scantwo {qtl} | R Documentation |
Perform a two-dimensional genome scan with a two-QTL model, with possible allowance for covariates. Warning: two-dimensional genome scans on a dense grid can take a great deal of computer time and memory.
scantwo(cross, chr, pheno.col=1, method=c("em","imp","hk","mr","mr-imp","mr-argmax"), model=c("normal","binary"), addcovar=NULL, intcovar=NULL, weights=NULL, run.scanone=TRUE, incl.markers=FALSE, maxit=4000, tol=1e-4, verbose=TRUE, n.perm)
cross |
An object of class cross . See
read.cross for details. |
chr |
Vector indicating the chromosomes for which LOD scores should be calculated. |
pheno.col |
Column number in the phenotype matrix which should be used as the phenotype. |
method |
Indicates whether to use standard interval mapping (ie
the EM algorithm), imputation, Haley-Knott regression, or marker
regression. Marker regression is performed either by dropping
individuals with missing genotypes ("mr" ), or by first filling
in missing data using a single imputation ("mr-imp" ) or by the
Viterbi algorithm ("mr-argmax" ). |
model |
The phenotypic model: the usual normal model or a model for binary traits. |
addcovar |
Additive covariates. |
intcovar |
Interactive covariates (interact with QTL genotype). |
weights |
Optional weights of individuals. Should be either NULL or a vector of length n.ind containing positive weights. |
run.scanone |
If TRUE, run the function scanone
and place the results on the diagonal. |
incl.markers |
If FALSE, do calculations only at points on an evenly spaced grid. |
maxit |
Maximum number of iterations in the EM algorithm; used
only with method "em" . |
tol |
Tolerance value for determining convergence in the EM
algorithm; used only with method "em" . |
verbose |
If TRUE, display information about the progress of
calculations. For method "em" , if verbose is an integer
above 1, further details on the progress of the algorithm will be
displayed. |
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. |
The interval mapping (i.e. EM algorithm) and Haley-Knott regression
methods require that multipoint genotype probabilities are first
calculated using 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 = m + b[q1] + b[q2] + b[q1 x q2] + A g + Z d[q1] + Z d[q2] + Z d[q1 x q2] + e
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 = m + A g + e
The epistasis LOD score compares the full model to the following additive model:
y = m + b[q1] + b[q2] + A g + Z d[q1] + Z d[q2] + e
In the case that n.perm
is specified, the R function
scantwo
is called repeatedly.
If n.perm
is missing, the function returns a list with
class "scantwo"
and containing three 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, the third column is a 1/0 indicator for ease in later
pulling out only the equally spaced positions, and the fourth column
indicates whether the position is on the X chromosome or not. The
final component is a version of the results of
scanone
including sex and/or cross direction as
additive covariates, which is needed for a proper calculation of
conditional LOD scores.
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.
The X chromosome must be treated specially in QTL mapping.
As in scanone
, if both males and females are
included, male hemizygotes are allowed to be different from female
homozygotes, and the null hypothesis must be changed in order to ensure
that sex- or pgm-differences in the phenotype do not results in spurious
linkage to the X chromosome.
Karl W Broman, kbroman@jhsph.edu; Hao Wu, hao@jax.org
Churchill, G. A. and Doerge, R. W. (1994) Empirical threshold values for quantitative trait mapping. Genetics 138, 963–971.
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
,
scanone
, max.scantwo
data(fake.f2) fake.f2 <- calc.genoprob(fake.f2, step=10) out.2dim <- scantwo(fake.f2, method="hk") plot(out.2dim) ## Not run: permo.2dim <- scantwo(fake.f2, method="hk", n.perm=1000) ## End(Not run)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, addcovar=ac, intcovar=ic) plot(out)