Integrating genetic data with biological insight: A practical guide to cis-Mendelian randomization.

Abstract

Cis-Mendelian randomization is a computational approach that uses genetic variants from a biologically relevant gene region to assess the plausibility of a causal effect of a specific mechanism on an outcome of interest. As the use of cis-Mendelian randomization has recently increased due to the abundance of genetic association data for molecular quantitative traits, there is a need for informed guidance on how best to conduct such studies. Here, we review and discuss the key considerations for conducting robust cis-Mendelian randomization analyses. The main considerations include selection of the gene region of interest, the choice of the best traits to proxy the exposure of interest, variant selection and validation, and relevant sensitivity analyses. We highlight the importance of incorporating biological insight throughout the whole analysis process and that the analytical methods should be tailored to each gene region. Moreover, we point out some key differences from genome-wide Mendelian randomization-where variants are selected across the genome-and emphasize that cis-Mendelian randomization requires a distinct set of sensitivity analyses. We believe the advice we provide in this review will lead to a higher standard in planning, conducting, reviewing, and interpreting cis-Mendelian randomization studies.