| qtlVar {QTLRel} | R Documentation |
Estimate variance in a quantitative trait induced by QTL.
qtlVar(lrt, prdat, simulation = FALSE, nsim = 25)
lrt |
A data frame (a, d, ...), where 'a' and 'd' are respectively additive and dominance effects. |
prdat |
A 3-D array that provides probabilities of genotypes "AA", "AB" and "BB". If |
simulation |
Whether to use simulations to estimate the variance explained by QTL. |
nsim |
Number of simulations to perform if |
A vector displaying the estimated variance at each loci.
Correlations among observations are ignored, and this function should be used with caution.
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$DD[ii,ii])
gdtmp<- geno
gdtmp<- replace(gdtmp,is.na(gdtmp),0)
# rung 'genoProb'
prDat<- genoProb(gdat=gdtmp, gmap=gmapF8, step=Inf,
gr=8, method="Haldane", msg=TRUE)
# estimate variance components
o<- estVC(y=pheno$bwt, x=pheno$sex, v=v)
# genome scan
pv.hk<- scanOne(y=pheno$bwt, x=pheno$sex, prdat=prDat, vc=o)
# run 'qtlVar'
qef<- NULL
for(n in 1:length(pv.hk$par))
qef<- rbind(qef,pv.hk$par[[n]][c("a","d")])
qef<- as.data.frame(qef)
qtlVar(qef,prDat$pr)[1:3]
## End(Not run)