EHSI reports favorable completion of due diligence with Stand-By Systems

January 20, 2016

Importantly, by comparing their data with previous clinical studies, they found their tools to be both highly sensitive and highly accurate, correctly predicting the effect of mutations at 98 percent, with a false-positive rate of less than 10 percent. "These numbers are quite critical, because they mean that we can use this approach to interpret information in the clinical setting; these percentages should be good enough for application in clinical labs," Katsanis said.

"A next step is to develop similar tools to let us evaluate various human genetic mutations within the context of their functions," Katsanis said. "Genotype must have a predictive value or it doesn't tell us much. Knowing all of the disease-related variants in a genome is only a starting point, because our work suggests that there is complexity that many do not yet appreciate in disease architecture."

Other authors include Norann Zaghoul, Yangjian Liu, Jantje Gerdes and Carmen Leitch of the McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine in Baltimore; Cecilia Gascue and Jose Badano of the Institut Pasteur de Montevideo, Montevideo, Uruguay; Yana Bromberg of the Department of Biochemistry and Molecular Biophysics, Columbia University Center for Computational Biology and Bioinformatics; Jonathan Binkley and Arend Sidow of the Departments of Genetics and Pathology, Stanford University Medical Center; and Rudolph Leibel of the Division of Molecular Genetic and Naomi Berrie Diabetes Center, Columbia University, New York, NY.

The work was supported by grants from the National Institute of Child Health and Human Development, the National Institute of Diabetes and Digestive and Kidney Diseases, the Macular Vision Research Foundation, a Visual Neuroscience Training Program fellowship, and the Russell Berrie Foundation.

Source: Duke University Medical Center