Mendelian randomisation

Specific approaches and limitations in (multi)-omic Mendelian randomization

  • Purpose: This paper reviews critical methodological challenges in Mendelian Randomization (MR) when applied to complex exposures derived from multi-omics data.
  • Key Challenge: Pleiotropy: The authors advocate for using a biologically motivated strategy for selecting genetic instrumental variables (IVs) to avoid pleiotropy, such as explicitly excluding confounding genetic regions (e.g., the IL6R locus when using IL-6 levels).
  • Data Quality: The review highlights the importance of the measurement technique for the exposure (e.g., avoiding low-resolution 16S sequencing for microbiome or techniques that cannot separate lipid isomers) as data quality issues can invalidate MR assumptions and introduce pleiotropy.
23 January 2026

Transcriptome-wide Mendelian randomization study prioritising novel tissue-dependent genes for glioma susceptibility

  • Objective: This study used a two-sample Mendelian randomization (MR) and colocalization approach to identify genes whose tissue-specific expression is causally associated with the risk of glioma and its subtypes (glioblastoma and non-GBM gliomas).
  • Key Findings: MR analysis provided evidence for a causal link between the expression of 12 genes and glioma risk, including three novel genes: RETREG2/FAM134A, FAM178B, and MVB12B.
  • Tissue Specificity: The results confirmed that effects are often tissue-dependent, such as the association for MDM4 being specific to brain tissue, emphasizing the necessity of using relevant eQTL data.
23 January 2026

Harnessing tissue-specific genetic variation to dissect putative causal pathways between body mass index and cardiometabolic phenotypes

  • Objective: This MR study partitioned BMI-associated genetic variants into two distinct groups based on their association with gene expression in subcutaneous adipose tissue (metabolic) or brain tissue (appetite) to assess their differential effects on cardiometabolic risk.
  • Key Finding: The brain-mediated genetic pathway (appetite regulation) for higher BMI was found to be significantly more strongly associated with increased risk of Type 2 Diabetes Mellitus (T2DM) compared to the adipose-mediated pathway.
  • Shared Effects: Both tissue-specific pathways showed similar detrimental effects on blood lipids (LDL-C, triglycerides) and blood pressure, suggesting these are shared downstream consequences of increased BMI regardless of the initiating genetic mechanism.
23 January 2026

Prioritizing putative influential genes in cardiovascular disease susceptibility by applying tissue-specific Mendelian randomization

  • Core Method: The study developed a multi-step analysis pipeline integrating eQTL-wide association, two-sample Mendelian randomization (MR), and multiple-trait colocalization to investigate the causal pathway from genetic variants to cardiovascular traits.
  • Key Findings: MR analysis provided evidence for tissue-specific genetic effects on traits like BMI (ADCY3) and cholesterol (FADS1 and SORT1 in liver tissue), demonstrating that the biological context of gene expression is critical.
  • Mechanism Elucidation: Colocalization analyses suggested that DNA methylation may also play a role alongside gene expression, particularly at the FADS1/TMEM258 locus, and the pipeline identified a total of 233 candidate association signals for future functional studies.
23 January 2026

The challenging interpretation of instrumental variable estimates under monotonicity

  • Core Argument: The paper analyzes the interpretation difficulties of Instrumental Variable (IV) estimates that rely on the monotonicity assumption, particularly when the instrument is non-causal (e.g., in many Mendelian Randomization studies).
  • Effect Identified: IV methods typically identify the Local Average Treatment Effect (LATE), which applies only to the subgroup of compliers—those whose treatment status is modified by the instrument.
  • Policy Implications: The LATE is often not ideal for informing clinical or policy decision-making because the complier subgroup is often ill-defined or not the target of the intervention, requiring researchers to be cautious about generalization.
23 January 2026

Disentangling the aetiological pathways between body mass index and site-specific cancer risk using tissue-partitioned Mendelian randomisation

  • Objective: This study used tissue-partitioned Mendelian randomization (MR) to separate the genetic effects of BMI into brain-mediated (appetite) and adipose-mediated (metabolic) pathways to assess their independent causal impact on seven site-specific cancer risks.
  • Key Finding (Lung Cancer): The brain-mediated BMI variants were identified as the predominant causal driver of lung cancer risk (OR: 1.17), an effect which was shown to be highly correlated with an increase in cigarettes per day, suggesting a shared genetic mechanism acting via addictive behaviors.
  • Key Finding (Colorectal Cancer): A non-significant trend suggested that the adipose-mediated BMI variants were more strongly associated with colorectal cancer risk, supporting the role of metabolic consequences of adiposity in this disease.
23 January 2026

