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Annual Review of Biomedical Data Science

Volume 5, 2022

Phenotypic Causal Inference Using Genome-Wide Association Study Data: Mendelian Randomization and Beyond

Abstract

Summary statistics for genome-wide association studies (GWAS) are increasingly available for downstream analyses. Meanwhile, the popularity of causal inference methods has grown as we look to gather robust evidence for novel medical and public health interventions. This has led to the development of methods that use GWAS summary statistics for causal inference. Here, we describe these methods in order of their escalating complexity, from genetic associations to extensions of Mendelian randomization that consider thousands of phenotypes simultaneously. We also cover the assumptions and limitations of these approaches before considering the challenges faced by researchers performing causal inference using GWAS data. GWAS summary statistics constitute an important data source for causal inference research that offers a counterpoint to nongenetic methods when triangulating evidence. Continued efforts to address the challenges in using GWAS data for causal inference will allow the full impact of these approaches to be realized.

Keyword(s): cause, effect, genetic variant, GWAS, inference, polymorphism
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/content/journals/10.1146/annurev-biodatasci-122120-024910
2022-08-10
2024-05-10
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Phenotypic Causal Inference Using Genome-Wide Association Study Data: Mendelian Randomization and Beyond
Annual Review of Biomedical Data Science 5, 1 (2022); https://doi.org/10.1146/annurev-biodatasci-122120-024910
/content/journals/10.1146/annurev-biodatasci-122120-024910
/content/journals/10.1146/annurev-biodatasci-122120-024910
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