lmem.qtler (version 0.1.1)

qtl.analysis: Performs a balanced population QTL mapping analysis.

Description

Performs a balanced population QTL mapping analysis through marker-regression (Haley and Knott 1992; Martinez and Curnow 1992). This function could use any of the following populations: double haploid, F2, recombinant inbred lines, back-cross, and 4-way crosses. Performs a Single Marker Analysis, a Single Interval Mapping, or a Composite Interval Mapping analysis, and then constructs a final model with of relevant QTL. This function is for single environment single trait QTL mapping.

Usage

qtl.analysis(crossobj = crossobj, trait = "pred", step, method, threshold,distance,cofactors, window.size = 50)

Arguments

crossobj

An object of class = cross obtained from the qtl.cross function from this package, or the read.cross function from r/qtl package (Broman and Sen, 2009).This file contains phenotypic means, genotypic marker score, and genetic map data.

trait

Column name for the phenotypic trait to be analyzed.

step

Maximum distance (in cM) between positions at which the genotype probabilities are calculated, though for step = 0, probabilities are calculated only at the marker locations.

method

"SIM" or "CIM" for simple interval (SIM) or composite interval mapping (CIM).

threshold

Threshold cut-of for multi-comparison correction. Value could be either a set threshold or "Li&Ji". If a fixed threshold is desired, a numerical value representing the alpha level should be indicated. If the threshold is set to "Li&Ji", the threshold is estimated through a bonferroni correction based on the effective number of markers (Li and Ji, 2005). The effective number of markers is calculated based on a singular value decomposition of the molecular marker matrix and the Tracy-Widom statistic (Li and Ji, 2005).

distance

To avoid co-linearity, nearby markers are not allowed in the same model. This is the minimum distance within which two markers are allowed to stay in the model.

cofactors

Vector of genetic predictors to be used as cofactors.

window.size

To avoid co-linearity, marker cofactors close to the markers being tested are not allowed in the model. This is the minimum distance to allow a co-factor when testing for a specific marker. Given the resolution of common QTL studies, it is recommended to use a large window.size (i.e. 50 cM). The default is set to 50 cM.

Value

A list of two elements: all, a data-frame containing the markers, map positions, and p-values from the marker-trait test for association for all markers in the data-set; and selected, a data-frame containing selected markers (i.e. putative QTL, selected based on their p-value), their map position, and the p-values from the marker-trait test for association. This is also written as a report to qtl_reports. A profile-plot is created showing the -log(p-value) against the map position.

Details

"SIM" or "CIM" could be perform.

References

Broman KW, Sen S (2009) A Guide to QTL Mapping with R/qtl. Springer, New York Haley CS, Knott SA (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity, 69: 315-324 Li J, Ji L (2005) Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity, 95: 221-227. Martinez O, Curnow RN (1992) Estimating the locations and the sizes of the effects of quantitative trait loci using flanking markers. Theoretical and Applied Genetics 85(4): 480-488

See Also

qtl.cross mq.diagnostics and pq.diagnostics

Examples

data (DHpop_pheno)
data (DHpop_geno)
data (DHpop_map)

G.data <- DHpop_geno
map.data <- DHpop_map
P.data <- DHpop_pheno

cross.data <- qtl.cross (P.data, G.data, map.data, cross='dh',
                         heterozygotes=FALSE)

summary (cross.data)

## Not run: 
# QTL_SMA
# QTL.result <- qtl.analysis (crossobj=cross.data,step=0,
# method='SIM', trait="height", threshold="Li&Ji", distance=30, cofactors=NULL,
# window.size=30)
# ## End(Not run)

# QTL_SIM
QTL.result <- qtl.analysis ( crossobj=cross.data, step=5,
method='SIM',trait="height", threshold="Li&Ji",
distance=30,cofactors=NULL,window.size=30)

# QTL CIM
cofactors <- as.vector (QTL.result$selected$marker)

QTL.result <- qtl.analysis ( crossobj=cross.data, step=5,
method='CIM', trait="height", threshold="Li&Ji", distance=30,
cofactors=cofactors, window.size=30)