Common Methods for Performing Mendelian Randomization.

Alexander Teumer

doi:10.3389/fcvm.2018.00051

Abstract

Mendelian randomization (MR) is a framework for assessing causal inference using cross-sectional data in combination with genetic information. This paper summarizes statistical methods commonly applied and strait forward to use for conducting MR analyses including those taking advantage of the rich dataset of SNP-trait associations that were revealed in the last decade through large-scale genome-wide association studies. Using these data, powerful MR studies are possible. However, the causal estimate may be biased in case the assumptions of MR are violated. The source and the type of this bias are described while providing a summary of the mathematical formulas that should help estimating the magnitude and direction of the potential bias depending on the specific research setting. Finally, methods for relaxing the assumptions and for conducting sensitivity analyses are discussed. Future researches in the field of MR include the assessment of non-linear causal effects, and automatic detection of invalid instruments.

Highlights

Observational epidemiological studies made important contributions to our understanding of common diseases by identifying important risk factors
This review describes the assumptions of Mendelian randomization (MR) and potential biases caused by violation of these assumptions, and provides an overview of commonly applied statistical methods for conducting MR analyses using individual level data as well as using Genome-wide association studies (GWAS) meta-analyses results
Large sample sizes are required to achieve sufficient statistical power for revealing causal effects, it is often possible to overcome this limitation by using the publically available genetic association results of large GWAS meta-analyses conducted during the last decade

Summary

Common Methods for Performing Mendelian Randomization

Reviewed by: Bastiaan Geelhoed, University Medical Center Groningen, Netherlands Joylene Elisabeth Siland, University of Groningen, Netherlands. Mendelian randomization (MR) is a framework for assessing causal inference using crosssectional data in combination with genetic information. This paper summarizes statistical methods commonly applied and strait forward to use for conducting MR analyses including those taking advantage of the rich dataset of SNP-trait associations that were revealed in the last decade through large-scale genome-wide association studies. Using these data, powerful MR studies are possible. The causal estimate may be biased in case the assumptions of MR are violated. Future researches in the field of MR include the assessment of non-linear causal effects, and automatic detection of invalid instruments

Introduction

MR Methods Review

Discussion