Abstract
Mendelian randomization (MR) uses genetic variants as instrumental variables to infer whether a risk factor causally affects a health outcome. Meta‐analysis has been used historically in MR to combine results from separate epidemiological studies, with each study using a small but select group of genetic variants. In recent years, it has been used to combine genome‐wide association study (GWAS) summary data for large numbers of genetic variants. Heterogeneity among the causal estimates obtained from multiple genetic variants points to a possible violation of the necessary instrumental variable assumptions. In this article, we provide a basic introduction to MR and the instrumental variable theory that it relies upon. We then describe how random effects models, meta‐regression, and robust regression are being used to test and adjust for heterogeneity in order to improve the rigor of the MR approach.
Keywords
Mendelian randomizationInstrumental variableGenome-wide association studyCausal inferenceComputer scienceGenetic associationEconometricsMeta-analysisRegressionRegression analysisStatisticsComputational biologyGenetic variantsGeneticsMachine learningBiologyMedicineSingle-nucleotide polymorphismMathematicsGene
MeSH Terms
Genetic VariationGenome-Wide Association StudyHumansInternetMendelian Randomization AnalysisMeta-Analysis as TopicObservational Studies as TopicPolymorphismSingle NucleotideRegression AnalysisResearch DesignRisk FactorsSoftware
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Publication Info
- Year
- 2019
- Type
- review
- Volume
- 10
- Issue
- 4
- Pages
- 486-496
- Citations
- 1582
- Access
- Closed
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Cite This
Jack Bowden,
Michael V. Holmes
(2019).
Meta‐analysis and<scp>Mendelian</scp>randomization: A review.
Research Synthesis Methods
, 10
(4)
, 486-496.
https://doi.org/10.1002/jrsm.1346
Identifiers
- DOI
- 10.1002/jrsm.1346
- PMID
- 30861319
- PMCID
- PMC6973275