Avoiding bias from weak instruments in Mendelian randomization studies

  • Core Problem: This study addresses weak instrument bias in Mendelian Randomization (MR), which occurs when genetic variants are only weakly associated with the exposure phenotype.
  • Bias Direction: When instruments are weak, the MR causal estimate is biased toward the confounded observational association (i.e., the same bias that conventional observational studies suffer from).
  • Guideline: To minimize this bias, the paper recommends that genetic instruments used in MR studies should have a collective F-statistic greater than 10 in the exposure sample.
23 January 2026

Bias in two-sample Mendelian randomization when using heritable covariable-adjusted summary associations

  • Core Problem: This study demonstrates that using GWAS summary statistics that have been adjusted for heritable covariables (e.g., adjusting BMI for height) can introduce bias into Two-Sample Mendelian Randomization (2SMR) estimates.
  • Mechanism: Adjusting the exposure GWAS for a heritable covariable makes the resulting MR estimate prone to bias toward the null (zero), as the genetic variant is no longer a valid instrument for the total effect of the unadjusted exposure.
  • Recommendation: Researchers should prioritize using unadjusted GWAS summary statistics to estimate the total causal effect of the exposure and must be transparent about any adjustments made to the summary data used.
23 January 2026

Reclaiming mendelian randomization from the deluge of papers and misleading findings

  • Core Concern: The paper addresses the decline in quality of Mendelian Randomization (MR) research due to an overwhelming number of low-value Two-Sample MR (2SMR) papers and the premature, unvalidated application of highly complex MR methods.
  • Recommendations for Editors: Editors are advised to set a higher bar, specifically to reject papers that report only standard 2SMR findings without additional supporting evidence, methodological novelty, or validation.
  • Reviewer Focus: Reviewers must enforce rigorous quality control, demanding comprehensive sensitivity analyses (e.g., MR-Egger, Steiger filtering) and ensuring that findings, especially those from complex non-linear MR, are biologically plausible and properly validated.
23 January 2026

Characterising metabolomic signatures of lipid-modifying therapies through drug target Mendelian randomisation

  • Objective: This study employed drug target Mendelian randomization (MR) to emulate the effects of five different classes of lipid-modifying therapies and characterize their resulting causal impact on 229 circulating metabolic traits.
  • Key Findings: The analysis successfully generated distinct metabolomic signatures for each drug class, confirming known effects (e.g., Statins reducing LDL-C) while providing novel insights into their differential impact on lipoprotein subclasses; for instance, PCSK9 inhibitors showed limited effect on VLDL/triglycerides compared to Statins.
  • Implication: The findings validate drug target MR as a valuable tool for predicting the pleiotropic effects of therapies on the metabolome, which can inform drug development, safety assessment, and personalized clinical choice.
23 January 2026

Reappraising the role of instrumental inequalities for mendelian randomization studies in the mega Biobank era

  • Objective: This commentary discusses the increasing relevance and power of instrumental inequalities (IIs)—mathematical constraints derived from the core assumptions of Instrumental Variables (IV)—as a tool for detecting bias in Mendelian Randomization (MR) studies using large-scale Biobank data.
  • Instrumental Inequalities: IIs are a set of conditions that must be satisfied if the IV assumptions hold true. If the data violates these inequalities, it proves the instruments are invalid.
  • Role in Mega-Biobanks: The authors argue that the large sample sizes of modern Biobanks provide the necessary statistical power to accurately detect subtle violations of the inequalities, making this tool highly effective for falsifying the validity of genetic instruments.
  • Recommendation: IIs should be used as a complementary, stringent test alongside standard MR sensitivity analyses to enhance the statistical rigor of causal inference.

reviewing

23 January 2026

Understanding the Assumptions Underlying Instrumental Variable Analyses: a Brief Review of Falsification Strategies and Related Tools

  • Core Purpose: This review advocates for the systematic use of falsification strategies and statistical tools to assess the plausibility of the unverifiable assumptions underlying Instrumental Variable (IV) analyses, especially in Mendelian Randomization (MR).
  • Methods: While the Relevance assumption is statistically testable (e.g., F-statistic), the Exclusion Restriction and Independence assumptions can be tested for violation using methods like Negative Control Outcomes and sensitivity analyses (e.g., MR-Egger regression).
  • Recommendation: The paper stresses that epidemiologists should move beyond justifying IV assumptions solely with subject matter knowledge and routinely employ formal statistical checks to improve the robustness and transparency of causal inference.
23 January 2026

