Nonparametric bootstrap to get an estimated confidence interval for the location of a QTL, in the context of a single-QTL model.
scanoneboot(cross, chr, pheno.col=1, model=c("normal","binary","2part","np"),
method=c("em","imp","hk","ehk","mr","mr-imp","mr-argmax"),
addcovar=NULL, intcovar=NULL, weights=NULL,
use=c("all.obs", "complete.obs"), upper=FALSE,
ties.random=FALSE, start=NULL, maxit=4000,
tol=1e-4, n.boot=1000, verbose=FALSE)
A vector of length n.boot
, giving the estimated QTL locations
in the bootstrap replicates. The results for the original data are
included as an attribute, "results"
.
An object of class cross
. See
read.cross
for details.
The chromosome to investigate. Only one chromosome is allowed. (This should be a character string referring to the chromosomes by name.)
Column number in the phenotype matrix which should 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.
The phenotypic model: the usual normal model, a model for binary traits, a two-part model or non-parametric analysis
Indicates whether to use the EM algorithm,
imputation, Haley-Knott regression, the extended Haley-Knott method,
or marker regression. Not all methods are available for all models.
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"
).
Additive covariates; allowed only for the normal and binary models.
Interactive covariates (interact with QTL genotype); allowed only for the normal and binary models.
Optional weights of individuals. Should be either NULL
or a vector of length n.ind containing positive weights. Used only
in the case model="normal"
.
In the case that multiple phenotypes are selected to be scanned, this argument indicates whether to use all individuals, including those missing some phenotypes, or just those individuals that have data on all selected phenotypes.
Used only for the two-part model; if true, the "undefined" phenotype is the maximum observed phenotype; otherwise, it is the smallest observed phenotype.
Used only for the non-parametric "model"; if TRUE, ties in the phenotypes are ranked at random. If FALSE, average ranks are used and a corrected LOD score is calculated.
Used only for the EM algorithm with the normal model and
no covariates. If NULL
, use the usual starting values; if
length 1, use random initial weights for EM; otherwise, this should
be a vector of length n+1 (where n is the number of possible
genotypes for the cross), giving the initial values for EM.
Maximum number of iterations for methods "em"
and
"ehk"
.
Tolerance value for determining convergence for methods
"em"
and "ehk"
.
Number of bootstrap replicates.
If TRUE, display information about the progress of the bootstrap.
Karl W Broman, broman@wisc.edu
We recommend against the use of the bootstrap to derive a confidence
interval for the location of a QTL; see Manichaikul et al. (2006).
Use lodint
or bayesint
instead.
The bulk of the arguments are the same as for the
scanone
function. A single chromosome should be
indicated with the chr
argument; otherwise, we focus on the
first chromosome in the input cross
object.
A single-dimensional scan on the relevant chromosome is performed. We further perform a nonparametric bootstrap (sampling individuals with replacement from the available data, to create a new data set with the same size as the input cross; some individuals with be duplicated and some omitted). The same scan is performed with the resampled data; for each bootstrap replicate, we store only the location with maximum LOD score.
Use summary.scanoneboot
to obtain the desired
confidence interval.
Manichaikul, A., Dupuis, J., Sen, Ś and Broman, K. W. (2006) Poor performance of bootstrap confidence intervals for the location of a quantitative trait locus. Genetics 174, 481--489.
Visscher, P. M., Thompson, R. and Haley, C. S. (1996) Confidence intervals in QTL mapping by bootstrap. Genetics 143, 1013--1020.
scanone
, summary.scanoneboot
,
plot.scanoneboot
,
lodint
, bayesint
data(fake.f2)
fake.f2 <- calc.genoprob(fake.f2, step=1, err=0.001)
if (FALSE) bootoutput <- scanoneboot(fake.f2, chr=13, method="hk")
bootoutput <- scanoneboot(fake.f2, chr=13, method="hk", n.boot=50)
plot(bootoutput)
summary(bootoutput)
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