Mendelian randomization (MR) is a powerful method in epidemiology that leverages genetic variation to explore causal relationships between exposures and outcomes. By using measured genetic variants, MR aims to estimate the causal effect of an exposure on a specific outcome. The fundamental idea behind MR lies in the random assignment of genetic variants from parents to offspring, which serves as a natural experiment. Unlike traditional randomized controlled trials, MR provides unbiased estimates without directly manipulating the exposure. This design helps address issues related to reverse causation and confounding, which often complicate the interpretation of results in observational studies.
Epidemiologists strive to identify modifiable causes of health outcomes, especially those of public health concern. MR plays a crucial role in assessing whether modifying a specific trait (e.g., through interventions or policy changes) will lead to beneficial effects within a population. To achieve this, robust evidence linking the trait to the outcome is essential. However, observational epidemiological studies often struggle to distinguish correlation from causation. MR bridges this gap by providing causal evidence, allowing researchers to modify traits effectively and reduce disease burden. Notable successes include establishing causal links between smoking and lung cancer, as well as blood pressure and stroke.
Mechanisms of Mendelian Randomization MR relies on the principles of genetic inheritance to estimate causal effects. The process involves three key steps:
- Genetic Variants as Instruments: Researchers identify genetic variants (single nucleotide polymorphisms or SNPs) associated with the exposure of interest (e.g., cholesterol levels). These SNPs act as instrumental variables, mimicking a randomized experiment.
- Association with Exposure and Outcome: The identified SNPs are then tested for their association with the exposure (e.g., cholesterol levels) and the outcome (e.g., coronary heart disease). If the SNPs are associated with the exposure but not directly related to the outcome, they serve as valid instruments.
- Causal Inference: By comparing the genetic associations with the exposure and outcome, MR estimates the causal effect. Essentially, the genetic variants act as a natural randomization, allowing researchers to infer whether the exposure influences the outcome. Importantly, this approach avoids biases due to reverse causation and confounding, which plague observational studies.
In the next posts, we will discuss further use of this method of research!
Resources:
https://www.bmj.com/content/362/bmj.k601
https://www.cdc.gov/genomics/disease/mendelian_randomization.htm
