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Predicting the relevance of genetic mutations

By Mike Nagle , 26-Jan-2007

Scientists have published a website that could aid the quest for personalised medicine by predicting which genetic mutations make an individual susceptible to disease.

Scientists at the Cincinnati Children's Hospital Medical Centre, in collaboration with the University of Cincinnati, have developed PolyDoms ; an online tool that integrates biological and theoretical data on genes and proteins. The database can be used to give a theoretical probability of which changes in the genome sequence could cause disease and whether such changes need further clinical investigation. Personalised medicine has long been a goal of both the pharmaceutical industry and clinicians. Genetic mutations can cause diseases or prevent one individual responding to medication that works well with others. In order to effectively diagnose and treat each patient, it is crucial to determine which genetic mutations occur regularly and what, exactly, those mutations do. A single nucleotide polymorphism (SNP) is a DNA sequence variation where just one nucleotide in the genome differs between individuals. SNPs make up around 90 per cent of all human genetic variations and can occur anywhere on the genome. However, not all mutations are important. First, the mutation must occur in a genome region that produces proteins - a coding region. Then, the mutation must cause one of the protein's constituent amino acids to be changed. If these two conditions are met, the structure of the resultant protein may be affected. "Having this computational tool available to researchers will help prioritise which genetic variations are most likely to alter the structure and function of a protein, and provides us with critical information related to disease susceptibility, progression and targets for therapeutic interventions," said David Schwartz, director of the National Institute of Environmental Health Sciences (NIEHS), which helped fund the research. There is a vast amount of real life data from Biomedical researchers on the genetic influences of disease. However, it is difficult to determine how the structure of a protein might change if its amino acids are mutated or how this could affect its function. Instead, data of this nature is predicted using computer models. The PolyDoms tool combines the results from biological and theoretical research to give a complete picture of genetic mutation. "PolyDoms is part of a new wave of informatic resources that we and others in the computational biology community are developing to expedite and advance research in personalised, predictive and preventive medicine," said Dr Bruce Aronow, co-director of the Computational Medicine Centre. "At every stage our goals are to improve understanding, to decrease risk of disease and to improve the care and health for each individual," he continued.

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