The unique challenges of studying the genetics of diet and nutrition

  • Core Problem: This comment highlights the methodological difficulties of using Mendelian Randomization (MR) to establish causal links between dietary exposures and health outcomes due to the inherent nature of diet.
  • Key Challenges: Challenges include difficulty quantifying intake and reliance on non-representative midlife measurements, the time-varying nature of diet, and its compositional constraints (change in one nutrient affects others).
  • MR Limitation: The strong correlation between nutrition and other lifestyle/environmental factors (e.g., physical activity) risks violating core MR assumptions (pleiotropy and independence), potentially leading to biased or misleading causal estimates .
23 January 2026

Cell state-dependent allelic effects and contextual Mendelian randomization analysis for human brain phenotypes

  • Objective: This study introduced a novel Contextual Mendelian Randomization (cMR) framework that leverages single-cell RNA sequencing (scRNA-seq) to identify genetic effects on gene expression that are dependent on the cell type and the cell’s state (e.g., inflammatory vs. homeostatic) in the human brain.
  • Key Findings: The cMR approach identified hundreds of cell state-dependent eQTLs, and, when applied to neurological diseases, it prioritized novel causal genes missed by bulk MR; for example, linking the expression of LRRC18 and RHOBTB3 in microglia to Alzheimer’s Disease (AD) risk.
  • Significance: By resolving genetic signals to specific cellular contexts, cMR offers highly refined and biologically precise causal estimates, reducing pleiotropy and identifying superior, cell state-specific drug targets for complex brain phenotypes.

methods

23 January 2026

The contribution of tissue-grouped BMI-associated gene sets to cardiometabolic-disease risk: a Mendelian randomization study

  • Objective: This Mendelian randomization (MR) study aimed to determine if grouping BMI-associated genetic variants based on differential tissue expression profiles (from GTEx data) could identify distinct causal effects on Type 2 Diabetes Mellitus (T2DM) and Coronary Artery Disease (CAD).
  • Key Finding: The study identified 17 tissue-grouped gene sets; however, all sets showed a similar association with increased risks of T2DM and CAD, with effect estimates comparable to those from randomly sampled genetic variants.
  • Conclusion: The novel approach of clustering BMI genetic instruments by tissue expression did not provide additional insight into the role of specific tissues in the genetic risk for these cardiometabolic diseases.
23 January 2026

Interpretation and Potential Biases of Mendelian Randomization Estimates With Time-Varying Exposures

  • Core Problem: This study addresses the difficulty of interpreting Mendelian Randomization (MR) results when the exposure (e.g., smoking) is time-varying and changes over an individual’s life course.
  • Interpretation: Standard MR estimates for a time-varying exposure should be interpreted as the causal effect of an intervention on the long-term average or cumulative lifetime exposure, not the effect of intervening at a specific moment (e.g., quitting at age 40).
  • Recommendation: Researchers must be explicit about this interpretation, as the estimate may not align with the causal effect relevant to clinical or public health policy interventions, necessitating the development of MR methods based on g-methods for time-varying treatments.
23 January 2026

Distinct pathway-based effects of blood pressure and body mass index on cardiovascular traits: comparison of novel Mendelian randomization approaches

  • Objective: This study introduced a novel pathway-partitioned Mendelian randomization (MR) approach (based on proximity to Mendelian disease genes) and compared its findings to the existing tissue-partitioned MR approach for Blood Pressure (BP) and Body Mass Index (BMI) effects on cardiovascular traits.
  • BP Findings: The pathway-partitioned MR showed that renal-system related IVs were significantly associated with Coronary Artery Disease (CAD), while vascular-related IVs were significantly associated with Stroke, demonstrating distinct causal pathways.
  • BMI Findings: Both the pathway-partitioned (Mental Health vs. Metabolic) and tissue-partitioned (Brain vs. Adipose) methods for BMI consistently found that genetic variants related to central/appetite regulation had a stronger causal effect on the risk of Type 2 Diabetes Mellitus (T2DM).
23 January 2026

Single-cell transcriptome-wide Mendelian randomization and colocalization analyses uncover cell-specific mechanisms in atherosclerotic cardiovascular disease

