  The advent of high-throughput sequencing technologies has lead to vast
comparative genome sequences. The construction of gene-gene interaction
networks or dependence graphs on the genome scale is vital for understanding
the regulation of biological processes. Different dependence graphs can provide
different information.
  Some existing methods for dependence graphs based on high-order partial
correlations are sparse and not informative when there are latent variables
that can explain much of the dependence in groups of genes. Other methods of
dependence graphs based on correlations and first-order partial correlations
might have dense graphs. When genes can be divided into groups with stronger
within group dependence in gene expression than between group dependence, we
present a dependence graph based on truncated vines with latent variables that
makes use of group information and low-order partial correlations. The graphs
are not dense, and the genes that might be more central have more neighbors in
the vine dependency graph. We demonstrate the use of our dependence graph
construction on two RNA-seq data sets -- yeast and prostate cancer. There is
some biological evidence to support the relationship between genes in the
resulting dependence graphs.
  A flexible framework is provided for building dependence graphs via low-order
partial correlations and formation of groups, leading to graphs that are not
too sparse or dense. We anticipate that this approach will help to identify
groups that might be central to different biological functions.
