twopartCIM {twopartqtl} | R Documentation |
Conducts composite interval mapping for phenotypes distributed as a point-mass mixture following the same procedure as QTL Cartographer. Significant covariate markers are chosen through a forward selection procedure up to a user-specified number. Selected markers are used as covariates in interval mapping procedure. Covariate marker covariates within a fixed, user-specified window of the location being tested are dropped.
twopartCIM(cross, pheno.col = 1, n.marcovar = 3, window = 10, pm.value = 0, threshold = 1, maxit = 4000, tol = 1e-04, verbose = FALSE, imp.method = c("imp", "argmax"), error.prob = 1e-04, map.function = c("haldane", "kosambi", "c-v", "morgan"), use.log = FALSE, n.perm)
cross |
An object of class cross . See read.cross for details. |
pheno.col |
Column number in the phenotype matrix to be used as the phenotype. Only one column can be analyzed at a time. |
n.marcovar |
Number of marker covariates to use |
window |
Window size, in cM |
pm.value |
Value of the point-mass observations |
threshold |
Significance threshold (p-value) for retaining covariates |
maxit |
Maximum number of iterations |
tol |
Tolerance value for determining convergence |
verbose |
In the case n.perm is specified, displays
information about the progress of the permutation tests. |
imp.method |
Method used to impute any missing marker genotype
data. See fill.geno for details. |
error.prob |
Genotyping error probability assumed when imputing the missing marker genotype data. |
map.function |
Map function used when imputing the missing marker genotype data. |
use.log |
If TRUE phenotype values not in the point-mass are log transformed. |
n.perm |
If specified, a permutation test is performed rather than an analysis of the observed data. This argument defines the number of permutation replicates. |
Missing marker genotype data are first imputed via fill.geno
according to the specified imp.method
.
Covariate markers are then identified through a forward selection process as described
in Taylor and Pollard (2009). The most significant marker is retained at each step
until n.marcovar
markers are identified. If threshold
is less
than 1, a marker will be used as a covariate only if it is significant (i.e.,
p-value < threshold). Significant markers up to n.marcovar
markers will
be retained.
Selected markers are then used as covariates in interval mapping procedure. Covariate marker covariates within a fixed, user-specified window of the location being tested are dropped. The number of covariate markers is adjusted if necessary to be at least twice the number of continuous observations.
The function returns an object of the same form as the function
scanone
:
If n.perm
is missing, the function returns the scan results as
a data.frame with three columns: chromosome, position, LOD score.
Attributes indicate the names and positions of the chosen marker
covariates.
If n.perm
> 0, the function results the results of a
permutation test: a vector giving the genome-wide maximum LOD score in
each of the permutations.
Sandra L. Taylor, sltaylor@ucdavis.edu
Taylor, S.L. and K.S. Pollard 20XX. Composite interval mapping to identify quantitative trait loci for point-mass mixture phenotypes. Genetics Research, XX, xxx–xxx
# Simulate backcross experiment Map <- sim.map(c(100), n.mar=c(22), include.x=FALSE, eq.spacing=TRUE) num.ind <- 200 QTL.info <- rbind(c(1,30,0.6,-0.02)) sim <- sim.cross.exp(map=Map, model=QTL.info, n.ind=num.ind, type="bc", keep.qtlgeno=TRUE, map.function="haldane", dist="pointmass", params=c(0.5,6,0.6)) sim <- calc.genoprob(sim, step=1) out <- twopartCIM(sim, pheno.col=1, n.marcovar=3, pm.value=0) # Permutation tests ## Not run: out.perm <- twopartCIM(sim, pheno.col=1, n.marcovar=3, pm.value=0, n.perm=1000)