addpair: Scan for an additional pair of QTL in a multiple-QTL model
In qtl: Tools for Analyzing QTL Experiments
addpair R Documentation
Scan for an additional pair of QTL in a multiple-QTL model
Description
Scan for an additional pair of QTL in the context of a
multiple QTL model.
Usage
addpair(cross, chr, pheno.col=1, qtl, covar=NULL, formula,
method=c("imp","hk"), model=c("normal", "binary"),
incl.markers=FALSE, verbose=TRUE, tol=1e-4, maxit=1000,
forceXcovar=FALSE)
Arguments
cross
An object of class cross. See
read.cross for details.
chr
Optional vector indicating the chromosomes to be scanned. If
missing, all chromosomes are scanned. Refer to chromosomes by
name. Refer to chromosomes with a preceding - to have all
chromosomes but those considered. A logical (TRUE/FALSE) vector may
also be used.
pheno.col
Column number in the phenotype matrix to be
used as the phenotype. One may also give a character string matching
a phenotype name. Finally, one may give a numeric vector of
phenotypes, in which case it must have the length equal to the number
of individuals in the cross, and there must be either non-integers or
values < 1 or > no. phenotypes; this last case may be useful for studying
transformations.
qtl
An object of class qtl, as output from
makeqtl.
covar
A matrix or data.frame of covariates. These must be
strictly numeric.
formula
An object of class formula
indicating the model to be fitted. (It can also be the character
string representation of a formula.) QTLs are referred to as
Q1, Q2, etc. Covariates are referred to by their names
in the data frame covar. If the new QTL are not included in
the formula, a two-dimensional scan as in scantwo
is performed.
method
Indicates whether to use multiple imputation or
Haley-Knott regression.
model
The phenotype model: the usual model or a model for binary
traits
incl.markers
If FALSE, do calculations only at points on an
evenly spaced grid. If calc.genoprob or
sim.geno were run with
stepwidth="variable" or stepwidth="max", we force incl.markers=TRUE.
verbose
If TRUE, display information about the progress of
calculations. If verbose is an integer > 1, further messages
from scanqtl are also displayed.
tol
Tolerance for convergence for the binary trait model.
maxit
Maximum number of iterations for fitting the binary trait
model.
forceXcovar
If TRUE, force inclusion of X-chr-related covariates
(like sex and cross direction).
Details
The formula is used to specified the model to be fit. In the
formula, use Q1, Q2, etc., or q1,
q2, etc., to represent the QTLs, and the column names in the
covariate data frame to represent the covariates.
We enforce a hierarchical structure on the model formula: if a QTL or
covariate is in involved in an interaction, its main effect must also
be included.
If neither of the two new QTL are indicated in the formula, we
perform a two-dimensional scan as in scantwo. That
is, for each pair of QTL positions, we fit two models: two
additive QTL added to the formula, and two interacting QTL
added to the formula.
If the both of the new QTL are indicated in the formula, that
particular model is fit, with the positions of the new QTL allowed to
vary across the genome. If just one of the QTL is indicated in the
formula, a main effect for the other is added, and that
particular model is fit, again with the positions of both QTL varying.
Note that in this case the LOD scores are not analogous to those
produced by scantwo. Thus, there slightly modified forms
for the plots (produced by plot.scantwo) and
summaries (produced by summary.scantwo and
max.scantwo). In the plot, the x-axis is to be
interpreted as the position of the first of the new QTL, and the
y-axis is to be interpreted as the position of the second of the new
QTL. In the summaries, we give the single best pair of positions on
each pair of chromosomes, and give LOD scores comparing that pair of
positions to the base model (without each of these QTL), and to the
base model plus one additional QTL on one or the other of the chromosomes.
Value
An object of class scantwo, as produced by
scantwo.
If neither of the new QTL were indicated
in the formula, the result is just as in
scantwo, though with LOD scores relative to the
base model (omitting the new QTL).
