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Cited by 1,273Genetic Associations and EpidemiologyAdvanced Causal Inference TechniquesMeta-analysis and systematic reviews

Guidelines for performing Mendelian randomization investigations

Open access
Burgess, Stephen, Davey Smith, George, Davies, Neil M., Dudbridge, Frank, Gill, Dipender, Glymour, M. Maria, Hartwig, Fernando P., Holmes, Michael V. +3 more·2020·Wellcome Open Research
DOI 10.12688/wellcomeopenres.15555.2PMC PMC7384151
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Abstract

This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into nine sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust statistical methods and one on other approaches), data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 18 months.

Verified 2026-10-10 · 3 references not checked yet

AI snapshot

Written from the paper’s full text.

TL;DR

This paper provides updated guidelines for performing and appraising Mendelian randomization investigations, organized into ten sections from motivation and data sources through analysis, sensitivity analyses, presentation, and interpretation. It recommends inverse-variance weighted analysis with multiplicative random-effects as the primary summarized-data method and a range of sensitivity analyses, while emphasizing cautious causal interpretation.

Key findings

  • The guidelines are divided into ten sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust statistical methods and one on other approaches), extensions and additional analyses, data presentation, and interpretation; updates are planned at least every 24 months.
  • For summarized data, the paper recommends the inverse-variance weighted (IVW) method with multiplicative random-effects as the primary analysis, because it is most efficient with valid instruments and accounts for heterogeneity; if a causal effect is detected, investigators should perform sensitivity analyses.
  • Table 1 summarizes robust methods including MR-Egger (InSIDE assumption), weighted median (majority valid), mode-based estimation (plurality valid), MR-PRESSO, MR-RAPS, MR-cML, and others; the paper suggests, for example, performing MR-RAPS, weighted median, and MR-cML because they require different assumptions, and recommends reporting heterogeneity measures such as Cochran’s Q or I².
  • Interpretation should be cautious: Mendelian randomization estimates are better interpreted as a test statistic for a causal hypothesis and an indicator of direction rather than the expected impact of a well-defined intervention, and estimates are often larger than those from trials for the same magnitude of exposure difference.

Methods

  • Design: a guideline/consensus document aimed at practitioners, journal editors, and reviewers, complementary to the STROBE-MR reporting recommendations; it is not an empirical study or systematic review.
  • Data or sample: no primary data or study sample are analyzed; the paper references statistical methods and software in Table 1 and provides flowcharts and a reviewer checklist.
  • Main analysis recommendations: two-stage least-squares for individual-level one-sample data; IVW for summarized data; multivariable Mendelian randomization when related exposures share genetic predictors; robust methods for sensitivity analysis.

Limitations

  • Stated: the guidelines are deliberately written as suggestions and recommendations rather than prescriptive rules, and the authors state there is no recipe or single “right way” to perform a Mendelian randomization investigation; best practice depends on the aim and the specific exposure and outcome variables.
  • Stated: causal inferences from Mendelian randomization rely on untestable assumptions, including gene–environment equivalence; even replicated findings retain intrinsic uncertainty, and robust methods make different assumptions and may be less effective for invalidity affecting all variants systematically, such as population stratification or dynastic effects.
  • Evident: as a guideline document, it does not provide new empirical data or a systematic quantitative synthesis; its recommendations are based on cited methodological literature and author consensus.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.12688/wellcomeopenres.15555.2

Cites

174 of 177 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 111 cited sources checked · 0 supported · 0 flagged

  • Interpretationreasoning

    Access to only summarized data limits the range of analyses that can be performed.

    “However, access to only summarized data limits the range of analyses that can be performed.”
    Quote in the paperSubstantial claim
  • Premisecitation

    If published summarized association estimates have already been adjusted for a variable causally downstream of the exposure or outcome, collider bias can occur.

    “If published summarized association estimates have already been adjusted for a variable causally downstream of the exposure or outcome, collider bias (see Section 7) can occur 38 .”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [38] S Burgess, 2016, Bias due to participant overlap in two-sample Mendelian randomization., Genet Epidemiol. source

      Not checked in the free run.

  • Interpretationreasoning

    If the two samples in a two-sample MR setting represent different ethnic groups, patterns of linkage disequilibrium can differ between population ancestry groups, meaning that a genetic variant may not be as strongly (or even not at all) associated with the exposure in the outcome dataset.

    “A particular concern arises if the two samples represent different ethnic groups, as patterns of linkage disequilibrium can differ between population ancestry groups, meaning that a genetic variant may not be as strongly (or even not at all) associated with the exposure in the outcome dataset.”
    Quote in the paperSubstantial claim
  • Interpretationreasoning

    In a two-sample MR setting, if the two samples differ in associations of the genetic variant with the exposure or with variables on pleiotropic pathways, this could affect the validity of the instrumental variable assumptions.

    “In a two-sample setting, the populations from which the two samples were extracted may differ. This is problematic if associations of the genetic variant with the exposure or with variables on pleiotropic pathways differ between the two samples, as this could affect the validity of the instrumental variable assumptions.”
    Quote in the paperSubstantial claim
  • Premisecitation

    Several large consortia have made summarized genetic association estimates publicly available for millions of variants.

    “Several large consortia have made such estimates publicly available for millions of variants 30, 35, 36 .”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [30] S Burgess, 2016, Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods., Stat Med. source

      Not checked in the free run.

