scanOne {QTLRel}

Genome Scan for QTL

Description

Likelihood ratio tests or F tests at scanning loci over the genome.

Usage

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

Arguments

y

A numeric vector or a numeric matrix of one column, representing a phenotype.

x

A data frame or matrix, representing covariates if not missing.

gdat

Genotype data. It should be a matrix or a data frame, with each row being a sample and each column a locus. The column names should be marker names. It is ignored if an object prdat from genoProb is used as an argument.

If gdat is not numeric, there can be more than three genotypes and all scanning loci should have the same number of genotypes.

prdat

An object from genoProb, or in the same form. It should have a class "addEff" if allelic effects are assumed to be additive (see example below).

vc

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

intc

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 "LRT".

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 if any loci have a genotype frequency smaller than minorGenoFreq.

Details

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

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

Value

A list with at least the following components:

F or LRT

the F-test or likelihood ratio test (LRT) statistic at the SNP (marker) if test is "F" or otherwise

pval

P-value at the snp (marker) if test is "F" or "LRT"

v

Variation explained by 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, gr=8, na.str=NA, msg=FALSE)
# genome scan and plotting
lrt<- scanOne(y=pheno$bwt, x=pheno$sex, gdat=gdtmp, vc=o)
lrt
plot(lrt,gmap=gmapF8)

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

# assume additive allelic effects
class(prDat)<- c(class(prDat), "addEff")
lrt.hk<- scanOne(y=pheno$bwt, intc=pheno$sex, prdat=prDat, vc=o)
lrt.hk

## End(Not run)