Researchers find cipher for decoding disease genes

13 Mar 2007 | News | Update from University of Warwick
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Research lead

Danish and Belgian researchers have found a computer key that maps the genes underlying heritable disorders, such as breast cancer, multiple sclerosis, and Alzheimer’s disease, and shown that genes important for the development of diseases like Alzheimer’s follow the same cellular rules as genes involved in fundamentally different disorders, such as heart disorders, multiple sclerosis, breast cancer, and Type 2 diabetes.

The work has important implications in drug discovery, and in the diagnosis and treatment of disease. The results also highlight the growing contribution that systems biology is making in linking raw genetic data to disease processes.

”Many disorders manifest themselves in fundamentally different ways, but the surprising discovery is that the underlying genes play together according to the same rules,” says Kasper Lage from Technical University of Denmark, project coordinator.

“Our results show that the genes that trigger diseases, regardless of the type of disease in question, are social team players which cooperate according to highly specific rules. These rules have now been mapped, and we have pointed at hundreds of new genes that are likely to be involved in disorders including multiple sclerosis, Parkinson’s disease, heart disorders, and diabetes.”

Heritable disorders will be easier for clinicians to diagnose using the new results. Furthermore, the identification of new genes likely to be involved in disorders will help patients with defects in these genes. For example, a high-risk carrier of a gene that underlies Type 2 diabetes could prevent or delay the manifestations of the disease through following dietary guidance from early in life.

The results – which are publicly available – are the product of a collaboration between the Centre for Biological Sequence Analysis, the Wilhelm Johannsen Centre for Functional Genomics, and the Steno Diabetes Centre in Denmark, and the SymBioSys Centre for Computational Systems Biology at the Katholieke Universiteit Leuven in Belgium.

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