Institute of Information Science, Academia Sinica



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TIGP--A "network" view of human diseases

  • LecturerDr. Ueng-Cheng Yang (Institute of Biomedical Informatics, National Yang Ming University)
    Host: Miss Elsa Pan
  • Time2011-12-29 (Thu.) 09:30 – 11:00
  • LocationAuditorium 101 at new IIS Building

Identification of disease-causing genes is useful not only for developing diagnostic method, but also in understanding the disease mechanism. Methods such as linkage analysis and association studies are commonly used to locate disease-causing genes in the genome. However, the candidate region identified by these methods may still have up to tens or even hundreds of genes. Therefore, it is important to rank these genes to facilite the final identification of the disease genes. A network-based method was developed for this purpose. In 80% of the disease groups from the Mandelian Inheritance in Man (OMIM) database, the disease gene can be found in the top-five gene list. Moreover, this parameter-free method out-performed the other methods in another 10 diseases, which were not yet collected by OMIM.

After discovering the disease genes, the next step is to study the disease mechanism. By using schizophrenia as an example, we have gone further to assess the effect of gene-gene interactions in this complex disease. For example, the interaction between NRG1 and CACNG2 were consistently discovered from different data sets. By using a protein-interaction based method, the detail mechanism of this gene-gene interaction pair was proposed. This mechanism is consistent with the result of a later published clinical trial. The gene-gene interaction concept can also be applied to study the cancer problem. In the case of breast cancer, some gene-gene interaction may lead to synergistic effect on patient’s survival rates. One of these synergistic pairs was found to be involved in the epithelial-mesenchymal transition (EMT), which may regulate metastasis.

Taken together, we have developed methods to examine the gene-gene interactions in a biological network. This strategy can be used to prioritize the disease candidate genes, propose putative disease mechanisms, or discover potential biomarkers.