scantwo(cross, chr, pheno.col=1, method=c("em","imp","hk","mr"),
addcov=NULL, intcov=NULL, run.scanone=TRUE,
incl.markers=FALSE, maxit=4000, tol=1e-4,
trace=TRUE, n.perm)cross. See
read.cross for details.scanone
and place the results on the diagonal."em"."em"."em", if trace is an integer
above 1, further details on the progress of the algorithm will be
displayed.n.perm is missing, the function returns a list with
class "scantwo" and containing two components. The first
component is a matrix of dimension [tot.pos x tot.pos] whose upper
triangle contains the epistasis LOD scores and whose lower triangle
contains the joint LOD scores. If run.scanone=TRUE, the
diagonal contains the results of scanone. The
second component of the output is a data.frame indicating the
locations at which the two-QTL LOD scores were calculated. The first
column is the chromosome identifier, the second column is the position
in cM, and the third column is a 1/0 indicator for ease in later
pulling out only the equally spaced positions. If n.perm is specified, the function returns a matrix with two
columns, containing the maximum joint and epistasis LOD scores, across
a two-dimensional scan, for each of the permutation replicates.
calc.genoprob. The imputation
method uses the results of sim.geno. The method em is standard interval mapping by the EM algorithm
(Dempster et al. 1977; Lander and Botstein 1989).
Marker regression is simply linear regression of phenotypes on marker
genotypes (individuals with missing genotypes are discarded).
Haley-Knott regression uses the regression of phenotypes on multipoint
genotype probabilities. The imputation method uses the pseudomarker
algorithm described by Sen and Churchill (2001).
Individuals with missing phenotypes are dropped.
In the presence of covariates, the full model is $$y = \mu + \beta_{q1} + \beta_{q2} + \beta_{q1 \times q2} + A \gamma + Z \delta_{q1} + Z \delta_{q2} + Z \delta_{q1 \times q2} + \epsilon$$ where q1 and q2 are the unknown QTL genotypes at two locations, A is a matrix of covariates, and Z is a matrix of covariates that interact with QTL genotypes. The columns of Z are forced to be contained in the matrix A.
We calculate LOD scores testing comparing the full model to each of two alternatives. The joint LOD score compares the full model to the following null model: $$y = \mu + A \gamma + \epsilon$$ The epistasis LOD score compares the full model to the following additive model: $$y = \mu + \beta_{q1} + \beta_{q2} + A \gamma + Z \delta_{q1} + Z \delta_{q2} + \epsilon$$
In the case that n.perm is specified, the R function
scantwo is called repeatedly.
Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977) Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. B, 39, 1--38.
Haley, C. S. and Knott, S. A. (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69, 315--324.
Lander, E. S. and Botstein, D. (1989) Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121, 185--199.
Sen, S. and Churchill, G. A. (2001) A statistical framework for quantitative trait mapping. Genetics 159, 371--387.
Soller, M., Brody, T. and Genizi, A. (1976) On the power of experimental designs for the detection of linkage between marker loci and quantitative loci in crosses between inbred lines. Theor. Appl. Genet. 47, 35--39.
plot.scantwo, summary.scantwo,
scanonedata(fake.f2)
fake.f2 <- calc.genoprob(fake.f2, step=10)
out.2dim <- scantwo(fake.f2, method="hk")
plot(out.2dim)
<testonly>permo.2dim <- scantwo(fake.f2, method="hk", n.perm=3)</testonly>permo.2dim <- scantwo(fake.f2, method="hk",n.perm=1000)apply(permo.2dim,2,quantile,0.95)
# covariates
data(fake.bc)
fake.bc <- calc.genoprob(fake.bc, step=10)
ac <- fake.bc$pheno[,c("sex","age")]
ic <- fake.bc$pheno[,"sex"]
out <- scantwo(fake.bc, method="hk", pheno.col=1,
addcov=ac, intcov=ic)
plot(out)Run the code above in your browser using DataLab