Methods For Causal Inference Using Genetic Variants

Causal inference is a fundamental goal of scientific research. In particular, the ability to infer causality from observational data is of great interest in many fields, including epidemiology, economics, and social science. However, observational data can be subject to confounding, which can bias the results of causal inference analyses. Genetic variants, which are inherited from parents and are not subject to confounding, can be used to overcome this problem.

There are a number of different methods for causal inference using genetic variants. The most common method is Mendelian randomization, which uses genetic variants as instrumental variables to estimate the causal effect of an exposure on an outcome. Mendelian randomization has a number of advantages over other methods, including its ability to control for confounding and its ability to estimate causal effects in the absence of random assignment.

Mendelian Randomization: Methods for Causal Inference Using Genetic Variants (Chapman & Hall/CRC Interdisciplinary Statistics)

by Simon G. Thompson

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Other methods for causal inference using genetic variants include instrumental variables, mediation analysis, and gene-environment interaction analysis. Instrumental variables analysis is similar to Mendelian randomization, but it uses genetic variants that are not associated with the exposure to estimate the causal effect of the exposure on the outcome. Mediation analysis is used to determine whether the effect of an exposure on an outcome is mediated by a third variable. Gene-environment interaction analysis is used to determine whether the effect of an exposure on an outcome is modified by genetic variants.

Methods for Causal Inference Using Genetic Variants

• Mendelian Randomization

Mendelian randomization (MR) is a method for causal inference that uses genetic variants as instrumental variables (IVs) to estimate the causal effect of an exposure on an outcome. MR is based on the principle that genetic variants are randomly assigned at conception, and thus are not subject to confounding. This makes them ideal for use as IVs, as they can be used to control for confounding factors that may bias the results of observational studies.

MR has a number of advantages over other methods for causal inference. First, MR can be used to estimate causal effects in the absence of random assignment. This is important because it allows researchers to study the effects of exposures that are not amenable to experimental manipulation, such as smoking or air pollution.

Second, MR can be used to control for confounding factors that may bias the results of observational studies. Confounding occurs when a third variable is associated with both the exposure and the outcome, and biases the estimate of the causal effect of the exposure on the outcome. MR can control for confounding by using genetic variants as IVs. Genetic variants are not associated with confounding factors, so they can be used to estimate the causal effect of the exposure on the outcome without bias.

Third, MR can be used to estimate causal effects in the presence of unmeasured confounding. Unmeasured confounding occurs when there is a third variable that is associated with both the exposure and the outcome, but is not measured in the study. MR can control for unmeasured confounding by using genetic variants as IVs.

• Instrumental Variables

Instrumental variables (IVs) are variables that are associated with the exposure, but are not associated with the outcome, except through the exposure. This makes them ideal for use in causal inference, as they can be used to estimate the causal effect of the exposure on the outcome without bias.

Genetic variants can be used as IVs in MR because they are randomly assigned at conception, and thus are not associated with confounding factors. This makes them ideal for use in MR, as they can be used to control for confounding factors that may bias the results of observational studies.

• Mediation Analysis

Mediation analysis is a method for determining whether the effect of an exposure on an outcome is mediated by a third variable. Mediation occurs when the exposure causes the mediator, and the mediator causes the outcome. Mediation analysis can be used to determine the extent to which the effect of the exposure on the outcome is mediated by the mediator.

Genetic variants can be used as mediators in mediation analysis because they can be used to identify the causal effect of the exposure on the outcome, and the causal effect of the mediator on the outcome. This information can then be used to determine the extent to which the effect of the exposure on the outcome is mediated by the mediator.

• Gene-Environment Interaction Analysis

Gene-environment interaction (GxE) analysis is a method for determining whether the effect of an exposure on an outcome is modified by genetic variants. GxE occurs when the effect of the exposure on the outcome is different for individuals with different genotypes. GxE analysis can be used to identify genetic variants that modify the effect of the exposure on the outcome.

GxE analysis can be used to identify genetic variants that increase or decrease the risk of disease. This information can then be used to develop personalized prevention and treatment strategies for individuals with different genotypes.

Methods for causal inference using genetic variants are a powerful tool for understanding the causes of disease and other health outcomes. These methods have the potential to improve our understanding of the genetic and environmental factors that contribute to disease, and to develop more effective prevention and treatment strategies.

References

1. Smith, G. D., & Ebrahim, S. (2003). Mendelian randomization: prospects for studying gene-environment interactions in cardiovascular disease. Journal of internal medicine, 254(6),437-453.
2. Lawlor, D. A., Harbord, R. M., Sterne, J. A., Timpson, N., Davey Smith, G., &

Mendelian Randomization: Methods for Causal Inference Using Genetic Variants (Chapman & Hall/CRC Interdisciplinary Statistics)

by Simon G. Thompson

5 out of 5

Language	:	English
File size	:	16954 KB
Screen Reader	:	Supported
Print length	:	240 pages

FREE