pQTL Mediation analysis

Test mediation across set of indexed drivers

mediation_index(target, mediator, driver = NULL, annotation = NULL,
  covar_tar = NULL, covar_med = NULL, kinship = NULL,
  driver_med = NULL, driver_index = colnames(mediator),
  facet_name = "chr", index_name = "pos", ...)

## S3 method for class 'mediation_index'
plot(x, ...)

## S3 method for class 'mediation_index'
autoplot(x, ...)

ggplot_mediation_index(x, response = c("pvalue", "IC"), alpha = 0.5,
  pattern_name = "pattern", ...)

`target`	vector or 1-column matrix with target values
`mediator`	vector or 1-column matrix with mediator values
`driver`	vector or matrix with driver values
`annotation`	optional annotation data frame for mediators
`covar_tar`	optional covariates for target
`covar_med`	optional covariates for mediator
`kinship`	optional kinship matrix among individuals
`driver_med`	driver array for mediators
`driver_index`	index to driver array
`facet_name`	name of facet column (default 'chr')
`index_name`	name of index column (default 'pos')
`...`	additional parameters

data(Tmem68)
 
target <- Tmem68$target

# Mediator is Tmem68.
m <- grep("Tmem68", Tmem68$annotation$symbol)
mediator <- Tmem68$mediator[,m]

# Reconstruct 8-allele genotype probabilities.
driver_allele <- cbind(A = 1 - apply(Tmem68$qtl.geno, 1, sum), Tmem68$qtl.geno)
rownames(driver_allele) <- rownames(Tmem68$qtl.geno)

# Reduce to SNP
driver_SNP <- cbind(B6 = driver_allele[,2], rest = 1 - driver_allele[,2])
driver_med <- list(allele = driver_allele, SNP = driver_SNP)

med_index <- mediation_index(target = target,
                      mediator = mediator,
                      driver = NULL,
                      annotation = med_lod,
                      covar_tar = Tmem68$covar,
                      covar_med = Tmem68$covar,
                      driver_med = driver_med, driver_index = names(driver_med), index_name = "probs")
summary(med_index)
ggplot2::autoplot(med_index)