diff --git a/DESCRIPTION b/DESCRIPTION
index 353241d..d22f1bc 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: graph
 Title: graph: A package to handle graph data structures
-Version: 1.89.1
+Version: 1.89.2
 Authors@R: c(
         person("R", "Gentleman", role = "aut"),
         person("Elizabeth", "Whalen", role="aut"),
@@ -25,7 +25,7 @@ Description: A package that implements some simple graph handling
 License: Artistic-2.0
 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.13.11)
 Imports: stats, stats4, utils
-Suggests: SparseM (>= 0.36), XML, RBGL, RUnit, cluster, BiocStyle, knitr
+Suggests: SparseM (>= 0.36), XML, RBGL, RUnit, cluster, BiocStyle, knitr, qtl, qpgraph
 Enhances: Rgraphviz
 Collate: AllClasses.R AllGenerics.R bitarray.R buildDepGraph.R
         methods-graph.R graphNEL.R clustergraph.R NELhandler.R
diff --git a/inst/unitTests/qpgraph_test.R b/inst/unitTests/qpgraph_test.R
new file mode 100644
index 0000000..bc6e4d7
--- /dev/null
+++ b/inst/unitTests/qpgraph_test.R
@@ -0,0 +1,35 @@
+library(qtl)
+library(graph)
+library(qpgraph)
+
+## simulate an eQTLnetwork, this code is taken from section 2 of the vignette
+## entitled 'Estimate eQTL networks using qpgraph' from the 'qpgraph' package.
+## this simulation uses the API of the 'graph' package, concretely calling
+## 'subGraph()', 'nodes()', 'edges()', 'degree()', 'edgeData()', 'numEdges()',
+## 'graphBAM()', 'nodeDataDefaults()', 'edgeDataDefaults()', 'nodeData()',
+## 'edgeData()', 'numNodes()', 'numEdges()', 'edgemode()' and 'addEdge()'
+test_eQTLnetwork <- function() {
+    ## Simulate a genetic map using the R/CRAN package qtl, consisting of
+    ## nine chromosomes, being 100 cM long with 10 markers equally spaced along
+    ## each of them, no telomeric markers and no X sexual chromosome.
+    map <- sim.map(len=rep(100, times=9),
+                   n.mar=rep(10, times=9),
+                   anchor.tel=FALSE,
+                   eq.spacing=TRUE,
+                   include.x=FALSE)
+
+    ## Simulate an eQTL network consisting of 50 genes, where half of them have
+    ## one cis-acting (local) eQTL, there are 5 eQTL trans-acting (distant) on
+    ## 5 genes each, and each gene is connected to 2 other genes. Each eQTL has
+    ## an additive effect of a=2 and each gene-gene association has a marginal
+    ## correlation rho=0.5.
+    set.seed(12345)
+    sim.eqtl <- reQTLcross(eQTLcrossParam(map=map, genes=50, cis=0.5,
+                                          trans=rep(5, 5),
+                                          networkParam=dRegularGraphParam(d=2)),
+                           a=2, rho=0.5)
+
+    ## check that all simulated genes are connected to two other genes in the
+    ## network
+    checkTrue(all(degree(sim.eqtl$model$g, sim.eqtl$model$Y) == 3))
+}