    • cites [35] J Staley, 2017, Semiparametric methods for estimation of a nonlinear exposure-outcome relationship using instrumental variables with application to Mendelian randomization., Genet Epidemiol. source

      Not checked in the free run.

    • cites [36] B Pierce, 2013, Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators., Am J Epidemiol. source

      Not checked in the free run.

  • Classified as probably not a substantial claim. 48
  • May combine two claims. 45
  • The quote only partly states this claim. 1

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    Summarized data are often available for larger sample sizes, meaning that power to detect a causal effect is increased.

    “Summarized data are often available for larger sample sizes, meaning that power to detect a causal effect is increased.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Differences between the two samples in population characteristics such as age, sex, and socio-economic background can affect not only the interpretation of causal estimates, but also the validity of causal inferences.

    “Alternatively, the two samples could differ substantially according to population characteristics such as age, sex, socio-economic background, and so on 32 . Such differences can affect not only the interpretation of causal estimates, but also the validity of causal inferences 33 .”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [32] J Bowden, 2017, A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization., Stat Med. source

      Not checked in the free run.

    • cites [33] J Staley, 2016, PhenoScanner: a database of human genotype-phenotype associations., Bioinformatics. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Two-sample approaches can be used with individual-level data, and summarized data approaches can be used with one-sample data.

    “Moreover, two-sample approaches can be used with individual-level data (such as the use of externally-derived weights), and summarized data approaches can be used with one-sample data (if necessary, by creating the summarized data from the individual-level data 37 ).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [37] S Burgess, 2011, Avoiding bias from weak instruments in Mendelian randomization studies., Int J Epidemiol. source

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationreasoning

    Individual-level data are required to conduct analyses in specific subgroups or strata of the population, or to choose which variables to adjust for when generating the summarized data.

    “Individual-level data are required to conduct analyses in specific subgroups or strata of the population, or to choose which variables to adjust for when generating the summarized data.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationnone given

    One-sample MR analyses do not suffer from concerns about differences between the two samples and do not require harmonization of genetic variants across datasets.

    “One-sample analyses do not suffer from these concerns, nor do they require harmonization of the genetic variants across the datasets (see Section 4).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationnone given

    A specific advantage of publicly available summarized data is transparency, as the analysis can be reproduced by a third party with access to the same data.

    “A specific advantage of publicly available summarized data is transparency, as the analysis can be reproduced by a third party with access to the same data.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationnone given

    Dynastic effects occur when a parent's genotype affects their child's outcome by a causal pathway not acting via the child's phenotype, and this can induce associations between the offspring's genotype and outcomes that do not reflect the effect of the exposure in the offspring.

    “Finally, dynastic effects occur when a parent’s genotype affects their child’s outcome by a causal pathway not acting via the child’s phenotype (for example, due to an effect of the parent’s phenotype on the child’s outcome). This can induce associations between the offspring’s genotype and outcomes that do not reflect the effect of the exposure in the offspring.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    For a one-sample MR analysis with a binary disease outcome, if genetic associations with the exposure are estimated in the controls only, then genetic associations with the exposure and outcome will not be correlated, and bias will follow the pattern of the two-sample setting.

    “For the special case of a one-sample analysis with a binary disease outcome, if the genetic associations with the exposure are estimated in the controls only, then genetic associations with the exposure and outcome will not be correlated, and bias will follow the pattern of the two-sample setting 42 .”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [42] L Mokry, 2015, Vitamin D and Risk of Multiple Sclerosis: A Mendelian Randomization Study., PLoS Med. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    Within-family MR analyses typically have lower power than analyses in unrelated individuals, because families where all individuals have the same genotype do not contribute information.

    “However, within-family analyses typically have lower power than analyses in unrelated individuals, as families where all individuals have the same genotype (such as where both parents are major homozygotes) would not contribute any information to the analysis.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    In a one-sample MR setting with weak instruments, instrumental variable estimates are biased in the direction of the confounded association, and false positive (type 1 error) rates are inflated, particularly when more than one variant is included.

    “This results in instrumental variable estimates that are biased in the direction of the confounded association, and inflated false positive (type 1 error) rates, particularly when more than one variant is included in the analysis 41 .”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [41] M Brion, 2013, Calculating statistical power in Mendelian randomization studies., Int J Epidemiol. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    In a two-sample MR setting without sample overlap, bias due to weak instruments is in the direction of the null, and does not lead to false positive findings.

    “In a two-sample setting without sample overlap, bias due to weak instruments is in the direction of the null, and does not lead to false positive findings.”
    Quote in the paperSubstantial claim

  • The quote only partly states this claim.

    Interpretationreasoning

    The key consequence of the randomization in Mendelian randomization is that genetic variants are independently distributed from traits that they do not affect, an implication of Mendel's laws of segregation and independent assortment.

    “The key consequence of this randomization is that genetic variants are independently distributed from traits that they do not affect, an implication of Mendel’s laws of segregation and independent assortment.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Various statistical methods have been proposed to reduce weak instrument bias due to sample overlap and bias due to winner's curse.

    “Various statistical methods have been proposed to reduce weak instrument bias due to sample overlap and bias due to winner’s curse (see Section 3) 43– 45 .”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [43] D Swerdlow, 2012, The interleukin-6 receptor as a target for prevention of coronary heart disease: a mendelian randomisation analysis., Lancet. source

      Not checked in the free run.

    • cites [44] Q Zhao, 2019, Powerful three-sample genome-wide design and robust statistical inference in summary-data Mendelian randomization., Int J Epidemiol. source

      Not checked in the free run.

