gsensXY: Sensivity analysis based on two polygenic scores (X and Y)

In JBPG/Gsens: R Package for a genetic sensitivity analysis

Usage

gsensXY(rxy,rg1x,rg2y,rg1y,rg2x,rg1g2,n,h2.x,h2.y,constrain=NULL,print=FALSE)

Arguments

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)

Value

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

References

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.

JBPG/Gsens documentation built on Dec. 31, 2020, 1:07 p.m.