  • Objective: This study introduced a novel single-cell transcriptome-wide Mendelian randomization (scTWMR) framework to leverage cell-type-specific gene expression data from atherosclerotic plaques and identify causal genes for Atherosclerotic Cardiovascular Disease (ASCVD) risk.
  • Key Findings: The approach prioritized 23 causal genes by resolving signals to specific cell types, finding the strongest evidence in immune cells; for example, genetically predicted increased expression of HSH2D in macrophages was causally associated with increased ASCVD risk.
  • Significance: scTWMR provides a level of resolution superior to traditional bulk-tissue MR, enabling the identification of cell-specific drug targets and offering a scalable strategy for uncovering mechanistic details of complex diseases.
23 January 2026

Avoiding collider bias in Mendelian randomization when performing stratified analyses

  • Objective: This paper introduced a novel, robust method to conduct Mendelian Randomization (MR) stratified analyses while effectively avoiding the introduction of collider bias, a common pitfall when stratifying by a variable influenced by both the exposure and the outcome.
  • Methodology (Residual Collider): The solution is to construct a residual collider (\(\tilde{C}\)) by regressing the original collider (\(C\)) on the genetic instrument (\(G\)). Stratifying the MR analysis by quantiles of this residual (\(\tilde{C}\)) preserves the independence of the instrument and the stratifying variable, thus ensuring the validity of the causal estimate.
  • Conclusion: The study validates that stratification by the residual collider provides unbiased causal estimates and allows for the valid investigation of causal effect heterogeneity across population subgroups defined by the collider.
23 January 2026

Incorporating biological and clinical insights into variant choice for Mendelian randomisation: examples and principles

  • Core Recommendation: For Mendelian Randomisation (MR), a biologically motivated strategy for selecting genetic variants is preferred over a genome-wide approach to increase the plausibility of the No Pleiotropy instrumental variable assumption.
  • Selection Methods: Plausible variant selection includes Cis-MR (variants in the coding region of the exposure gene) and using instruments associated with a specific biomarker rather than a broad behavioral proxy.
  • Validation: The study advocates for rigorous sensitivity checks such as Multivariable MR (MVMR), positive controls, and negative controls to validate the instruments and dissect causal pathways from confounding or pleiotropy.

approaches

23 January 2026

Orienting the causal relationship between imprecisely measured traits using GWAS summary data

  • Core Problem: This research addresses how to determine the causal direction between two highly correlated traits (\(X \rightarrow Y\) vs. \(Y \rightarrow X\)) using easily accessible GWAS summary data.
  • Key Method (Steiger Filtering): The primary method is Steiger filtering, which tests if the genetic instrument explains more variance in the intended exposure than in the intended outcome. If the reverse is true, it suggests an incorrect causal direction or unmodeled pleiotropy.
  • Impact: The method has become a routine, mandatory step in Mendelian Randomization studies to validate that the genetic variants selected are genuine instruments for the exposure and not proxies for the outcome or unmodeled confounders, thus preventing reverse causation bias.

assumptions/limitations/bias

23 January 2026

Guidelines for performing Mendelian randomization investigations: update for summer 2023

  • Core Goal: This paper provides updated guidelines for Mendelian Randomization (MR) investigations, stressing the need for a rigorous, multi-method approach to inferring causality between an exposure and an outcome using genetic variants.
  • Validity Checks: The guidelines mandate systematic testing of the core IV assumptions, particularly the Exclusion Restriction (no pleiotropy), using multiple sensitivity analyses such as MR-Egger, Weighted Median/Mode, and Steiger Filtering.
  • Best Practices: Recommended practices for Two-Sample MR include proper variant selection (clumping), careful data harmonization, and comprehensive reporting of all sensitivity analysis results, including visual plots, to ensure robustness and transparency.
23 January 2026

Brain multi-omic Mendelian randomisation to identify novel drug targets for gliomagenesis

  • Objective: This study employed a novel brain multi-omic Mendelian randomization (MR) approach, integrating brain-specific genetic, transcriptomic (eQTLs), and proteomic (pQTLs) data to identify genes and proteins causally associated with glioma risk.
  • Key Findings: The analysis identified 25 causal genes and, more specifically, 13 causal proteins. The strongest evidence pointed to two promising drug targets for glioblastoma (GBM): genetically predicted lower levels of the protein FAM178B and higher levels of the protein MDM4 both increased risk.
  • Significance: The work validates the use of brain-specific proteomic data in MR to enhance the prioritization of highly relevant molecular drug targets for neurological cancers.
23 January 2026
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