Meta-analysis and Mendelian randomization: A review

Meta-analysis and Mendelian randomization: A review

Received: 11 September 2018 | Revised: 14 January 2019 | Accepted: 11 February 2019 | Jack Bowden, Michael V. Holmes
The article provides a comprehensive review of meta-analysis and Mendelian randomization (MR) methods, focusing on their application in epidemiological research. MR uses genetic variants as instrumental variables to infer causal relationships between risk factors and health outcomes. The authors introduce the basic concepts of MR, including the use of directed acyclic graphs (DAGs) to explain the causal assumptions and the instrumental variable (IV) theory. They discuss the estimation of causal effects using ratio estimates and two-stage least squares (TSLS) methods, and the quantification of these effects in the context of continuous and binary outcomes. The article also covers the application of meta-analysis in MR, particularly in combining results from multiple studies or genome-wide association studies (GWAS) to improve the power and accuracy of causal inference. It highlights the challenges and pitfalls of meta-analysis in MR, such as heterogeneity among causal estimates and the need to adjust for pleiotropy, which can violate the IV assumptions. A key focus is on two-sample summary data MR, where summary statistics from two GWAS are combined to estimate causal effects. The authors explain how to detect and address heterogeneity and bias due to pleiotropy using methods like MR-Egger regression and robust meta-analytic approaches. They provide practical examples and illustrate the performance of these methods through a reanalysis of a study examining the causal effect of systolic blood pressure (SBP) on coronary heart disease (CHD). Finally, the article discusses future directions, including the extension of MR methods to correlated genetic variants and the potential for random effects meta-analysis to enhance the reliability of summary data MR analyses. The authors emphasize the importance of guidelines and best practices to ensure the principled and reliable use of MR in the era of big data.The article provides a comprehensive review of meta-analysis and Mendelian randomization (MR) methods, focusing on their application in epidemiological research. MR uses genetic variants as instrumental variables to infer causal relationships between risk factors and health outcomes. The authors introduce the basic concepts of MR, including the use of directed acyclic graphs (DAGs) to explain the causal assumptions and the instrumental variable (IV) theory. They discuss the estimation of causal effects using ratio estimates and two-stage least squares (TSLS) methods, and the quantification of these effects in the context of continuous and binary outcomes. The article also covers the application of meta-analysis in MR, particularly in combining results from multiple studies or genome-wide association studies (GWAS) to improve the power and accuracy of causal inference. It highlights the challenges and pitfalls of meta-analysis in MR, such as heterogeneity among causal estimates and the need to adjust for pleiotropy, which can violate the IV assumptions. A key focus is on two-sample summary data MR, where summary statistics from two GWAS are combined to estimate causal effects. The authors explain how to detect and address heterogeneity and bias due to pleiotropy using methods like MR-Egger regression and robust meta-analytic approaches. They provide practical examples and illustrate the performance of these methods through a reanalysis of a study examining the causal effect of systolic blood pressure (SBP) on coronary heart disease (CHD). Finally, the article discusses future directions, including the extension of MR methods to correlated genetic variants and the potential for random effects meta-analysis to enhance the reliability of summary data MR analyses. The authors emphasize the importance of guidelines and best practices to ensure the principled and reliable use of MR in the era of big data.
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