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A 3-Day Livestream Seminar Taught by Renato Polimanti, Ph.D.

Genetically Informed Causal Inference Through Mendelian Randomization and Other Approaches

Genome-wide association studies (GWAS) have revolutionized the investigation of human traits and diseases, generating an unprecedented amount of information. In addition to identifying genes and pathways involved in the pathogenesis of many medical conditions, GWAS data can be used to gain insights into the epidemiology of health outcomes and other complex phenotypes. In particular, genetic variation can be used as an anchor for causal inference. Indeed, the fact that genetic variation cannot be reversed by environmental factors permits genetically informed causal inference analysis to avoid some of the biases that affect observational studies (e.g., reverse causation).

This seminar provides a comprehensive theoretical background regarding the integration of causal inference and human genetics. It also offers detailed guidelines regarding how Mendelian randomization and other approaches can be used to make genetically informed causal inferences using large-scale datasets. To ensure the robustness of the results, a key portion of the seminar will be devoted to issues of data quality control, sensitivity analysis, and triangulation.

Mendelian randomization and other genetically informed causal inference analyses are being widely used by scientists to understand the dynamics linking human traits and diseases. Indeed, the availability of large-scale genome-wide datasets permits investigators to conduct these analyses quickly and inexpensively. However, there are several challenges in ensuring that the findings generated by genetically informed causal inference analyses are reliable and robust. Understanding the assumptions and implications of different Mendelian randomization methods and the meaning of sensitivity analyses can be challenging. Additionally, there are many different methods for performing genetically informed causal inference, and new ones are being developed every year. It can be difficult to figure out which approaches are best to test specific hypotheses.

This seminar will introduce causal inference and human genetics, reviewing the theoretical framework supporting the use of genetic variation as an anchor to infer causal relationships. Subsequently, it will focus on differences across Mendelian randomization approaches, reviewing assumptions and sensitivity analyses. We will also compare Mendelian randomization with other designs to perform genetically informed causal inference analyses. To more accurately model real-world scenarios, we will also discuss multivariable analyses to test mediation and moderation hypotheses. With respect to genetically informed analyses, we will focus on multivariable Mendelian randomization and genomic structural equation modeling. Additional multivariable methods will also be introduced.