Abstract
Mendelian randomization (MR) is an increasingly common study design in infectious diseases (ID). It holds promise for identifying causes and consequences of infections where conventional epidemiology has struggled, and can highlight plausible drug targets, as shown in successful coronavirus disease 2019 (COVID-19) trials (baricitinib, tocilizumab). However, many current applications provide limited insight due to violations of core assumptions, yielding uninterpretable results. This article reviews MR principles, assumptions, and specific challenges in ID. We highlight examples violating key assumptions, noting that MR studies using infection as an exposure are particularly prone to bias compared to using infection as an outcome. We discuss the future of MR in ID, emphasizing appropriate application to address causal questions unanswerable by other methods and capitalize on emerging opportunities where MR can provide unique insights.