Causal inference in psychiatric research: how to critically evaluate and interpret mendelian randomization studies

Abstract

Mendelian Randomization (MR) has become an essential tool in psychiatric research offering valuable insights into the causal relationships underlying risks and consequences of psychiatric conditions. This method utilizes genetic data to infer causal effects, effectively reducing biases commonly encountered in traditional observational studies. By leveraging genetic information, MR helps to identify potential risk factors for psychiatric conditions, paving the way for more effective interventions. However, to draw reliable and meaningful conclusions from MR studies, several critical concepts must be carefully evaluated. These include instrument selection, the magnitude of effect, the strength of the causal evidence, generalizability across diverse populations, and the clinical relevance of findings. This review will explore these key concepts in depth with illustrative examples providing a comprehensive and accessible guide for clinicians and scientists to understand and interpret psychiatric MR findings. Additionally, we will discuss novel emerging techniques, such as advanced statistical methods and the integration of high-dimensional genomic data, highlighting their potential impact on the progression of MR studies. The overall aim of this review is to foster a deeper understanding of its application in psychiatric research, ultimately enhancing its ability to unravel the intricacies of psychiatric disorders and inform personalized treatment strategies.

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ORCID for Miguel Garcia-Argibay: orcid.org/0000-0002-4811-2330

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