Basic Introduction

Mendelian Randomization (MR) is a statistical method used to estimate the causal effect of exposure factors (such as lifestyle, biomarkers, and disease risk factors) on outcome variables (e.g., diseases or health outcomes). The method uses the principle of genetic variants being randomly assigned at conception, akin to natural randomized controlled trials (RCTs). Genetic variants are used as instrumental variables to infer causality between exposure and outcome.

Technical Principle

Mendelian Randomization (MR) is built upon the principle that genetic variations are randomly assigned in individuals, similar to a natural randomized controlled trial. In MR, genetic variants—typically single nucleotide polymorphisms (SNPs) that are strongly associated with an exposure factor—are used as instrumental variables (IVs) to infer the causal effect of that exposure on an outcome variable. Since genetic variants are fixed at conception and are generally not influenced by environmental or behavioral confounders, MR helps reduce confounding bias and reverse causality, common issues in observational studies.

In practice, researchers first identify genetic variants significantly associated with the exposure through genome-wide association studies (GWAS). These variants are then tested in an independent dataset for their association with the outcome. Using statistical models that link exposure, instruments, and outcomes, methods such as Inverse Variance Weighted (IVW), MR-Egger, and Weighted Median are applied to estimate causality. The validity of MR hinges on three core assumptions: (1) the instrument is strongly correlated with the exposure, (2) the instrument is independent of all confounding variables, and (3) the instrument affects the outcome only through the exposure and not through other pathways. If these assumptions hold, MR makes possible robust causal inference from observational data.

Application Directions

- Causal inference between biomarkers or environmental exposures and disease outcomes

- Drug target validation and drug repurposing research

- Understanding complex diseases by identifying causal risk factors

- Integrating GWAS data for multi-omics analysis in epidemiology

- Identifying early intervention targets to prevent disease progression

Technical Advantages

- MR mimics randomized controlled trials without requiring actual interventions, suitable for unethical or impractical RCTs

- Reduces bias from confounding variables due to the use of genetic instruments

- Eliminates reverse causality because genetic variants are determined before disease onset

- Provides directional and causal interpretation rather than simple associations

- Highly suitable for large-scale epidemiological studies in the post-genomic era