Otherwise, the results are contained in what would ordinarily be in
the full and additive LOD scores, with the additive LOD scores
corresponding to the case that the first of the new QTL is to the left
of the second of the new QTL, and the full LOD scores corresponding to
the case that the first of the new QTL is to the right of the second
of the new QTL. Because the structure of the LOD scores in this
case is different from those output by scantwo, we
include, in this case, an attribute "addpair"=TRUE. (We also
require results of single-dimensional scans, omitting each of the two
new QTL from the formula, one at a time; these are included as
attributes "lod.minus1" and "lod.minus2".) The
results are then treated somewhat differently by
summary.scantwo, max.scantwo,
and plot.scantwo. See the Details section.
Author(s)
Karl W Broman, broman@wisc.edu
References
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.
Sen, Ś. and Churchill, G. A. (2001) A statistical framework for quantitative
trait mapping. Genetics 159, 371–387.
See Also
addint, addqtl,
fitqtl, makeqtl,
scanqtl, refineqtl,
makeqtl, scantwo,
addtoqtl
Examples
# A totally contrived example to show some of what you can do
# simulate backcross data with 3 chromosomes (names "17", "18", "19")
# one QTL on chr 17 at 40 cM
# one QTL on chr 18 at 30 cM
# two QTL on chr 19, at 10 and 40 cM
data(map10)
model <- rbind(c(1,40,0), c(2,30,0), c(3,10,0), c(3,40,0))
## Not run: fakebc <- sim.cross(map10[17:19], model=model, type="bc", n.ind=250)
# het at QTL on 17 and 1st QTL on 19 increases phenotype by 1 unit
# het at QTL on 18 and 2nd QTL on 19 decreases phenotype by 1 unit
qtlgeno <- fakebc$qtlgeno
phe <- rnorm(nind(fakebc))
w <- qtlgeno[,1]==2 & qtlgeno[,3]==2
phe[w] <- phe[w] + 1
w <- qtlgeno[,2]==2 & qtlgeno[,4]==2
phe[w] <- phe[w] - 1
fakebc$pheno[,1] <- phe
## Not run: fakebc <- calc.genoprob(fakebc, step=2, err=0.001)
# base model has QTLs on chr 17 and 18
qtl <- makeqtl(fakebc, chr=c("17", "18"), pos=c(40,30), what="prob")
# scan for an additional pair of QTL, one interacting with the locus
# on 17 and one interacting with the locus on 18
out.ap <- addpair(fakebc, qtl=qtl, formula = y~Q1*Q3 + Q2*Q4, method="hk")
max(out.ap)
summary(out.ap)
plot(out.ap)
qtl documentation built on Sept. 11, 2024, 5:43 p.m.
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| addpair | R Documentation |
Scan for an additional pair of QTL in a multiple-QTL model
Description
Scan for an additional pair of QTL in the context of a multiple QTL model.
Usage
addpair(cross, chr, pheno.col=1, qtl, covar=NULL, formula,
method=c("imp","hk"), model=c("normal", "binary"),
incl.markers=FALSE, verbose=TRUE, tol=1e-4, maxit=1000,
forceXcovar=FALSE)
Arguments
cross |
An object of class |
chr |
Optional vector indicating the chromosomes to be scanned. If
missing, all chromosomes are scanned. Refer to chromosomes by
name. Refer to chromosomes with a preceding |
pheno.col |
Column number in the phenotype matrix to be used as the phenotype. One may also give a character string matching a phenotype name. Finally, one may give a numeric vector of phenotypes, in which case it must have the length equal to the number of individuals in the cross, and there must be either non-integers or values < 1 or > no. phenotypes; this last case may be useful for studying transformations. |
qtl |
An object of class |
covar |
A matrix or data.frame of covariates. These must be strictly numeric. |
formula |
An object of class |
method |
Indicates whether to use multiple imputation or Haley-Knott regression. |
model |
The phenotype model: the usual model or a model for binary traits |
incl.markers |
If FALSE, do calculations only at points on an
evenly spaced grid. If |
verbose |
If TRUE, display information about the progress of
calculations. If |
tol |
Tolerance for convergence for the binary trait model. |
maxit |
Maximum number of iterations for fitting the binary trait model. |
forceXcovar |
If TRUE, force inclusion of X-chr-related covariates (like sex and cross direction). |
Details
The formula is used to specified the model to be fit. In the
formula, use Q1, Q2, etc., or q1,
q2, etc., to represent the QTLs, and the column names in the
covariate data frame to represent the covariates.