    • cites [45] G Davey Smith, 2014, Mendelian randomization: genetic anchors for causal inference in epidemiological studies., Hum Mol Genet. source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    In practice, distinguishing between horizontal pleiotropy and vertical pleiotropy requires knowledge of the relationships between the variables in the analysis.

    “In practice, distinguishing between horizontal pleiotropy and vertical pleiotropy requires knowledge of the relationships between the variables in the analysis.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationnone given

    For a polygenic analysis, there are two main strategies for selecting variants: either a biologically driven approach or a statistically driven approach.

    “For a polygenic analysis, there are two main strategies for selecting variants: either a biologically driven approach or a statistically driven approach.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Genetic variants in the IL6R gene region are associated with increased circulating levels of interleukin-6, but they decrease interleukin-6 signalling, leading to opposite directions of association with disease outcomes to those expected based on serum interleukin-6 measurements.

    “As an example, although genetic variants in the IL6R gene region are associated with increased circulating levels of interleukin-6, they in fact decrease interleukin-6 signalling, leading to opposite directions of association with disease outcomes to those expected based on serum interleukin-6 measurements 65 .”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [65] S Burgess, 2017, Interpreting findings from Mendelian randomization using the MR-Egger method., Eur J Epidemiol. source

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationreasoning

    For complex multifactorial exposures such as body mass index or blood pressure, there is no single relevant gene, so a more agnostic polygenic analysis may be necessary.

    “However, for complex multifactorial exposures such as body mass index or blood pressure, there is no single relevant gene, and so a more agnostic polygenic analysis may be necessary.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    When genetic variants are all valid instruments, power depends on the proportion of variance in the exposure explained by the variants, so a polygenic Mendelian randomization investigation will typically have greater power than one including variants only from a single gene region.

    “When genetic variants are all valid instruments, power depends on the proportion of variance in the exposure explained by the variants 56 – hence a polygenic Mendelian randomization investigation will typically have greater power than one including variants only from a single gene region.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [56] M Greco, 2015, Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome., Stat Med. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    Variants in the same gene region are likely to either all be valid instruments or all invalid.

    “Variants in the same gene region are likely to either all be valid instruments or all invalid.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationnone given

    The two approaches to variant selection are not mutually exclusive, and the overall decision of which variants to include may comprise elements from both approaches.

    “The two approaches are not mutually exclusive, and the overall decision of which variants to include may comprise elements from both approaches.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Similar two-stage analyses can be performed with binary variables using logistic regression.

    “Similar two-stage analyses can be performed with binary variables using logistic regression 73”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [73] J Smith, 2014, Association of low-density lipoprotein cholesterol-related genetic variants with aortic valve calcium and incident aortic stenosis., JAMA. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationnone given

    Scenario e reflects horizontal pleiotropy that violates the instrumental variable assumptions.

    “Scenario e reflects “horizontal pleiotropy” (also called “direct pleiotropy”) that violates the instrumental variable assumptions.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    A null finding in a liberal analysis is more convincing evidence of a true null relationship because there is little evidence for a causal relationship even when potentially pleiotropic genetic variants are included.

    “Hence a null finding in a liberal analysis is more convincing evidence of a true null relationship – there is little evidence for a causal relationship even when potentially pleiotropic genetic variants are included in the analysis.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    If all genetic variants are valid instruments and the relationships between all variables are linear and homogeneous for all individuals in the population, then the variant-specific estimates all target the same causal parameter, and there is no more heterogeneity between them than expected by chance alone.

    “If all genetic variants are valid instruments and the relationships between all variables (genetic variants, exposure and outcome) are linear and homogeneous for all individuals in the population, then we would expect the variant-specific estimates (that is, the ratio estimates based on each variant in turn) to all target the same causal parameter, and for there to be no more heterogeneity between”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [13] B Bulik-Sullivan, 2015, LD Score regression distinguishes confounding from polygenicity in genome-wide association studies., Nat Genet. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    If there is a reverse causal effect of the outcome on the exposure, genetic predictors of the outcome could be identified as hits in a genome-wide association study for the exposure, but such variants would not be valid instrumental variables.

    “If there is a reverse causal effect of the outcome on the exposure, then genetic predictors of the outcome could be identified as hits in a genome-wide association study for the exposure. However, such variants would not be valid instrumental variables.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisereasoning

    Allele and strand information can be double-checked by comparing allele frequency information; if allele frequencies are similar for the A and T alleles, the researcher can be more confident that this is a strand mismatch.

    “Allele and strand information can be double-checked by comparing allele frequency information – if the allele frequencies are similar for the A and T alleles, then the researcher can be more confident that this is a strand mismatch.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationnone given

    Causal conclusions will be more reliable when the instrumental variable assumptions are more plausible.

    “Causal conclusions will be more reliable when the instrumental variable assumptions are more plausible.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationnone given

    Scenario f reflects a reverse causation situation where the genetic variant has been incorrectly identified as primarily affecting the exposure.

    “Scenario f reflects a reverse causation situation where the genetic variant has been incorrectly identified as primarily affecting the exposure.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Estimates from the MR-Egger method are particularly affected by outlying and influential datapoints and are prone to be imprecise, particularly when the variant-exposure associations are all similar in magnitude.

