The bayesIndirect R package examines the indirect effect of a SNP on the outcome through the mediator in a Bayesian framework with a spike and slab prior.
install.packages("devtools") #The devtools package must be installed first install.packages("coda") #The coda package must be installed first devtools::install_github("SharonLutz/bayesIndirect")
For the given dataset, one can test if the SNP acts on forced explanatory volume (FEV) through the intermediate phenotype, cigarette smoking (smoke) after adjusting for age and gender. The code below runs this analysis.
library(bayesIndirect) ?bayesIndirect # For details on this function and how to choose input variables data("dataB") snp<-dataB[,"snp"] smoke<-dataB[,"smoke"] fev<-dataB[,"fev"] age<-dataB[,"age"] gender<-dataB[,"gender"] bayesIndirect(x=snp,k=smoke,y=fev,z=cbind(age,gender),nCov=2)
For this analysis we have the following output and we can see that the snp acts on FEV through the intermediate phenotype cigarette smoking since 0 is contained in the 95 percent confidence bands for Beta. Please see the reference below for more details concerning this method.
1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE gamma1 0.895653 0.305713 1.081e-03 1.209e-03 beta 0.071090 0.050192 1.775e-04 3.564e-04 alpha1 -0.001617 0.001238 4.378e-06 1.007e-05 alpha2 0.050219 0.065589 2.319e-04 3.989e-04 sig2 1.083435 0.048633 1.719e-04 1.710e-04 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma1 0.000000 1.000000 1.000000 1.0000000 1.0000000 beta -0.009462 0.033432 0.072774 0.1066164 0.1678433 alpha1 -0.004052 -0.002464 -0.001612 -0.0007666 0.0007859 alpha2 -0.077065 0.005823 0.050013 0.0942904 0.1788785 sig2 0.992504 1.049987 1.081790 1.1154274 1.1828478
Lutz SM, Hokanson JE, Sharma S, Weiss S, Raby B, Lange C. (2013) On the Integration of Expression Profiles in Genetic Association Studies: A Bayesian Approach to Determine the Path from Gene to Disease. Open Journal of Genetics. 3(3).
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