eqtlMcmc {iBMQ} | R Documentation

## Bayesian Multiple eQTL mapping using MCMC

### Description

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

### Usage

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

### Arguments

`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.

### Details

The value of `mc.cores` may be ignored and set to one when the iBMQ installation does not support openMP.

### Value

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.

### References

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.

### Examples

    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)

[Package iBMQ version 1.30.0 Index]