    “Estimates from the MR-Egger method are particularly affected by outlying and influential datapoints 90 , and are prone to be imprecise, particularly when the variant—exposure associations are all similar in magnitude.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [90] J White, 2016, Plasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysis., Lancet Diabetes Endocrinol. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Examples of exposure sets where multivariable Mendelian randomization has been used include lipid fractions (such as high-density lipoprotein cholesterol, LDL-cholesterol, and triglycerides) and body composition measures (such as fat mass and fat-free mass).

    “Examples of exposure sets where multivariable Mendelian randomization has been used include lipid fractions (such as high-density lipoprotein cholesterol, LDL-cholesterol, and triglycerides) 82 , and body composition measures (such as fat mass and fat-free mass) 83 .”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [82] N Solovieff, 2013, Pleiotropy in complex traits: challenges and strategies., Nat Rev Genet. source

      Not checked in the free run.

    • cites [83] M Benn, 2017, Low LDL cholesterol, PCSK9 and HMGCR genetic variation, and risk of Alzheimer's disease and Parkinson's disease: Mendelian randomisation study., BMJ. source

      Not checked in the free run.

  • May combine two claims.

    Premisenone given

    If there is no more heterogeneity between the ratio estimates for the individual variants than expected by chance alone, then the random-effect analysis is equivalent to the fixed-effect analysis, and there is no loss of precision in making the weaker random-effects assumption.

    “If there is no more heterogeneity between the ratio estimates for the individual variants than would be expected by chance alone, then the random-effect analysis is equivalent to the fixed-effect analysis, and there is no loss of precision in making the weaker random-effects assumption.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Premisenone given

    The instrumental variable assumptions in multivariable Mendelian randomization require each variant to be associated with at least one of the exposures, not associated with the outcome via confounding, and not to affect the outcome except potentially via its association with one or more of the exposures included in the analysis model.

    “The instrumental variable assumptions in multivariable Mendelian randomization require each variant to be associated with at least one of the exposures, not associated with the outcome via confounding, and not to affect the outcome except potentially via its association with one or more of the exposures included in the analysis model.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Premisenone given

    The most commonly used robust methods are MR-Egger, median- and mode-based methods, and MR-PRESSO.

    “The most commonly used robust methods are MR-Egger, median- and mode-based methods, and MR-PRESSO.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Robust methods are more likely to be effective for addressing instrument invalidity that arises due to issues such as pleiotropy or linkage disequilibrium with a variant influencing a confounder, which affect specific variants in a sporadic way, and less effective for instrument invalidity that arises due to issues such as population stratification or dynastic effects, which affect all variants in

    “However, the robust methods are more likely to be effective for addressing instrument invalidity that arises due to issues such as pleiotropy or linkage disequilibrium with a variant influencing a confounder, which affect specific variants in a sporadic way, and less effective for instrument invalidity that arises due to issues such as population stratification or dynastic effects, which affect”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [48] D Lawlor, 2008, Mendelian randomization: using genes as instruments for making causal inferences in epidemiology., Stat Med. source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Premisecitation

    The mode-based method assumes more variants estimate the true causal effect than estimate any other quantity (plurality valid assumption).

    “and the mode-based method assumes more variants estimate the true causal effect than estimate any other quantity (plurality valid assumption).”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [19] J Bowden, 2015, Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression., Int J Epidemiol. source

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    • cites [20] J Bowden, 2016, Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator., Genet Epidemiol. source

      Not checked in the free run.

    • cites [92] E Porcu, 2019, Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits., Nat Commun. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based).

    Premisereasoning

    A causal model is evidenced if the predominance of variants that associate with the exposure also associate with the outcome in a proportional way.

    “A causal model is evidenced if the predominance of variants that associate with the exposure also associate with the outcome in a proportional way.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    The MR-RAPS method first excludes strongly pleiotropic variants, and then assumes all remaining variants follow the InSIDE assumption.

    “Another method making the InSIDE assumption is the MR-RAPS (robust adjusted profile score) method, which first excludes strongly pleiotropic variants, and then assumes all remaining variants follow the InSIDE assumption 18 .”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [18] S Burgess, 2013, Mendelian randomization analysis with multiple genetic variants using summarized data., Genet Epidemiol. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The MR-PRESSO method is a variation on the IVW method that first sequentially removes genetic variants from the analysis whose variant-specific causal estimate differs substantially from those of other variants.

    “The MR-PRESSO method is a variation on the IVW method that first sequentially removes genetic variants from the analysis whose variant-specific causal estimate differs substantially from those of other variants 21 .”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [21] F Hartwig, 2017, Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption., Int J Epidemiol. source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Premisecitation

    Leave-one-out analyses can be valuable in assessing the reliance of a Mendelian randomization analysis on a particular variant.

    “Leave-one-out analyses (i.e. remove one variant from the analysis and re-estimate the causal effect) can be valuable in assessing the reliance of a Mendelian randomization analysis on a particular variant 100 .”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [100] M Holmes, 2017, Mendelian randomization in cardiometabolic disease: challenges in evaluating causality., Nat Rev Cardiol. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    It is unlikely that one approach will perform best in all settings.

    “It is unlikely that one approach will perform best in all settings”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    If estimates from all methods are similar, then any causal claim is more credible.

    “If estimates from all methods are similar, then any causal claim is more credible.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    Conclusions are more reliable when multiple genetic variants provide concordant evidence for a causal effect, and particularly when there is no more heterogeneity between the variant-specific causal estimates than expected by chance.

