QTLRel (version 0.2-14)

scanOne: 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 geno

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.

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.

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

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".

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)

# impute missing genotypes
gdatTmp<- genoImpute(gdatF8, gmap=gmapF8, step=Inf,
   gr=8, na.str=NA)

# estimate variance components
o<- estVC(y=pdatF8$bwt, x=pdatF8$sex, v=list(AA=gmF8$AA,DD=gmF8$DD,
	HH=NULL, AD=NULL, MH=NULL, EE=diag(length(pdatF8$bwt))))

# genome scan and plotting
pv<- scanOne(y=pdatF8$bwt, x=pdatF8$sex, gdat=gdatTmp, vc=o)
pv
plot(pv,gmap=gmapF8)

# Haley-Knott method
gdtmp<- gdatF8; unique(unlist(gdtmp))
   gdtmp<- replace(gdtmp,is.na(gdtmp),0)
prDat<- genoProb(gdat=gdtmp, gmap=gmapF8, step=Inf,
   gr=8, method="Haldane", verbose=TRUE)
pv.hk<- scanOne(y=pdatF8$bwt, x=pdatF8$sex, prdat=prDat, vc=o)
pv.hk
plot(pv.hk)