We enforce a hierarchical structure on the model formula: if a QTL or covariate is in involved in an interaction, its main effect must also be included.
If neither of the two new QTL are indicated in the formula, we
perform a two-dimensional scan as in scantwo. That
is, for each pair of QTL positions, we fit two models: two
additive QTL added to the formula, and two interacting QTL
added to the formula.
If the both of the new QTL are indicated in the formula, that
particular model is fit, with the positions of the new QTL allowed to
vary across the genome. If just one of the QTL is indicated in the
formula, a main effect for the other is added, and that
particular model is fit, again with the positions of both QTL varying.
Note that in this case the LOD scores are not analogous to those
produced by scantwo. Thus, there slightly modified forms
for the plots (produced by plot.scantwo) and
summaries (produced by summary.scantwo and
max.scantwo). In the plot, the x-axis is to be
interpreted as the position of the first of the new QTL, and the
y-axis is to be interpreted as the position of the second of the new
QTL. In the summaries, we give the single best pair of positions on
each pair of chromosomes, and give LOD scores comparing that pair of
positions to the base model (without each of these QTL), and to the
base model plus one additional QTL on one or the other of the chromosomes.
Value
An object of class scantwo, as produced by
scantwo.
If neither of the new QTL were indicated
in the formula, the result is just as in
scantwo, though with LOD scores relative to the
base model (omitting the new QTL).
Otherwise, the results are contained in what would ordinarily be in
the full and additive LOD scores, with the additive LOD scores
corresponding to the case that the first of the new QTL is to the left
of the second of the new QTL, and the full LOD scores corresponding to
the case that the first of the new QTL is to the right of the second
of the new QTL. Because the structure of the LOD scores in this
case is different from those output by scantwo, we
include, in this case, an attribute "addpair"=TRUE. (We also
require results of single-dimensional scans, omitting each of the two
new QTL from the formula, one at a time; these are included as
attributes "lod.minus1" and "lod.minus2".) The
results are then treated somewhat differently by
summary.scantwo, max.scantwo,
and plot.scantwo. See the Details section.
Author(s)
Karl W Broman, broman@wisc.edu
References
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.
Sen, Ś. and Churchill, G. A. (2001) A statistical framework for quantitative trait mapping. Genetics 159, 371–387.
See Also
addint, addqtl,
fitqtl, makeqtl,
scanqtl, refineqtl,
makeqtl, scantwo,
addtoqtl
Examples
# A totally contrived example to show some of what you can do
# simulate backcross data with 3 chromosomes (names "17", "18", "19")
# one QTL on chr 17 at 40 cM
# one QTL on chr 18 at 30 cM
# two QTL on chr 19, at 10 and 40 cM
data(map10)
model <- rbind(c(1,40,0), c(2,30,0), c(3,10,0), c(3,40,0))
## Not run: fakebc <- sim.cross(map10[17:19], model=model, type="bc", n.ind=250)
# het at QTL on 17 and 1st QTL on 19 increases phenotype by 1 unit
# het at QTL on 18 and 2nd QTL on 19 decreases phenotype by 1 unit
qtlgeno <- fakebc$qtlgeno
phe <- rnorm(nind(fakebc))
w <- qtlgeno[,1]==2 & qtlgeno[,3]==2
phe[w] <- phe[w] + 1
w <- qtlgeno[,2]==2 & qtlgeno[,4]==2
phe[w] <- phe[w] - 1
fakebc$pheno[,1] <- phe
## Not run: fakebc <- calc.genoprob(fakebc, step=2, err=0.001)
# base model has QTLs on chr 17 and 18
qtl <- makeqtl(fakebc, chr=c("17", "18"), pos=c(40,30), what="prob")
# scan for an additional pair of QTL, one interacting with the locus
# on 17 and one interacting with the locus on 18
out.ap <- addpair(fakebc, qtl=qtl, formula = y~Q1*Q3 + Q2*Q4, method="hk")
max(out.ap)
summary(out.ap)
plot(out.ap)
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