scanOne | R Documentation |
Likelihood ratio tests or F tests at scanning loci over the genome.
scanOne(y, x, gdat, prdat = NULL, vc = NULL, intc = NULL,
numGeno = FALSE, test = c("None","F","LRT"),
minorGenoFreq = 0, rmv = TRUE)
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 If |
prdat |
An object from |
vc |
An object from |
intc |
Covariates that interact with QTL. |
numGeno |
Whether to treat numeric coding of genotypes as numeric. If true, |
test |
"None", "F" or "LRT". |
minorGenoFreq |
Specify the minimum tolerable minor genotype frequency at a scanning locus if |
rmv |
A logical variable. If true, then the scanning locus will be skipped if the minor genotype frequency at the locus is smaller than |
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".
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 |
pval |
P-value at the snp (marker) if |
v |
Variation explained by the SNP (marker) |
parameters |
Estimated parameters at all scanning loci, including additive effect |
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.
genoImpute
and genoProb
.
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)
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