eqtlMcmc: Bayesian Multiple eQTL mapping using MCMC
In raphg/iBMQ: integrated Bayesian Modeling of eQTL data

Description Usage Arguments Value References Examples

Compute the MCMC algorithm to produce Posterior Probability of Association values for eQTL mapping.

1 2	eqtlMcmc(snp, expr, n.iter, burn.in, n.sweep, mc.cores, write.output = TRUE, RIS = TRUE)

`snp`	SnpSet class object
`expr`	ExpressionSet class object
`n.iter`	Number of samples to be saved from the Markov Chain
`burn.in`	Number of burn-in iterations for the Markov Chain
`n.sweep`	Number of iterations between samples of the Markov Chain (AKA thinning interval)
`mc.cores`	The number of cores you would like to use for parallel processing. Can be set be set via ‘options(cores=4)’, if not set, the code will automatically detect the number of cores.
`write.output`	Write chain iterations to file. If TRUE, output for variables will be written to files created in the working directory.
`RIS`	If TRUE, the genotype needs to be either 0 and 1. If FALSE the genotype need to be either 1,2 and 3.

A matrix with Posterior Probability of Association values. Rows correspond to snps from original snp data objects, columns correspond to genes from expr data objects.

Scott-Boyer, MP., Tayeb, G., Imholte, Labbe, A., Deschepper C., and Gottardo R. An integrated Bayesian hierarchical model for multivariate eQTL mapping (iBMQ). Statistical Applications in Genetics and Molecular Biology Vol. 11, 2012.

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data(phenotype.liver)
data(genotype.liver)
#PPA.liver <-  eqtlMcmc(genotype.liver, phenotype.liver, n.iter=100,burn.in=100,n.sweep=20,mc.cores=6, RIS=FALSE)