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

PubMed: 38362310 DOI: 10.1136/egastro-2023-100042 Overview generated by: Gemini 2.5 Flash, 28-11-2025

Key Findings: Prioritizing Biology for Instrument Validity

This review emphasizes that the validity of Mendelian Randomisation (MR) analyses is fundamentally determined by the choice of genetic variants used as instrumental variables (IVs). The most critical risk is horizontal pleiotropy, where a genetic variant influences the outcome through a pathway separate from the exposure of interest.

• The central argument is that a biologically motivated strategy for variant selection is generally preferred over a comprehensive genome-wide approach, as it increases the plausibility that the core IV assumptions are met.
• Genome-wide analyses, while potentially offering greater power, often introduce invalid instruments and should be viewed as complementary evidence, particularly when a clear biological signal is lacking or when assessing the robustness of findings.

Study Design and Instrumental Variable Assumptions

This paper is a Review that provides a principled discussion of IV selection in MR, an epidemiological method used to infer causal relationships using genetic variants. The validity of MR relies on three core IV assumptions:

1. Relevance: The genetic variant must be strongly associated with the exposure.
2. Independence (No Confounding): The genetic variant must not share common causes with the outcome.
3. Exclusion Restriction (No Pleiotropy): The genetic variant must not affect the outcome except through the exposure.

The paper focuses heavily on strategies to satisfy the third, most vulnerable, assumption, stating that the plausibility of meeting the assumptions is greatest when the functional relevance of the genetic variants to the exposure is clearly understood.

Methods for Biologically Motivated Selection

The authors detail practical ways to implement a biologically informed strategy, ensuring the genetic instrument acts primarily through the exposure:

• Cis-Mendelian Randomisation (Cis-MR): Restricting variants to the coding gene region of the exposure itself (e.g., a specific protein). This is considered the most reliable approach due to the clear, short-distance functional link.
• Regulatory Variants: Using variants in a gene region that encodes a key regulator of the exposure levels, rather than the exposure itself, but still with a clear biological link.
• Biomarker Selection: Choosing instruments based on their association with a circulating biomarker (e.g., plasma caffeine levels) rather than a broader behavioral phenotype (e.g., self-reported coffee consumption), to isolate the biological effect of the circulating factor.
• Consistency Assessment: When using multiple variants, evaluating the consistency of the effects of those variants on the outcome is crucial, as inconsistent effects (some raising risk, some lowering) may point to mechanism-specific effects or horizontal pleiotropy.

Validation and Sensitivity Analyses

Biological and clinical insights should inform sensitivity checks to validate the instruments:

• Positive Controls: Testing the selected IVs on an outcome where a causal link with the exposure is already well-established. This confirms the IVs are appropriately capturing the exposure’s effect.
• Negative Controls: Testing for associations in populations or with outcomes where a causal effect is biologically or clinically not expected. Finding a null association in these controls strengthens confidence in the primary analysis.
• Multivariable MR (MVMR): This method should be used to adjust for genetically predicted levels of related traits, helping to distinguish the direct effect of the exposure from the indirect effects of correlated risk factors (i.e., known pleiotropy).

Conclusions and Recommendations

The authors conclude that an optimal MR investigation requires combining statistical methods with critical biological and clinical thinking. Recommendations for researchers include:

1. Prioritizing the use of biologically plausible instruments.
2. Using sensitivity analyses (MVMR, positive/negative controls) to rigorously test the IV assumptions.
3. Reporting estimates derived from all plausible instrument selection strategies (e.g., both biologically motivated and genome-wide) to demonstrate the robustness and consistency of the causal estimate.