Applying math to biology: Software identifies disease-causing mutations in undiagnosed illnesses

PUBLIC RELEASE DATE:

22-Apr-2014

Contact: Phil Sahm phil.sahm@hsc.utah.edu 801-581-2517 University of Utah Health Sciences

(SALT LAKE CITY)A computational tool developed at the University of Utah (U of U) has successfully identified diseases with unknown gene mutations in three separate cases, U of U researchers and their colleagues report in a new study 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.

Sequencing the 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 U of U, shows that Phevor's unique approach allows it to identify disease-causing genes more precisely than other computational tools.

Mark Yandell, Ph.D, professor of human genetics, led the research. He was joined by co-authors Martin Reese, Ph.D., of Omicia Inc., an Oakland, Calif., genome interpretation software company, Stephen L. Guthery, M.D., professor of pediatrics who saw two of the cases in clinic, a colleague at the MD Anderson Cancer Center in Houston, and other U of U researchers. Marc V. Singleton, a doctoral student in Yandell's lab, is the first author.

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 also 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 works by using 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. By combining those factors, Phevor identifies an undiagnosed disease or the most likely candidate gene mutation for causing a disease. It 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.

Yandell, the lead developer of the software, describes Phevor as the application of mathematics to biology. "Phevor is a way to try to get the most out of a child's genome to identify diseases or find disease-causing gene mutations," Yandell said.

Here is the original post:
Applying math to biology: Software identifies disease-causing mutations in undiagnosed illnesses