Heart Failure ADRA2C (5) phenotypes are found close in space, such as asthma, allergic rhinitis, and atopic dermatitis. This overlap due to gene sharing recapitulates an overlap in clinical characteristics between these related disorders. Similarly, phenotypes within the metabolic group related to diabetes are closely aligned in space including; insulin resistance, hyperglycemia, hyperinsulinemia, and hyperlipidemia. This close apposition of related disease phenotypes and sub-phenotypes at both a coarse and fine level is a consistent feature of the overall display. The human gene sets used in creating this tree diagram can be found in Table S6[ 46] . It is important to emphasize that this display and the distance relationships between diseases are calculated through an unbiased gene-sharing algorithm independent of disease phenotype labels and not as a result of an imposed logical hierarchy or an ontological annotation system. This grouping of major disease phenotypes based solely on gene sharing provides supporting evidence that the underlying disease based gene sets may have a fundamental relevance to disease and may not be reported in the literature by chance alone. Dendrogram analysis of mouse phenotypic gene sets Figure 3 is a similar dendrogram to the human tree using 1056 mouse phenotypic gene sets, using gene sets each containing at least 10 genes. This was produced using the same gene sharing algorithm as for the human gene sets in Figure 2 . As with the human dendrogram, the mouse tree displays informative groupings at both a coarse and fine level. This tree groups into major groupings nominally assigned as brain development and brain function, embryonic development, cardiovascular, reproduction, inflammation, renal function, bone development, metabolism, and skin/hair development. The identification of major groupings emphasizing developmental processes reflects the emphasis of gene knockouts and developmental models resulting in observable morphological traits and less so with regard to end stage clinical diseases as in the human dendrogram. Like the human dendrogram ( Figure 2 ) discrete major functional groupings in the mouse dendrogram suggests that individual experimental observations are not random. Fundamental complex processes such as metabolism, cardiovascular phenomena, and developmental processes are integrated by extensive sharing of related pliotropic genes. Moreover, like the human tree, fine structure in the mouse tree shows related mouse phenotypes are closely positioned in space. For example, in the metabolism major grouping, the individual phenotypes of body mass, adipose phenotypes, and weight gain are closely positioned. Similarly, in the brain function group, the behavioral phenotypes of anxiety, exploration, and responses to novel objects are found next to one another. This pattern is a fundamental feature of this tree. Like the human tree, the mouse dendrogram shown here is based solely on a gene sharing algorithm using genes assigned to individual phenotypes. It is not based on an imposed predetermined hierarchy or ontology. Importantly, unlike the human tree, the information contained in the mouse tree is derived from individual independent mouse genetic studies and phenotypic observations and not from large case controlled population based epidemiological studies. Controversial issues such as publication bias or study size which confound human genetic association studies are not as relevant here in the context of studies of experimentally determined individual mouse gene knockouts and related studies. The mouse gene sets used in creating this tree diagram can be found in Table S7[ 47] . Hierarchical clustering of human and mouse gene sets Hierarchical clustering has become a common tool in the analysis of large molecular data sets [48] allowing identification of similar patterns in a scalable fashion from the whole experiment down to a level of fine structure. To provide further evidence of disease relevance and biological content contained in both the human and mouse gene sets hierarchical clustering was performed on both human and mouse. Four hundred and eighty human gene sets were clustered producing 46 major disease clusters. In the mouse, clustering was performed on 2067 mouse phenotype gene sets, using gene sets containing at least 3 genes. This resulted in 165 major subgroups of functional phenotypic specificity. Hierarchical clustering is shown for human [Additional file 1 and Additional file 2] and for mouse [Additional file 3 and Additional file 4]. Like the human and mouse dendograms, this hierarchical clustering showed functional disease grouping at both a coarse group level and at a fine level within major phenotypic groupings. These clusters in both human and mouse falling into closely defined broad functional groups as well as closely related clinical, physiological, and developmental phenotypes demonstrates a general pattern of relevance to disease in their original underlying genetic associations. As in the dendrogram displays, this suggests that the genes nominally positively associated to these disorders, drawn from the medical literature, are not pervasively randomly assigned or due to a widespread pattern of random false positives associations.