R: Genome Scan for QTL

scanOne {QTLRel} | R Documentation

## Genome Scan for QTL

### Description

Evaluate log-likelihood ratio test statistics or P-values at scanning loci along the genome.

### Usage

    scanOne(y, x, gdat, prdat = NULL, vc = NULL, intcovar = NULL,
       numGeno = FALSE, test = c("None","F","Chisq"),
       minorGenoFreq = 0, rmv = TRUE)

### Arguments

`y` | A numeric vector or a numeric matrix of one column (representing a phenotype for instance).
`x` | A data frame or matrix, representing covariates if not missing.
`gdat` | Genotype data. Should be a matrix or a data frame, with each row representing an observation and each column a marker locus. The column names should be marker names. Optional if an object `prdat` from `genoProb` is used as an argument.
`prdat` | An object from `genoProb`, or in the same form.
`vc` | An object from `estVC` or `aicVC`, or an estimated variance-covariance matrix induced by relatedness and environment.
`intcovar` | Covariates that interact with QTL.
`numGeno` | Whether to treat numeric coding of genotypes as numeric. If true, `minorGenoFreq` will be ignored.
`test` | "None", "F" or "Chisq".
`minorGenoFreq` | Specify the minimum tolerable minor genotype frequency at a scanning locus if `gdat` is used.
`rmv` | A logical variable. If true, then the scanning locus will be skipped if the minor genotype frequency at the locus is smaller than `minorGenoFreq`. Otherwise, the scanning process will stop and return with NULL.

### Details

The test at a scanning locus under the assumption of no QTL effect versus the assumption of QTL effect is performed by conditioning on the estimated polygenic genetic variance-covariance matrix. Normality is assumed for the random effects.

It is possible to extend the Haley-Knott approach to multiple-allelic cases under the assumption that allele effects are all additive. Then, `prdat` should be provided and be of class "addEff".

### Value

A list with at least the following components:

`p` | P-value at the snp (marker) if `test` is "F" or "Chisq", or the log-likelihood ratio statistic at the SNP (marker) if "test" is "None"
`v` | Percentage of variation explained by QTL related effects at the snp (marker)
`parameters` | Estimated parameters at all scanning loci, including additive effect `a` and dominance effect `d` if `prdat` is not NULL

### References

Haley, C. S., and S. A. Knott (1992). A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69: 315-324.

### See Also

`genoImpute` and `genoProb`.

### Examples

    data(miscEx)

    ## Not run:
    # impute missing genotypes
    pheno<- pdatF8[!is.na(pdatF8$bwt) & !is.na(pdatF8$sex),]
    ii<- match(rownames(pheno), rownames(gdatF8))
    geno<- gdatF8[ii,]
    ii<- match(rownames(pheno), rownames(gmF8$AA))
    v<- list(A=gmF8$AA[ii,ii], D=gmF8$DD[ii,ii])

    # estimate variance components
    o<- estVC(y=pheno$bwt, x=pheno$sex, v=v)

    # impute missing genotypes
    gdtmp<- genoImpute(geno, gmap=gmapF8, step=Inf,
            gr=8, na.str=NA, msg=FALSE)
    # genome scan and plotting
    pv<- scanOne(y=pheno$bwt, x=pheno$sex, gdat=gdtmp, vc=o)
    pv
    plot(pv,gmap=gmapF8)

    # Haley-Knott method
    gdtmp<- geno; unique(unlist(gdtmp))
       gdtmp<- replace(gdtmp,is.na(gdtmp),0)
    prDat<- genoProb(gdat=gdtmp, gmap=gmapF8, step=Inf,
       gr=8, method="Haldane", msg=TRUE)
    pv.hk<- scanOne(y=pheno$bwt, x=pheno$sex, prdat=prDat, vc=o)
    pv.hk
    plot(pv.hk)

    ## End(Not run)

[Package QTLRel version 1.0 Index]