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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
68 Not checked yet · 38 Own interpretation · 34 No basis given · 4 Partly supported · 4 Own method · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
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)
plain- Asserted: passes — The authors put it forward as true.
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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)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Partly supported
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
boosteddirect- Asserted: passes — The authors put it forward as true.
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- 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
“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.
- 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 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
“In MR using unrelated individuals, the similarity between the allele groups is not guaranteed as with a well-conducted RCT.”
§ Mendelian randomization > Experimentation
plain- Asserted: passes — The authors put it forward as true.
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- 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
“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
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
“Further, associations between allele distribution and traits can exist at a population level owing to population stratification or assortative mating.”
§ Mendelian randomization > Experimentation
plaincorrelational- Asserted: passes — The authors put it forward as true.
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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
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
“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
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
“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
plaindirect- Asserted: passes — The authors put it forward as true.
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- 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
“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.
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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.
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- 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.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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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.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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
“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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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- 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.
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- 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.
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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.
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.
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- 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.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
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
- Not checked yetcites [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). source
- 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 [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). source
- 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.
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.
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- 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.
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- 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
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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.
A language model drew the claims from the paper on 2026-10-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
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