QTLRel (version 0.1)

scanOne: Genome Scan

Description

Evaluate maximum likelihood ratio test statistics at scanning loci along the genome.

Usage

scanOne(y, x, gdat, prdat=NULL, vc=NULL, intcovar=NULL,
   minorGenoFreq=0, rmv=TRUE, nit=25)

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 without missing values. 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. Ignored if an object prdat from

prdat

an object from genoProb. Ignored if vc is NULL.

vc

an object from estVC or aicVC, or an estimated variance-covariance matrix induced by relatedness. The scan will assume no polygenic variation if vc

intcovar

Covariates that interact with QTL.

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.

nit

number of iterations to call optim for optimization. Ignored if vc is NULL.

Value

• A list with the following components:
• lr
  maximum likelihood ratio test statistic at the snp (marker)
• snp
  snp (marker) name
• parameters
  estimated parameters at all scanning loci

Details

Maximum likelihood is evaluated at a scanning locus. If polygenic variation is assumed, the evaluation is approximated by conditioning on estimated polygenic genetic variance-covariance matrix. Normality is assumed for the random effects. The optimization function optim is adopted in the above function to estimate the parameters and maximum likelihood. The maximum likelihood ratio test statistic is defined as -2{log(H0)-log(Ha)}, where log(H0) is the log-likelihood with the assumption of no QTL effect and log(Ha) is the log-likelihood with the assumption of QTL effect. If prdat is specified, then "Haley-Knott" regression method will be implemented.

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

estVC, scanOne4p.

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)))

llk<- scanOne(y=y, x=x, gdat=gdat.imp[,1:20], vc=o)
llk
plot(llk,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
llk.hk<- scanOne(y=y, prdat=prDat, vc=o, intcovar=x)
llk.hk
plot(llk.hk)

# ignore polygenic variation
llk.0<- scanOne(y=y, gdat=gdat.imp[,1:20],intcovar=x)
llk.0