Step 7. Collect Information About the Current R Session.
Contact
The Centre for Artificial Intelligence Driven Drug Discovery (AIDD) at Macao Polytechnic University.
Location:
匯智樓 (WUI CHI)-4/F, N46B, Rua de Luís Gonzaga Gomes, Macau
Email:
kefengl@mpu.edu.mo
Call:
(+853) 8599 6883
Open Hours:
Mon-Fri: 10AM - 16PM
Multivariable two-sample Mendelian randomization (MVMR)
Multivariable Mendelian Randomisation (MVMR) is a form of instrumental variable analysis which estimates the direct effect of multiple exposures on an outcome using genetic variants as instruments.
Rasooly D, Peloso GM. Two-Sample Multivariable Mendelian Randomization Analysis Using R. Curr Protoc. 2021 Dec;1(12):e335. doi: 10.1002/cpz1.335. PMID: 34936225; PMCID: PMC8767787.
Mendelian randomization is a framework that uses measured variation in genes for assessing and estimating the causal effect of an exposure on an outcome. Multivariable Mendelian randomization is an extension that can assess the causal effect of multiple exposures on an outcome, and can be advantageous when considering a set (>1) of potentially correlated candidate risk factors in evaluating the causal effect of each on a health outcome, accounting for measured pleiotropy.
This can be seen, for example, in determining the causal effects of lipids and cholesterol on type 2 diabetes risk, where the correlated risk factors share genetic predictors. Similar to univariate Mendelian randomization, multivariable Mendelian randomization can be conducted using two-sample summary-level data where the gene-exposure and gene-outcome associations are derived from separate samples from the same underlying population.
The workflow for performing MVMR is as follows:
- Select instruments for the exposures (perform LD clumping if necessary).
- Format data into suitable formats for statistical analysis, ensuring consistency and completeness.
- Test for weak instruments.
- Test for horizontal pleiotropy using conventional Q-statistic estimation.
- Estimate causal effects.
- Robust causal effect estimation.
An example of multivariable Mendelian randomization analysis:
Multivariable Mendelian randomization estimates the direct effect of intelligence and education on the outcomes.
https://doi.org/10.7554/eLife.43990.004The direct effect (red arrow) excludes any effect of either intelligence (or education) that is mediated via education (intelligence) on the outcome. It requires genetic variation that explains a sufficient proportion of the variation in intelligence and education conditional on the other trait (Davey Smith and Hemani, 2014). It uses a set of SNPs that associate with intelligence and/or education at p<5×10−08.
Choose exposure data (B).
Information for exposure (B)
Choose exposure data (A).
Information for exposure (A)
Notice
This application supports 2 to 5 exposure variables. Please ensure that the number of exposures you input falls within this range to proceed with the analysis. If you have more than 5 exposure variables, consider selecting or grouping them before running the analysis.