NBF {PAGWAS}

Normal/Bayes factors method for finding associated pathways

Description

A vector of the computed Bayes factors for the tested pathways.

Usage

NBF(y, G, P, a, b, s2, nu)

Arguments

y

Response vector of length N

G

Genotype matrix, with N rows and L columns (number of tested SNPs)

P

Pathway matrix, with L columns and M columns (number of tested pathways)

a

Hyper-parameter of the variance assumed for the integrated out SNP effects

b

Hyper-parameter of the variance assumed for the pathway effects

s2

Hyper-parameter of the Inverse-Chi-squared distribution assumed for the variance of the response vector

nu

Hyper-parameter of the Inverse-Chi-squared distribution assumed for the variance of the response vector

Value

A vector of the computed Bayes factors of the same length as the number of tested pathways

References

Evangelou, M., Dudbridge, F., Wernisch, L. (2014). Two novel pathway analysis methods based on a hierarchical model. Bioinformatics, 30(5), 690 - 697.

Examples

## Not run: 
	data(genotypes)
	G=genotypes
	data(pathways)
	data(SNPs)
	data(genes)
	snps.genes=snps.to.genes(SNPs,genes,distance=0)
	snps.paths=snps.to.pathways(pathways,snps.genes)
	P=create.pathway.df(G,snps.paths)
	y=rnorm(nrow(G),mean=0,sd=10)
	NBF(y,G,P,a,b,s2,nu)
## End(Not run)