Mendelian Randomization Analysis in Observational Epidemiology
- Author:
Kwan LEE
1
;
Chi Yeon LIM
Author Information
- Publication Type:Review
- Keywords: Mendelian randomization analysis; Genetic epidemiology; Instrument; Causality; Confounding factors
- MeSH: Bias (Epidemiology); Case-Control Studies; Epidemiology; Genotype; Mendelian Randomization Analysis; Methods; Molecular Epidemiology; Phenotype; Pregnancy; Random Allocation; Risk Factors
- From:Journal of Lipid and Atherosclerosis 2019;8(2):67-77
- CountryRepublic of Korea
- Language:English
- Abstract: Mendelian randomization (MR) in epidemiology is the use of genetic variants as instrumental variables (IVs) in non-experimental design to make causality of a modifiable exposure on an outcome or disease. It assesses the causal effect between risk factor and a clinical outcome. The main reason to approach MR is to avoid the problem of residual confounding. There is no association between the genotype of early pregnancy and the disease, and the genotype of an individual cannot be changed. For this reason, it results with randomly assigned case-control studies can be set by regressing the measurements. IVs in MR are used genetic variants for estimating the causality. Usually an outcome is a disease and an exposure is risk factor, intermediate phenotype which may be a biomarker. The choice of the genetic variable as IV (Z) is essential to a successful in MR analysis. MR is named ‘Mendelian deconfounding’ as it gives to estimate of the causality free from biases due to confounding (C). To estimate unbiased estimation of the causality of the exposure (X) on the clinically relevant outcome (Y), Z has the 3 core assumptions (A1-A3). A1) Z is independent of C; A2) Z is associated with X; and A3) Z is independent of Y given X and C; The purpose of this review provides an overview of the MR analysis and is to explain that using an IV is proposed as an alternative statistical method to estimate causal effect of exposure and outcome under controlling for a confounder.
