QTLRel (version 0.1)

scanOne4p: Genome Scan

Description

Evaluate P-values at scanning loci along the genome.

Usage

scanOne4p(y, x, gdat, prdat=NULL, vc=NULL, intcovar=NULL,
   test=c("F","Chisq","None"),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.

vc

an object from estVC or aicVC, or an estimated variance-covariance matrix induced by relatedness and environment.

intcovar

Covariates that interact with QTL.

test

"F", "Chisq", or "None".

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 the following components if polygenic variation is assumed:
• 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"
• 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.

See Also

genoImpute and genoProb.

Examples

data(miscEx)

gdat.imp<- genoImpute(gdat, gmap=genMap, step=Inf,
   gr=2, na.str=NA)
y<- rnorm(20)
x<- sample(c("Male","Female"),20,replace=TRUE)
v<- cov(matrix(rnorm(500*20),ncol=20))
o<- estVC(y, x, v = list(AA=v,DD=NULL,HH=NULL,AD=NULL,
   MH=NULL,EE=diag(20)))

pv<- scanOne4p(y=y, x=x, gdat=gdat.imp[,1:20], vc=o)
pv
plot(pv,gmap=genMap)

gdtmp<- (gdat=="AA") + (gdat=="AB")*2 + (gdat=="BB")*3
   gdtmp<- replace(gdtmp,is.na(gdtmp),0)
prDat<- genoProb(gdat=gdtmp, gmap=genMap, step=Inf,
   gr=2, method="Haldane", verbose=TRUE)

# Haley-Knott method
pv.hk<- scanOne4p(y=y, x=x, prdat=prDat, vc=o)
pv.hk
plot(pv.hk)