selQTLMPP {statgenMPP}

Multi round genome scans for QTL detection

Description

Multi round genome scans for QTL detection.

Several rounds of QTL detection are performed. First a model is fitted without cofactors. If for at least one marker the -log10(p) value is above the threshold the marker with the lowest p-Value is added as cofactor in the next round of QTL detection. This process continues until there are no new markers with a -log10(p) value above the threshold or until the maximum number of cofactors is reached.

Usage

selQTLMPP(
  MPPobj,
  trait = NULL,
  QTLwindow = 10,
  threshold = 3,
  maxCofactors = NULL,
  K = NULL,
  computeKin = FALSE,
  parallel = FALSE,
  verbose = FALSE
)

Arguments

MPPobj

An object of class gDataMPP, typically the output of either calcIBDMPP or readRABBITMPP.

trait

A character string indicating the trait QTL mapping is done for.

QTLwindow

A numerical value indicating the window around a QTL that is considered as part of that QTL.

threshold

A numerical value indicating the threshold for the -log10p value of a marker to be considered a QTL.

maxCofactors

A numerical value, the maximum number of cofactors to include in the model. If NULL cofactors are added until no new cofactors are found.

K

A list of chromosome specific kinship matrices. If NULL and computeKin = FALSE no kinship matrix is included in the models.

computeKin

Should chromosome specific kinship matrices be computed?

parallel

Should the computation of variance components be done in parallel? This requires a parallel back-end to be registered. See examples.

verbose

Should progress and intermediate plots be output?

Details

By default only family specific effects and residual variances and no kinship relations are included in the model. It is possible to include kinship relations by either specifying computeKin = TRUE. When doing so the kinship matrix is computed by averaging Z Z^t over all markers, where Z is the genotype x parents matrix for the marker. It is also possible to specify a list of precomputed chromosome specific kinship matrices in K. Note that adding a kinship matrix to the model increases the computation time a lot, especially for large populations.

Value

An object of class QTLMPP

See Also

kinshipIBD

Examples

## Read phenotypic data.
pheno <- read.delim(system.file("extdata/multipop", "AxBxCpheno.txt",
                               package = "statgenMPP"))
## Rename first column to genotype.
colnames(pheno)[1] <- "genotype"


## Compute IBD probabilities for simulated population - AxB, AxC.
ABC <- calcIBDMPP(crossNames = c("AxB", "AxC"),
                  markerFiles = c(system.file("extdata/multipop", "AxB.txt",
                                              package = "statgenMPP"),
                                  system.file("extdata/multipop", "AxC.txt",
                                              package = "statgenMPP")),
                  pheno = pheno,
                  popType = "F4DH",
                  mapFile = system.file("extdata/multipop", "mapfile.txt",
                                        package = "statgenMPP"),
                  evalDist = 5)

## Single-QTL Mapping.
ABC_SQM <- selQTLMPP(ABC, trait = "yield", maxCofactors = 0)

## Multi-QTL Mapping.
## Not run: 
## Register parallel back-end with 2 cores.
doParallel::registerDoParallel(cores = 2)

## Run multi-QTL mapping.
ABC_MQM <- selQTLMPP(ABC, trait = "yield", parallel = TRUE)

## Run multi-QTL mapping - include kinship matrix.
ABC_MQM_kin <- selQTLMPP(ABC, trait = "yield", parallel = TRUE,
                        computeKin = TRUE)

## End(Not run)