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University of Utah’s New Phevor Software Identifies Disease-Causing Gene Mutations in Three Undiagnosed Children

April 22, 2014

Study Results Are Published in The American Journal of Human Genetics

SALT LAKE CITY, April 22, 2014 /PRNewswire-USNewswire/ — A new computational tool developed at the University of Utah has successfully identified diseases with unknown gene mutations in three separate cases, University of Utah researchers and their colleagues report in The American Journal of Human Genetics. The software, Phevor (Phenotype Driven Variant Ontological Re-ranking tool), identifies undiagnosed illnesses and unknown gene mutations by analyzing the exomes, or areas of DNA where proteins that code for genes are made, in individual patients and small families.

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Sequencing genomes of individuals or small families often produces false predictions of mutations that cause diseases. But the study, conducted through the new USTAR Center for Genetic Discovery at the University of Utah, shows that Phevor’s unique approach allows it to identify disease-causing genes more precisely than other computational tools.

“Phevor as the application of mathematics to biology to get the most out of a child’s genome to identify diseases or find disease-causing gene mutations,” says Mark Yandell, Ph.D., University of Utah professor of human genetics who led the development of Phevor and is senior author on the study.

Phevor represents a major advance in personalized health care, according to Lynn B. Jorde, Ph.D., U of U professor and chair of human genetics and a co-author on the study. As the cost of genome sequencing continues to drop, Jorde expects it to become part of standardized health care within a few years, making diagnostic tools such as Phevor more readily available to clinicians.

“With Phevor, just having the DNA sequence will enable clinicians to identify rare and undiagnosed diseases and disease-causing mutations,” Jorde said. “In some cases, they’ll be able to make the diagnosis in their own offices.”

Phevor uses algorithms that combine the probabilities of gene mutations being involved in a disease with databases of phenotypes, or the physical manifestation of a disease, and information on gene functions. The software is particularly useful when clinicians want to identify an illness or gene mutation involving a single patient or the patient and two or three other family members, which is the most common clinical situation for undiagnosed diseases.

Co-authors on the study include Martin Reese, Ph.D., of Omicia Inc., an Oakland, Calif., genome interpretation software company, Stephen L. Guthery, M.D., University of Utah professor of pediatrics, and Chad D. Huff, Ph.D., at the University of Texas MD Anderson Cancer Center in Houston.

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SOURCE University of Utah Health Sciences


Source: PR Newswire



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