PALO ALTO, Calif. -- A novel computational method to detect disease-causing genes accurately and rapidly was announced by Roche scientists in the October 22 issue of Science. This approach, another innovation in computational genetic analysis from Roche scientists, promises to accelerate markedly the discovery of mouse correlates of genetic risk factors for human disease. The new approach enables researchers to identify a single causative genetic factor by correlating a pattern of observable physiological or pathological differences among selected strains of mice with a pattern of genomic variation. Using conventional methods, pin-pointing a gene contributing to disease risk could take five scientists five years. With Roche's latest innovation, which has up to 1,000-fold greater precision than current methods, a single researcher may accomplish the task in a single afternoon. The method takes advantage of the block-like patterns of genomic variation in selected mouse strains, as illustrated on the cover of Science in which the article appears.
"Our hope is that this new computational approach will increase the utility of the vast amount of DNA sequence information available today and help researchers more fully leverage mouse models of human disease to identify genes contributing to disease risk and drug response," said Gary Peltz, M.D., Ph.D., head of Genetics and Genomics at Roche Palo Alto. "It will help researchers understand the relationship between trait differences and variations in the mouse genome, which will move us a long way toward understanding the impact of human genetic differences. As that happens, we should be able to translate genetic data more effectively and efficiently into the development of both novel diagnostic tools and new medicines to treat human diseases."
In this regard, Roche Palo Alto is engaged in research with several leading universities and government institutions to leverage the power of the new computational Page: 1 2 3 Related biology news :1
Contact: Lisa English, PhD
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