R: Genome scan for QTL affecting mean and/or variance

scanonevar {qtl} | R Documentation

## Genome scan for QTL affecting mean and/or variance

### Description

Genome scan with a single QTL model for loci that can affect the variance as well as the mean.

### Usage

    scanonevar(cross, pheno.col=1, mean_covar=NULL, var_covar=NULL,
               maxit=25, tol=1e-6, quiet=TRUE)

### Arguments

`cross` | An object of class `cross`. See `read.cross` for details.
`pheno.col` | Column number in the phenotype matrix which should be used as the phenotype. This must be a single value (integer index or phenotype name) or a numeric vector of phenotype values, 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.
`mean_covar` | Numeric matrix with covariates affecting the mean.
`var_covar` | Numeric matrix with covariates affecting the variances.
`maxit` | Maximum number of iterations in the algorithm to fit the model at a given position.
`tol` | Tolerance for convergence.
`quiet` | If `FALSE`, print some information about the course of the calculations.

### Value

A data frame (with class `"scanone"`, in the form output by `scanone`), with four columns: chromosome, position, the -log P-value for the mean effect, and the -log P-value for the effect on the variance. The result is given class `"scanone"`

### Author(s)

Lars Ronnegard and Karl Broman

### References

Ronnegard, L. and Valdar W. (2011) Detecting major genetic loci controlling phenotypic variability in experimental crosses. Genetics 188:435-447

Ronnegard, L. and Valdar W. (2012) Recent developments in statistical methods for detecting genetic loci affecting phenotypic variability. BMC Genetics 13:63

### See Also

`scanone`, `summary.scanone`, `calc.genoprob`, `summary.scanoneperm`

### Examples

    data(fake.bc)

    fake.bc <- calc.genoprob(fake.bc, step=2.5)
    out <- scanonevar(fake.bc)
    color <- c("slateblue", "violetred")
    plot(out, lod=1:2, col=color, bandcol="gray80")
    legend("topright", lwd=2, c("mean", "variance"), col=color)

    # use format="allpeaks" to get summary for each of mean and variance
    #  also consider format="tabByCol" or format="tabByChr"
    summary(out, format="allpeaks")

    # with sex and age as covariates
    covar <- fake.bc$pheno[,c("sex", "age")]
    out.cov <- scanonevar(fake.bc, mean_covar=covar, var_covar=covar)

[Package qtl version 1.41-6 Index]