Abstract
The analysis of disease phenotype data with genetic information indicated that genes associated with clinically similar diseases tend to be functionally related and work together to perform a specific biological function. Therefore, it is of interest to relate disease phenotype data to mirror modular property implied in the association map of disease genes. Hence, we constructed a textbased human disease gene network (HDGN) by using the phenotypic similarity of their associated disease phenotype records in the OMIM database. Analysis shows that the network is highly modular and it is highly correlated with the physiological classification of genetic diseases. Using a graph clustering algorithm, we found 139 gene modules in the network of 1,865 genes and their gene products (proteins) in these gene modules tend to interact with each other via the computation of PPI intensity. Genes in such gene modules are functionally related and may represent the shared genetic basis of their corresponding diseases. These genes, alone or in combination, could be considered as potential therapeutic targets in future clinical therapy.
Highlights
When used together with genetic information, phenotype data can help to explore relationships between genetic diseases and mutation-bearing genes [1]
We referred to the disease classification, described by Gol et al [13], who manually classified disease phenotypes into 22 main disease classes according to the physiological system affected
It is visually indicative that disease genes with their associated disease phenotypes belonging to the same disease class tend to group together forming different modular structures in the network
Summary
When used together with genetic information, phenotype data can help to explore relationships between genetic diseases and mutation-bearing genes [1] Phenotype data such as Online Mendelian Inheretance in Man (OMIM) [2] and PhenomicDB [3] remain intractable to be deal with because the lack of a standardized vocabulary for the phenotype description. Freudenberg et al [4] clustered nearly 1,000 disease phenotypes of known genetic origin from OMIM functional annotation and protein-protein interaction (PPI). Both the investigations revealed a fact that genes associated with similar disease phenotypes are more likely to be functionally related. The functional relationship in these related genes are in agreement with the modular property of most biological networks, indicating the existence of densely-connected subgraphs in the gene functional network
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