1b:T779,# Generate and graph Glx network 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 ```r 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 ## Not run: gr <- graph.qdg(glxnet.qdg) plot(gr) ## Or use tkplot(). glxnet.cross <- clean(glxnet.cross) save(glxnet.cross, glxnet.qdg, glxnet.qtl, file = "glxnet.RData", compress = TRUE) ## End(Not run) ```

glxnet dataset