfield, J., Cooper, C., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30367-6 · Cited by 10,430
RetractedCausality: Models, Reasoning, and Inference
Pearl, J. (2009) · doi:10.1017/cbo9780511803161 · Cited by 11,653
Principal components analysis corrects for stratification in genome-wide association studies
Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006) · Nature Genetics · doi:10.1038/ng1847 · Cited by 10,248
Bowden, J., Davey Smith, G., & Burgess, S. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv080 · Cited by 9,816
Bowden, J., Davey Smith, G., Haycock, P. C., & Burgess, S. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21965 · Cited by 8,922
The UK Biobank resource with deep phenotyping and genomic data
Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., et al. (2018) · Nature · doi:10.1038/s41586-018-0579-z · Cited by 8,876
Verbanck, M., Chen, C.-Y., Neale, B., & Do, R. (2018) · Nature Genetics · doi:10.1038/s41588-018-0099-7 · Cited by 8,591
Double-slit photoelectron interference in strong-field ionization of the neon dimer
Kunitski, M., Eicke, N., Huber, P., Köhler, J., Zeller, S., Voigtsberger, J., Schlott, N., Henrichs, K., Sann, H., Trinter, F., et al. (2019) · Institutional Repository of Leibniz Universität Hannover (Leibniz Universität Hannover) · doi:10.15488/5174 · Cited by 8,378
Double-slit photoelectron interference in strong-field ionization of the neon dimer
Kunitski, M., Eicke, N., Huber, P., Köhler, J., Zeller, S., Voigtsberger, J., Schlott, N., Henrichs, K., Sann, H., Trinter, F., et al. (2019) · Nature Communications · doi:10.1038/s41467-018-07882-8 · Cited by 8,162
Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data
Burgess, S., Butterworth, A., & Thompson, S. G. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21758 · Cited by 6,050
Research works printed but not linked: 8
- Glymour, M. M. Natural experiments and instrumental variable analyses in social epidemiology. Methods Soc. Epidemiol. 1, 429 (2006).
- Zhao, Q., Wang, J., Spiller, W., Bowden, J. & Small, D. S. Two-sample instrumental variable analyses using heterogeneous samples. Stat. Sci. 34, 317–333 (2019).
- Bound, J., Jaeger, D. A. & Baker, R. M. Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak. J. Am. Stat. Assoc. 90, 443–450 (1995).
- Wooldridge, J. M. Econometric Analysis of Cross Section and Panel Data (MIT Press, 2010).
- Tchetgen Tchetgen, E. J., Sun, B. & Walter, S. The GENIUS approach to robust Mendelian randomization inference. Stat. Sci. 36, 443–464 (2019).
- Sallis, H. Triangulation protocol; intergenerational effects of parental substance use on child substance use and mental health outcomes. Preprint at https://osf.io/s6jv4/ (2021).
- Brumpton, B. et al. Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses. Nat. Commun. 11, 3519 (2020). · DOI · Paper page
- Mahmoud, O., Dudbridge, F., Davey Smith, G., Munafo, M. & Tilling, K. Slope-Hunter: a robust method for index-event bias correction in genome-wide association studies of subsequent traits. Nat. Commun. (in the press).
Cited by
Sorted by citations
Angrist, J. D., & Krueger, A. B. (2001) · Journal of Economic Perspectives · doi:10.1257/jep.15.4.69 · Cited by 2,355
Pang, Z., Lu, Y. P., Zhou, G., Hui, F., Xu, L., Viau, C., Spigelman, A. F., MacDonald, P. E., Wishart, D. S., Li, S., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae253 · Cited by 2,481
Guidelines 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
Self-citationMendelian randomization for cardiovascular diseases: Principles and applications
Larsson, S. C., Butterworth, A. S., & Burgess, S. (2023) · European Heart Journal · doi:10.1093/eurheartj/ehad736 · Cited by 735
Guidelines for performing Mendelian randomization investigations: update for summer 2023
Burgess, S., Smith, G. D., Davies, N. M., Dudbridge, F., Gill, D., Glymour, M. M., Hartwig, F. P., Kutalik, Z., Holmes, M. V., Minelli, C., et al. (2023) · Wellcome Open Research · doi:10.12688/wellcomeopenres.15555.3 · Cited by 747
Self-citation
Metadata
- Authors
- Sanderson, E., Glymour, M. M., Holmes, M. V., Kang, H., Morrison, J., Munafò, M. R., Palmer, T., Schooling, C. M., Wallace, C., Zhao, Q., & Davey Smith, G.
- Affiliations
- University of Bristol (GB)
- University of California, San Francisco (US)
- University of Wisconsin–Madison (US)
- University of Michigan (US)
- City University of New York (US)
- University of Cambridge (GB)
- Corresponding author
- Sanderson, Eleanor
- ORCID
- Venue
- Nature Reviews Methods Primers
- Published
- 2022-02-10
- Volume
- 2
- Issue
- 1
- ISSN
- 2662-8449 (ISSN-L)
- PMC
- PMC7614635
- OpenAlex
- W4211103501
- Semantic Scholar
- a18b5dabdb938b4c3b9f909a34a109d7744cc5a8
- Cited by
- 2,229
- References
- 222
- Citations by year
- 2026: 450 · 2025: 782 · 2024: 688 · 2023: 243 · 2022: 66
- FWCI
- 69.62
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Reviews Methods Primers (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Explore Bristol Research (acceptedVersion, other-oa)
- Bristol Research (University of Bristol) (submittedVersion, other-oa)
- Funders
- Wellcome Trust (107881/Z/15/Z, WT107881)
- British Heart Foundation (FS/18/23/33512)
- Medical Research Council (MC_UU_00011/7, MC_UU_00002/4, MC_UU_00011/3, MC_UU_00011/1)
- National Institute on Aging (R01AG057869)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- Mendelian randomization, causal inference, genetic instruments, unmeasured confounding, Mendelian inheritance, triangulation, genomic data
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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