    “Conclusions are more reliable when multiple genetic variants provide concordant evidence for a causal effect, and particularly when there is no more heterogeneity between the variant-specific causal estimates than expected by chance.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationcitation

    Combining a range of analyses with orthogonal validation is the most powerful way of addressing causal questions, within the triangulation of evidence framework.

    “Combining this with orthogonal validation – such as through the use of positive and negative controls ( Section 7) – is the most powerful way of addressing causal questions, within the triangulation of evidence framework 96, 97 .”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [96] S Burgess, 2015, Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways., Int J Epidemiol. source

      Not checked in the free run.

    • cites [97] J Bowden, 2018, Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression., Int J Epidemiol. source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Interpretationcitation

    Removing a variant from the analysis is more justified when a pleiotropic association of the variant has been identified.

    “Removing a variant from the analysis is more justified when a pleiotropic association of the variant has been identified 103 .”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [103] D Lawlor, 2016, Triangulation in aetiological epidemiology., Int J Epidemiol. source

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  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Steiger filtering removes variants from the analysis if their association with the outcome is stronger than that with the exposure.

    “A further approach for identifying variants to remove from the analysis is Steiger filtering, which removes variants from the analysis if their association with the outcome is stronger than that with the exposure 102 .”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [102] M Munafò, 2018, Robust research needs many lines of evidence., Nature. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Childhood levels of vitamin D have been used as a negative control outcome for the effect of adulthood BMI; childhood BMI was shown to affect childhood vitamin D levels in a Mendelian randomization investigation, but adulthood BMI did not.

    “For example, childhood levels of vitamin D have been used as a negative control outcome for the effect of adulthood BMI; childhood BMI was shown to affect childhood vitamin D levels in a Mendelian randomization investigation, but adulthood BMI did not 106 .”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [106] S Burgess, 2019, How humans can contribute to Mendelian randomization analyses., Int J Epidemiol. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisereasoning

    Other approaches for assessing robustness include varying the dataset and choice of genetic variants in the analysis, the use of positive and negative control outcomes and/or samples, colocalization, subgroup analyses, and examining associations with potentially pleiotropic variables.

    “Other approaches for assessing robustness include varying the dataset and choice of genetic variants in the analysis (including the suggestion of liberal and conservative variant sets in Section 3), the use of positive and negative control outcomes and/or samples, colocalization, subgroup analyses, and examining associations with potentially pleiotropic variables.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationcitation

    If there are many variants in an analysis, leaving one variant out at a time is unlikely to change the estimate substantially, and leaving out subsets of the variants may be more appropriate.

    “If there are many variants in an analysis, leaving one variant out at a time is unlikely to change the estimate substantially, and leaving out subsets of the variants (say, a randomly chosen 30% at a time 101 ) may be more appropriate.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [101] T Richardson, 2019, Can the impact of childhood adiposity on disease risk be reversed? A Mendelian randomization study., medRxiv. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Premisenone given

    For investigations based on a single gene region, several of the robust methods discussed above are not applicable.

    “This is particularly important for investigations based on a single gene region, as several of the methods discussed above are not applicable in this case.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationcitation

    Colocalization methods may provide helpful evidence supporting or questioning the Mendelian randomization assumptions in some cases, but in other cases they may provide no strong evidence for or against colocalization.

    “Hence, while in some cases colocalization methods will provide helpful evidence supporting or questioning the Mendelian randomization assumptions, in other cases they may provide no strong evidence for or against colocalization 112 .”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [112]

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    A colocalization analysis revealed distinct causal variants for LDL-cholesterol and Alzheimer's disease at the APOE locus, indicating that variants in this gene region are not valid instruments assessing the effect of LDL-cholesterol on Alzheimer's disease.

    “A colocalization analysis revealed distinct causal variants for LDL-cholesterol and Alzheimer’s disease at the APOE locus, indicating that variants in this gene region are not valid instruments assessing the effect of LDL-cholesterol on Alzheimer’s disease 112 .”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [112]

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    A further possible sensitivity analysis is to check the genetic associations with other variables associated with the outcome, and which are thought not to lie on the causal pathway through the exposure (i.e. are not mediators).

    “A further possible sensitivity analysis is to check the genetic associations with other variables associated with the outcome, and which are thought not to lie on the causal pathway through the exposure (i.e. are not mediators).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationcitation

    As sex cannot be affected by autosomal genetic variants, sex cannot be a collider, and so stratification on sex will not induce collider bias.

    “As sex cannot be affected by autosomal genetic variants, sex cannot be a collider, and so stratification on sex will not induce collider bias 119 .”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [119]

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationcitation

    A further possibility is to perform a multivariable Mendelian randomization, including putative pleiotropic variables as additional exposures in the analysis model.

    “A further possibility in this case is to perform a multivariable Mendelian randomization, including the putative pleiotropic variables as additional exposures in the analysis model 120 .”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [120]

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Simulation studies have shown that selection bias can have a severe impact on Mendelian randomization estimates, but only when the selection effects are quite strong.

    “Simulation studies have shown that selection bias can have a severe impact on Mendelian randomization estimates, but only when the selection effects are quite strong 72, 122 .”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [72] L Corbin, 2016, BMI as a Modifiable Risk Factor for Type 2 Diabetes: Refining and Understanding Causal Estimates Using Mendelian Randomization., Diabetes. source

      Not checked in the free run.

    • cites [122]

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Collider bias is not unique to Mendelian randomization, but it is particularly relevant as some published genetic association estimates have been adjusted for potential colliders.

