R: Bootstrap to get interval estimate of QTL location

scanoneboot {qtl} | R Documentation

## Bootstrap to get interval estimate of QTL location

### Description

Nonparametric bootstrap to get an estimated confidence interval for the location of a QTL, in the context of a single-QTL model.

### Usage

    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)

### Arguments

`cross` | An object of class `cross`. See `read.cross` for details.
`chr` | The chromosome to investigate. Only one chromosome is allowed. (This should be a character string referring to the chromosomes by name.)
`pheno.col` | 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.
`model` | The phenotypic model: the usual normal model, a model for binary traits, a two-part model or non-parametric analysis
`method` | 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"`).
`addcovar` | Additive covariates; allowed only for the normal and binary models.
`intcovar` | Interactive covariates (interact with QTL genotype); allowed only for the normal and binary models.
`weights` | 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"`.
`use` | 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.
`upper` | Used only for the two-part model; if true, the "undefined" phenotype is the maximum observed phenotype; otherwise, it is the smallest observed phenotype.
`ties.random` | 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.
`start` | 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.
`maxit` | Maximum number of iterations for methods `"em"` and `"ehk"`.
`tol` | Tolerance value for determining convergence for methods `"em"` and `"ehk"`.
`n.boot` | Number of bootstrap replicates.
`verbose` | If TRUE, display information about the progress of the bootstrap.

### Details

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.

### Value

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"`.

### Author(s)

Karl W Broman, broman@wisc.edu

### References

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.

### See Also

`scanone`, `summary.scanoneboot`, `plot.scanoneboot`, `lodint`, `bayesint`

### Examples

    data(fake.f2)
    fake.f2 <- calc.genoprob(fake.f2, step=1, err=0.001)
    ## Not run: bootoutput <- scanoneboot(fake.f2, chr=13, method="hk")

    plot(bootoutput)
    summary(bootoutput)

[Package qtl version 1.46-2 Index]