Inferring causal relationships between risk factors and outcomes using genetic variation

Abstract

An observational correlation between a suspected risk factor and an outcome does not necessarily imply that interventions on levels of the risk factor will have a causal impact on the outcome ("correlation is not causation") If genetic variants associated with the risk factor are also associated with the outcome, this increases the plausibility that the risk factor is a causal determinant of the outcome. However, if the genetic variants in the analysis do not have a specific biological link to the risk factor, then causal claims can be spurious. We introduce the Mendelian randomization paradigm for making causal inferences using genetic variants. We consider monogenic analysis, in which genetic variants are taken from a single gene region, and polygenic analysis, which include variants from multiple regions. We focus on answering two questions: when can Mendelian randomization be used to make reliable causal inferences, and when can it make relevant causal inferences.