    “Collider bias is not unique to Mendelian randomization, but it is particularly relevant as some published genetic association estimates have been adjusted for potential colliders 123 .”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [123]

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisenone given

    Selection bias is a specific example of collider bias which occurs when selection into a study sample depends on a collider.

    “Selection bias is a specific example of collider bias which occurs when selection into a study sample depends on a collider.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Specific weights to reduce selection bias have been proposed for the UK Biobank study.

    “Specific weights to reduce selection bias have been proposed for the UK Biobank study 131 .”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [131]

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    Selection bias can potentially be addressed using inverse-probability weighting, although this requires estimation of the probability of selection into the study sample for all participants.

    “Selection bias can potentially be addressed using inverse-probability weighting 130 , although this requires estimation of the probability of selection into the study sample for all participants.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [130]

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    This analysis will estimate the direct effect of the exposure on the outcome keeping these variables constant.

    “This analysis will estimate the direct effect of the exposure on the outcome keeping these variables constant.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationnone given

    In two-step Mendelian randomization, separate instrumental variables are required for the exposure and mediator.

    “Each of these steps can be performed using standard Mendelian randomization, although separate instrumental variables are required for the exposure and mediator.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationnone given

    A weakness of factorial Mendelian randomization investigations is that statistical power to detect an interaction is often low, in which case a null finding should not be interpreted as strong evidence for lack of interaction.

    “A weakness of these investigations is that statistical power to detect an interaction is often low, in which case a null finding should not be interpreted as strong evidence for lack of interaction.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationcitation

    Researchers should be cautious when performing time-varying Mendelian randomization analyses to ensure that the values of the exposure at different time periods genuinely represent biologically distinct risk factors, and not simply measures taken at different times but capturing the same essential risk factor.

    “Researchers should be cautious when performing such analyses to ensure that the values of the exposure at different time periods genuinely represent biologically distinct risk factors, and not simply measures taken at different times but capturing the same essential risk factor 144 .”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [144]

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The doubly-ranked stratification method has been proposed that may be less sensitive to variability in these genetic effects.

    “The doubly-ranked stratification method has been proposed that may be less sensitive to variability in these genetic effects 139 .”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [139]

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Variability in the effect of genetic variants on the exposure is evident for several exposures; this variability can lead to highly misleading estimates.

    “Indeed, variability in the effect of genetic variants on the exposure is evident for several exposures; this variability can lead to highly misleading estimates 137, 138 .”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [137]

      Not checked in the free run.

    • cites [138]

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    The genetic code is fixed at conception, and so Mendelian randomization investigations typically compare groups of the population having different trajectories in their distribution of the exposure over time.

    “The genetic code is fixed at conception, and so Mendelian randomization investigations typically compare groups of the population having different trajectories in their distribution of the exposure over time 161 .”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [161]

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    A scatter plot of genetic associations with the outcome against genetic associations with the exposure reveals whether different genetic variants provide similar estimates of the causal effect or if there is considerable heterogeneity, and whether the analysis is dominated by a single genetic variant.

    “For example, in a polygenic analysis, a scatter plot of the genetic associations with the outcome against the genetic associations with the exposure reveals much about the analysis – whether different genetic variants provide similar estimates of the causal effect or if there is considerable heterogeneity, and whether the analysis is dominated by a single genetic variant or not 31 .”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [31] J Labrecque, 2019, Interpretation and Potential Biases of Mendelian Randomization Estimates With Time-Varying Exposures., Am J Epidemiol. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    Plots allow investigators and readers to assess the reliability of the analysis method and its underlying assumptions.

    “Plots allow the investigators and readers to assess the reliability of the analysis method and its underlying assumptions, and we strongly recommend their inclusion in a manuscript.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    For multivariable Mendelian randomization, the conditional F statistic is a more relevant measure of instrument strength, and assesses the strength of association of the variants with each exposure in turn after accounting for the other exposures in the model.

    “For multivariable Mendelian randomization, the conditional F statistic is a more relevant measure of instrument strength, and assesses the strength of association of the variants with each exposure in turn after accounting for the other exposures in the model 158 .”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [158]

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    Even if a Mendelian randomization finding is replicated in a separate dataset, there is still intrinsic uncertainty in the instrumental variable assumptions, meaning that uncertainty in a causal conclusion remains.

    “Even if a Mendelian randomization finding is replicated in a separate dataset, there is still intrinsic uncertainty in the instrumental variable assumptions, meaning that uncertainty in a causal conclusion remains.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Results from Mendelian randomization investigations have often been shown to qualitatively agree with the results from randomized trials, suggesting that a causal interpretation for Mendelian randomization findings is often reasonable.

    “That said, results from Mendelian randomization investigations have often been shown to qualitatively agree with the results from randomized trials, suggesting that a causal interpretation for Mendelian randomization findings is often reasonable 121 .”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [121]

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    The appropriate degree of caution in interpreting Mendelian randomization findings will depend on the plausibility of the instrumental variable assumptions, the concordance of estimates from different methods and different analytical approaches, the results from sensitivity and supplementary analyses, and so on.

    “The appropriate degree of caution will depend on the plausibility of the instrumental variable assumptions, the concordance of estimates from different methods and different analytical approaches, the results from sensitivity and supplementary analyses, and so on.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Premisenone given

    Power calculations are often performed post hoc, as sample sizes are rarely determined based on a proposed Mendelian randomization analysis.

