This is the Glx network reported in Chaibub Neto et al 2008 and in Ferrara et
al 2008. Age was used as an additive covariate and we allowed for sex by
genotype interaction. The network differs slightly from the published network
due to improved code.
References
Chaibub Neto et al. 2008 Inferring causal phenotype networks
from segregating populations. Genetics 179: 1089-1100.
Ferrara et al. 2008 Genetic networks of liver metabolism revealed by
integration of metabolomic and transcriptomic profiling. PLoS Genetics 4:
e1000034.
See Also
qdg
Examples
data(glxnet)
glxnet.cross <- calc.genoprob(glxnet.cross)
set.seed(1234)
glxnet.cross <- sim.geno(glxnet.cross)
n.node <- nphe(glxnet.cross) - 2 ## Last two are age and sex.
markers <- glxnet.qtl <- vector("list", n.node)
for(i in 1:n.node) {
ac <- model.matrix(~ age + sex, glxnet.cross$pheno)[, -1]
ss <- summary(scanone(glxnet.cross, pheno.col = i,
addcovar = ac, intcovar = ac[,2]),
threshold = 2.999)
glxnet.qtl[[i]] <- makeqtl(glxnet.cross, chr = ss$chr, pos = ss$pos)
markers[[i]] <- find.marker(glxnet.cross, chr = ss$chr, pos = ss$pos)
}
names(glxnet.qtl) <- names(markers) <- names(glxnet.cross$pheno)[seq(n.node)]
glxnet.qdg <- qdg(cross=glxnet.cross,
phenotype.names = names(glxnet.cross$pheno[,seq(n.node)]),
marker.names = markers,
QTL = glxnet.qtl,
alpha = 0.05,
n.qdg.random.starts=10,
addcov="age",
intcov="sex",
skel.method="udgskel",
udg.order=6)
glxnet.qdg
gr <- graph.qdg(glxnet.qdg)
plot(gr)
## Or use tkplot().
## Not run:
glxnet.cross <- clean(glxnet.cross)
save(glxnet.cross, glxnet.qdg, glxnet.qtl, file = "glxnet.RData", compress = TRUE)
## End(Not run)