mcmc: Sample genetic architecture and QTL network
In qtlnet: Causal Inference of QTL Networks

Description Usage Arguments Details Value Author(s) References See Also Examples

Use MCMC to alternatively sample genetic architecture and QTL network as directed acyclic graphs (DAGs).

mcmc.qtlnet(cross, pheno.col, threshold, addcov = NULL, intcov = NULL,
  nSamples = 1000, thinning = 1, max.parents = 3, M0 = NULL,
  burnin = 0.1, method = "hk", random.seed = NULL, init.edges = 0,
  saved.scores = NULL, rev.method = c("nbhd", "node.edge", "single"),
  verbose = FALSE, ...)
init.qtlnet(pheno.col, max.parents, init.edges)

`cross`	Object of class `cross`. See `read.cross`.
`pheno.col`	Phenotype identifiers from `cross` object. May be numeric, logical or character.
`threshold`	Scalar or list of thresholds, one per each node.
`addcov`	Additive covariates for each phenotype (`NULL` if not used). If entered as scalar or vector (same format as `pheno.col`), then the same `addcov` is used for all phenotypes. Altenatively, may be a list of additive covariate identifiers.
`intcov`	Interactive covariates, entered in the same manner as `addcov`.
`nSamples`	Number of samples to record.
`thinning`	Thinning rate. Number of MCMC samples is `nSamples*thinning`.
`max.parents`	Maximum number of parents to a node. This reduces the complexity of graphs and shortens run time. Probably best to consider values of 3-5.
`M0`	Matrix of 0s and 1s with initial directed graph of row->col if (row,col) entry is 1. Cycles are forbidden (e.g. 1s on diagonal or symmetric 1s across diagonal). Default (if `NULL`) is sampled by a call to `init.qtlnet`; all 0s if `init.edges` = 0 (default).
`burnin`	Proportion of MCMC samples to use as burnin. Default is 0.1 if burnin is `TRUE`. Must be between 0 and 1.
`method`	Model fitting method for `scanone`.
`random.seed`	Initialization seed for random number generator. Must be `NULL` (no reset) or positive numeric. Used in `Random`.
`init.edges`	Initial number of edges for `M0`, to be sampled using `{init.qtlnet}`. Chosen uniformly from 0 to the number of possible edges if set to `NULL`.
`saved.scores`	Updated scores, typically pre-computed by `bic.qtlnet`.
`rev.method`	Method to use for reversing edges. See details.
`verbose`	Print iteration and number of models fit.
`...`	Additional arguments. Advanced users may want to supply pre-computed `saved.scores` to speed up calculations.

Models are coded compactly as (1)(2|1)(3|1,2,4,5)(4|2)(5|2). Each parenthetical entry is a of form (node|parents); these each require a model fit, for now with scanone.

The scanone routine is run on multiple phenotypes in the network that could all have the same parents. For instance, for 5 phenotypes, if (1|2,4) is sampled, then do scanone of this model as well as (3|2,4) and (5|2,4). Setting the hidden parameter scan.parents to a value smaller than length(pheno.col) - 1 (default) disallows multiple trait scanning with more than that number of parents.

The saved.scores parameter can greatly reduce MCMC run time, by supplying pre-computed BIC scores. See bic.qtlnet. Another option is to capture saved.scores from a previous mcmc.qtlnet run with the same phenotypes (and covariates). Caution is advised as only a modest amount of checking can be done.

The init.qtlnet routine can be used to randomly find an initial causal network M0 with up to init.edges edges.

MCMC updates include delete, add or reverse edge direction. The early version of this method only considered the edge on its own (rev.method = "single"), while the neighborhood method (rev.method = "nbhd") uses the update

List of class qtlnet

`post.model`	Model code (see details).
`post.bic`	Posterior BIC
`Mav`	Model average of `M` across MCMC samples.
`freq.accept`	Frequency of acceptance M-H proposals.
`saved.scores`	Saved LOD score for each phenotype and all possible sets of the other phenotypes as parent nodes.
`all.bic`
`cross`	The `cross` object with calculated genotype probabilities.

In addition, a number of attributes are recorded:

`M0`	Initial network matrix.
`threshold`	threshold list
`nSamples`	Number of samples saved
`thinning`	Thinning rate
`pheno.col`	Phenotype columns.
`pheno.names`	Phenotype names
`addcov`	Additive covariate columns.
`intcov`	Interactive covariate columns.
`burnin`	Burnin proportion
`method`	Method used for `scanone`.
`random.seed`	Initial random number generator seed.
`random.kind`	Random number generator kind from `Random`.

Brian S. Yandell and Elias Chaibub Neto

Chiabub Neto E, Keller MP, Attie AD, Yandell BS (2010) Causal graphical models in systems genetics: a unified framework for joint inference of causal network and genetic archicecture for correlated phenotypes. Ann Appl Statist 4: 320-339. http://dx.doi.org/10.1214/09-AOAS288

Grzegorczyk and Husmeier (2008) Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move. Mach Learn 71: 265-305. http://dx.doi.org/10.1007/s10994-008-5057-7

read.cross, scanone, Random, bic.qtlnet.

data(Pscdbp)
## Not run: 
  ## Run of subset of traits. Still takes some time.
  Pscdbp.qtlnet <- mcmc.qtlnet(Pscdbp, pheno.col = c(1,2,4,5,6),
                               threshold = 3.83,
                               nSamples = 1000, thinning = 20, 
                               random.seed = 92387475, verbose = TRUE)
  save(Pscdbp.qtlnet, file = "Pscdbp.qtlnet.RData", compress = TRUE)

## End(Not run)
data(Pscdbp.qtlnet)

## Not run: 
  out.qtlnet <- mcmc.qtlnet(Pscdbp, pheno.col = 1:13,
                            threshold = 3.83,
                            nSamples = 1000, thinning = 20, 
                            random.seed = 92387475, verbose = TRUE,
                            saved.scores = Pscdbp.bic)

## End(Not run)