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Mendelian randomization study designs represent new powerful tools available to researchers that enable causal inferences to be made about the effects of risk factors in health and disease outcomes in the context of a prospective observational study.1–4 These study designs involve estimating the association between a genetically modifiable risk factor and health and disease outcomes.1,2 If individuals with genetically lower or higher levels of a risk factor of interest are at greater or lesser risk of an outcome, then it can be inferred that the risk factor has a causal relationship to that outcome.2–4 Provided that a chosen genetic variant is strongly associated with the risk factor of interest, is not associated with other factors that might affect the risk factor, and imparts its influence on a given outcome exclusively through its link to the risk factor, these causal inferences are considered to be robust.2,3 Mendelian randomization study designs have become increasingly popular among epidemiologists in recent years as recently completed genome-wide association and genome sequencing studies have substantially increased our knowledge of the genetic factors associated with health and disease.1 Using Mendelian randomization techniques allows researchers to conduct studies that can make the kind of causal inferences that are typically only attainable from randomized controlled trials, thus avoiding much of expense, difficulty, and ethical issues that often arise with such trials.1,2,4 Furthermore, as demonstrated in an exciting recent publication in Circulation Research , titled Exploring the Causal Pathway from Telomere Length to Coronary Heart Disease: A Network Mendelian Randomization Study by Zhan et al,5 these study designs allow assessment of the influence of risk factors that are impossible to manipulate in humans, like telomere length, on disease outcomes and the …

Network Mendelian Randomization Study Design to Assess Factors Mediating the Causal Link Between Telomere Length and Heart Disease