BRtestMeta: Applies BinomiRare (BR) test in meta-analysis based on...

In tamartsi/BinomiRare: Test the association of rare variants with a disease

Description

Given a folder with files of data prepared for BR meta-analysis, merge the files together and applies the BR test.

Usage

BRtestMeta(folder, recursive = TRUE, output.file = "BR_meta_results.csv", 
	error.log.file = "BR_meta_inconsistencies.txt", return.result.object = FALSE)

Arguments

folder	A folder in which the files from the various studies are at. Only .csv files are considered.
recursive	Should folders with the main folders be looked at?
output.file	File with meta-analysis test results.
error.log.file	File for which the meta-analysis function prints identified errors (e.g. non-matching alleles, etc).
return.result.object	Should an R data.frame with results be returned, in addition the printing of the results to file?

Value

prints results to file, and if return.result.object == TRUE, also returns a data.frame with columns: variant (variant name), n.carrier (total across studies), n.D.carrier (total across studies), chromosome, position, alleleA, other.alleles, n.studies (the number of studied that had individuals carrying the variant), pval (BR p-value), and expected.n.D.carrier (the expected number of diseased individuals among the carriers).

Note

other.allele is still not used. It is assume that the same effect allele (alleleA) is tested by all studies that privde it. If there's no match in annotation for a given vairant, information about the variant is thrown away.

Author(s)

Tamar Sofer

Examples

require(poibin)	
dir.create(file.path(getwd(), "Files_for_meta"))
setwd(file.path(getwd(), "Files_for_meta"))


variant.annot <- data.frame(variant = paste0("var_", 1:500), chromosome = 1, 
	position = 1:500, alleleA = sample(c("A", "C", "G", "T"), 
	size = 500, replace = TRUE), other.alleles = NA, stringsAsFactors = FALSE)

for (i in 1:nrow(variant.annot)){
	variant.annot$other.alleles[i] <- sample(setdiff(c("A", "C", "G", "T"), 
		variant.annot$alleleA[i]), size = 1)
}


n <- 10000
effect.size <- 0
n.in.pop <- n/5
pop.risk <- c(rep(-2.6, n.in.pop), rep(-2.2, n.in.pop), rep(-2, n.in.pop), 
			rep(-1.8, n.in.pop) , rep(-1.6, n.in.pop) )
pop.inds <- c(rep(1, n.in.pop), rep(2, n.in.pop), rep(3, n.in.pop), 
			rep(4, n.in.pop) , rep(5, n.in.pop) )

### for each population, prepare information for meta-analysis and write to file.
x <- rnorm(n, sd = 0.02)
x <- pmax(x, 0)

g <- rbinom(n, size = 2, prob = x)  ##
G <- matrix(rbinom(n*100, size = 1, prob = 0.001), nrow = n)  ### another 100 null variants
G <- cbind(g, G)
colnames(G) <- paste0("var_", 1:ncol(G))
rownames(G) <- paste0("person_", 1:nrow(G))
p <- expit(pop.risk +   g*effect.size + 20*x)
d <- rbinom(n, 1, p)

for (pop in 1:5){
	d.pop <- d[which(pop.inds == pop)]
	G.pop <- G[which(pop.inds == pop),]
	x.pop <- x[which(pop.inds == pop)]
	prob.mod <- glm(d.pop ~ x.pop, family = binomial)
	prob.d <- expit(predict(prob.mod))
	prepareForMetaBRtest(d.pop, prob.d, G.pop, variant.annot, output.file = 
		file.path(getwd(), paste0("carriers_prob_study_", pop, ".csv")), 
		test = FALSE, return.result.object = FALSE)	
}


############# Now meta-analyze results from files:
results.folder <- getwd() 

res <- BRtestMeta(folder = results.folder, return.result.object = TRUE)

tamartsi/BinomiRare documentation built on May 31, 2019, 2:56 a.m.