The results – which are published in the current issue of Nature Biotechnology – show 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.
”Many disorders manifest themselves in fundamentally different ways, but the new surprising discovery is that the underlying genes play together after the same rules. Our results show that the genes that trigger diseases, regardless of the type of disease in question, are social team players who 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, heart disorders, and diabetes”, says Kasper Lage from Technical University of Denmark, who is the project coordinator on this work.
Heritable disorders will be easier to interpret for clinicians 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, if you are a high risk carrier of a gene that underlies a disease such as Type 2 diabetes, physicians could prevent or delay the manifestations of the disease by dietary guidance early in life.
”This is a crucial breakthrough for our understanding of heritable disorders, and a breakthrough for systems biology as a research strategy in the field genetics and disease”, says Søren Brunak leader of Center for Biological Sequence analysis at the Technical University of Denmark. ”We work with genes and proteins, but also with clinical literature describing the characteristics of different disorders. Then we let the computer integrate all of these data, and extract the pattern”, he adds.
The results are the product of a collaboration between the Center for Biological Sequence analysis, the Wilhelm Johannsen Center for Functional Genomics, Steno Diabetes Center in Denmark, and the SymBioSys Center for Computational Systems Biology, Katholieke Universiteit Leuven in Belgium.
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