    “Power calculations are often performed post hoc, as sample sizes are rarely determined based on a proposed Mendelian randomization analysis.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Premisenone given

    Power to detect a causal effect depends on the proportion of variance in the exposure explained by the genetic variants, proposed size of causal effect, sample size (for the genetic associations with the outcome), and (with a binary outcome) proportion of individuals with an outcome event.

    “Power to detect a causal effect depends on the proportion of variance in the exposure explained by the genetic variants, proposed size of causal effect, sample size (for the genetic associations with the outcome), and (with a binary outcome) proportion of individuals with an outcome event.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    The F statistic is a measure of instrument strength and can be used to judge the extent of weak instrument bias.

    “Other important information to report include the first-stage R 2 statistic (when the exposure is continuous), which is a measure of the variance in the exposure explained by the genetic variants, and (particularly in a one-sample setting) the related F statistic, which is a measure of instrument strength and can be used to judge the extent of weak instrument bias 157 .”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [157]

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.

    Interpretationreasoning

    Power calculations are more meaningful when performed prior to the analysis, and can guide investigators which exposure/outcome pairs to consider, and so focus on analyses that have a better chance of giving meaningful results.

    “Power calculations are more meaningful when performed prior to the analysis, and can guide investigators which exposure/outcome pairs to consider, and so focus on analyses that have a better chance of giving meaningful results.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The first-stage R2 statistic (when the exposure is continuous) is a measure of the variance in the exposure explained by the genetic variants.

    “Other important information to report include the first-stage R 2 statistic (when the exposure is continuous), which is a measure of the variance in the exposure explained by the genetic variants, and (particularly in a one-sample setting) the related F statistic, which is a measure of instrument strength and can be used to judge the extent of weak instrument bias 157 .”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [157]

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Forest plots, funnel plots, and radial plots each assess heterogeneity in the variant-specific causal estimates.

    “Alternatives are forest plots, funnel plots, and radial plots – each of these assesses heterogeneity in the variant-specific causal estimates 156 .”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [156]

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Particularly with the advent of summarized data and the two-sample setting, performing a Mendelian randomization analysis has become more straightforward.

    “Particularly with the advent of summarized data and the two-sample setting, performing a Mendelian randomization analysis has become more straightforward 30 .”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [30] S Burgess, 2016, Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods., Stat Med. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationcitation

    Mendelian randomization investigations are worthwhile in providing an alternative line of aetiological evidence even though the instrumental variable assumptions can never be proved beyond all doubt.

    “Mendelian randomization investigations are worthwhile in providing an alternative line of aetiological evidence even though the instrumental variable assumptions can never be proved beyond all doubt 96, 97 .”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [96] S Burgess, 2015, Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways., Int J Epidemiol. source

      Not checked in the free run.

    • cites [97] J Bowden, 2018, Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression., Int J Epidemiol. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationcitation

    The difficulty is not in performing a Mendelian randomization analysis, but rather in performing a credible analysis and providing a reasoned interpretation.

    “The difficulty is not in performing a Mendelian randomization analysis, but rather in performing a credible analysis 167 and providing a reasoned interpretation 164 .”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [167]

      Not checked in the free run.

    • cites [164]

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationnone given

    Even when a Mendelian randomization investigation is performed primarily to assess the causal role of an exposure, causal estimates can still be useful, for example to assess heterogeneity in estimates from different variants as a test of instrument validity, or to compare results from different analysis methods as an assessment of robustness.

    “But even when a Mendelian randomization investigation is performed primarily to assess the causal role of an exposure, causal estimates can still be useful, for example to assess heterogeneity in estimates from different variants as a test of instrument validity, or to compare results from different analysis methods as an assessment of robustness.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    The estimate from a Mendelian randomization investigation is better interpreted as a test statistic for a causal hypothesis and an indicator of the direction of the effect, rather than the estimated impact of a well-defined intervention at a specific point in time.

    “The estimate from a Mendelian randomization investigation is therefore better interpreted as a test statistic for a causal hypothesis and an indicator of the direction of the effect, rather than the estimated impact of a well-defined intervention at a specific point in time.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationnone given

    These elements (key elements to report) are necessary for the reader to judge the reliability of a Mendelian randomization investigation.

    “These elements are necessary for the reader to judge the reliability of a Mendelian randomization investigation.”
    Quote in the paperNot a substantial claim
84 of 84

  • The authors state this as an assumption.

    A genetic variant is associated with the exposure.

    “1) it is associated with the exposure”

  • The authors state this as an assumption.

    A genetic variant is not associated with the outcome due to confounding pathways.

    “2) it is not associated with the outcome due to confounding pathways”

  • The authors state this as an assumption.

    A genetic variant does not affect the outcome except potentially via the exposure.

    “3) it does not affect the outcome except potentially via the exposure”

  • The paper relies on this without support.

    Genetic associations with exposures and outcomes are typically reported per additional copy of a particular allele.

    “Genetic associations with exposures and outcomes are typically reported per additional copy of a particular allele.”

  • The paper relies on this without support.

    Allele alignment problems have led to incorrect results in Mendelian randomization analyses, and retractions and corrections of manuscripts.

    “allele alignment problems have led to incorrect results in Mendelian randomization analyses, and retractions and corrections of manuscripts.”

  • The paper relies on this without support.

    Some variants are associated with BMI from early childhood and others from adolescence or later.

