lineup (version 0.34-1)

find.gene.pseudomarker: Find nearest peudomarker to each gene

Description

Pull out the pseudomarker that is closest to the position of each of a series of genes.

Usage

find.gene.pseudomarker(cross, pmap, geneloc, where=c("prob", "draws"))

Arguments

cross

An object of class "cross" containing data for a QTL experiment. See the help file for read.cross in the R/qtl package (http://www.rqtl.org).

pmap

A physical map of the markers in cross, with locations in Mbp. This is a list whose components are the marker locations on each chromosome.

geneloc

A data frame specifying the physical locations of the genes. There should be two columns, chr for chromosome and pos for position in Mbp. The rownames should indicate the gene names.

where

Indicates whether to pull pseudomarkers from the genotype probabilities (produced by calc.genoprob) or from the imputed genotypes (produced by sim.geno

Value

• A data frame with columns chr (the chromosome) and pmark (the name of the pseudomarker). The third column pos contains the Mbp position of the pseudomarker. The final column is the signed distance between the gene and the pseudomarker. The rownames indicate the gene names.

Details

We first convert positions (by interpolation) from those contained within cross to physical coordinates contained in pmap. We then use find.pseudomarker to identify the closest pseudomarker to each gene location.

We also include the positions of the pseudomarkers, and we print a warning message if pseudomarkers are > 2 Mbp from the respective gene.

See Also

find.pseudomarker, find.pseudomarkerpos, plotEGclass, disteg, calc.locallod

Examples

##############################
# simulate an eQTL data set
##############################
# genetic map
L <- seq(120, length=8, by=-10)
map <- sim.map(L, n.mar=L/10+1, include.x=FALSE, eq.spacing=TRUE)

# physical map: make all intervals 2x longer
pmap <- rescalemap(map, 2)

# arbitrary locations of 40 local eQTL
thepos <- unlist(map)
theppos <- unlist(pmap)
thechr <- rep(seq(along=map), sapply(map, length))
eqtl.loc <- sort(sample(seq(along=thepos), 40))

x <- sim.cross(map, n.ind=250, type="f2",
               model=cbind(thechr[eqtl.loc], thepos[eqtl.loc], 0, 0))
x$pheno$id <- factor(paste("Mouse", 1:250, sep=""))

# first 20 have eQTL with huge effects
# second 20 have essentially no effect
edata <- cbind((x$qtlgeno[,1:20] - 2)*10+rnorm(prod(dim(x$qtlgeno[,1:20]))),
               (x$qtlgeno[,21:40] - 2)*0.1+rnorm(prod(dim(x$qtlgeno[,21:40]))))
dimnames(edata) <- list(x$pheno$id, paste("e", 1:ncol(edata), sep=""))

# gene locations
theloc <- data.frame(chr=thechr[eqtl.loc], pos=theppos[eqtl.loc])
rownames(theloc) <- colnames(edata)

# mix up 5 individuals in expression data
edata[1:3,] <- edata[c(2,3,1),]
edata[4:5,] <- edata[5:4,]

##############################
# now, the start of the analysis
##############################
x <- calc.genoprob(x, step=1)

# find nearest pseudomarkers
pmark <- find.gene.pseudomarker(x, pmap, theloc, "prob")

# calculate LOD score for local eQTL
locallod <- calc.locallod(x, edata, pmark)

# take those with LOD > 100 [which will be the first 20]
edatasub <- edata[,locallod>100,drop=FALSE]

# calculate distance between individuals
#     (prop'n mismatches between obs and inferred eQTL geno)
d <- disteg(x, edatasub, pmark)

# plot distances
plot(d)

# summary of apparent mix-ups
summary(d)

# plot of classifier for first eQTL
plotEGclass(d)