Usage Arguments Value References
1 |
rxy |
the observed phenotypic correlation between exposure X and outcome Y |
rg1x |
the correlation between the exposure X and the observed polygenic score for X |
rg2y |
the correlation between the outcome Y and the observed polygenic score for Y |
rg1y |
the correlation between the outcome Y and the observed polygenic score for X |
rg2x |
the correlation between the exposure X and the observed polygenic score for Y |
rg1g2 |
the correlation between the two observed polygenic scores |
n |
sample size |
h2.x |
the additive genetic variance explained in exposure X under the scenario of interest |
h2.y |
the additive genetic variance explained in outcome Y under the scenario of interest |
print |
optional, enables the examination of model parameters (default = FALSE) |
constrain |
optional, agrument to constrain model parameters (default = NULL) |
The function returns a dataframe with 3 estimates
Adjusted Bxy |
the standardized estimate of the relationship between X and Y, adjusted for G1 and G2 |
Genetic confounding |
the estimate of genetic confounding |
Total effect |
This is the total effect |
1. Pingault, J.-B., O’Reilly, P. F., Schoeler, T., Ploubidis, G. B., Rijsdijk, F., & Dudbridge, F. (2018). Using genetic data to strengthen causal inference in observational research. Nature Reviews Genetics, 19(9), 566–580. https://doi.org/10.1038/s41576-018-0020-3
2. Pingault, J.-B., Rijsdijk, F., Schoeler, T., Choi, S. W., Selzam, S., Kraphol, E., O’Reilly, P. F., & Dudbridge, F. Genetic sensitivity analysis: adjusting for genetic confounding in epidemiological associations. BioRxiv.
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