    “Additionally, some variants are associated with BMI from early childhood and others from adolescence or later.”

  • The paper relies on this without support.

    Different methods will perform better and worse in different scenarios.

    “Different methods will perform better and worse in different scenarios, so critical thought and judgement is required.”

  • The paper relies on this without support.

    New methods for Mendelian randomization analysis tend to report simulations and applications suggesting they have advantages over previous methods.

    “New methods for Mendelian randomization analysis are appearing regularly, and (unsurprisingly) tend to report simulations and applications suggesting they have advantages over previous methods.”

  • The paper relies on this without support.

    Raised uric acid levels are known to increase risk of gout.

    “as raised uric acid levels are known to increase risk of gout.”

  • The paper relies on this without support.

    The APOE gene region contains genetic variants associated with LDL-cholesterol and Alzheimer’s disease.

    “An example of this is the APOE gene region, which contains genetic variants associated with LDL-cholesterol and Alzheimer’s disease.”

  • The authors state this as an assumption.

    The genetic predictors are instrumental variables for the exposures.

    “Under the assumption that the genetic predictors are instrumental variables for the exposures, the statistical interaction can be interpreted as an interaction between the causal effects of the exposures on the outcome on the same scale 140 .”

  • The authors state this as an assumption.

    Observed differences in the outcome associated with genetically-predicted levels of the exposure would also be seen if the exposure were intervened on.

    “In Mendelian randomization, a key assumption is that observed differences in the outcome associated with genetically-predicted levels of the exposure would also be seen if the exposure were intervened on 9, 71 .”

  • The paper relies on this without support.

    The genetic code is fixed at conception and cannot be influenced by reverse causation.

    “While the genetic code is fixed at conception and so cannot be influenced by reverse causation, if the outcome influences the risk factor, then variants that primarily affect the outcome would (in a large enough sample size) be associated with the exposure 132 .”

  • The paper relies on this without support.

    Most epidemiological studies do not recruit all individuals from the target population with equal probability.

    “Most epidemiological studies do not recruit all individuals from the target population with equal probability, and so suffer from selection bias.”

  • The paper relies on this without support.

    Making causal inferences from observational data always relies on untestable assumptions.

    “Making causal inferences from observational data always relies on untestable assumptions.”

  • The paper relies on this without support.

    The 2SLS/IVW method provides the most efficient causal estimate when combining evidence across multiple valid instrumental variables.

    “A logical consequence of the 2SLS/IVW method providing the most efficient causal estimate when combining evidence across multiple valid instrumental variables is that, under the same assumptions, the method provides the most powerful test of the presence of a causal effect.”
16 of 16

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 approach outlined here to obtaining a scaling estimate has generally held true for lipid-lowering therapies and risk of cardiovascular disease, but there are several potential reasons why the derivation of such a scaling for other therapeutic target-indication pairs may pose a challenge.

    “The approach outlined here to obtaining a scaling estimate has generally held true for lipid-lowering therapies and risk of cardiovascular disease 22,23,40,92 but there are several potential reasons why the derivation of such a scaling for other therapeutic target-indication pairs may pose a challenge.”

    From Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development (2021) · cites this paper as [40]

  • In the case of drug-target MR, there is little evidence to support presence of such compensatory mechanisms, e.g. genetic variants with large phenotypic effects appear to have the expected dose-response association with disease as compared to genetic variants of smaller phenotypic effect.

    “in the case of drug-target MR, there is little evidence to support presence of such compensatory mechanisms -e.g. genetic variants with large phenotypic effects appear to have the expected dose-response association with disease as compared to genetic variants of smaller phenotypic effect 57 40 .”

    From Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development (2021) · cites this paper as [40]

Cites

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Authors
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., & Theodoratou, E.
Affiliations
  • University of Cambridge (GB)
  • University of Bristol (GB)
  • University of Leicester (GB)
  • University of California, San Francisco (US)
  • Universidade Federal de Pelotas (BR)
  • Imperial College London (GB)
Corresponding author
Burgess, Stephen
ORCID
Venue
Wellcome Open Research
Publisher
F1000 Research Ltd
Published
2020-04-28
Volume
4
Pages
186
ISSN
2398-502X (ISSN-L)
DOI
10.12688/wellcomeopenres.15555.2
PMC
PMC7384151
OpenAlex
W2991007563
Cited by
1,273
References
109
Citations by year
2026: 14 · 2025: 173 · 2024: 448 · 2023: 303 · 2022: 177 · 2021: 121 · 2020: 35 · 2019: 1
FWCI
93.38
Type
journal article
Language
en
Update notices
new_version (2023-08-04)
Open access
Licence
Locations
  • Wellcome Open Research (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Bristol Research (University of Bristol) (acceptedVersion, cc-by-nc)
  • Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
  • Apollo (University of Cambridge) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • UCL Discovery (University College London) (submittedVersion)
  • Spiral (Imperial College London) (submittedVersion, cc-by)
  • Apollo (cc-by)
Funders
  • Economic and Social Research Council (ES/N000757/1)
  • Cancer Research UK (C31250/A22804)
  • Wellcome Trust (204623)
  • Medical Research Council (MC_UU_12013/9, MC_UU_00011/1, MC_UU_00011/5, MC_UU_12013/1)
  • British Heart Foundation (FS/18/23/33512)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Keywords
Mendelian randomization, research guidelines, genetic variants, sensitivity analysis, robust statistical methods, manuscript review, data presentation, causal inference

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.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-10.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-10.

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