Grace Science Foundation and Stanford University Create Biobank for Ultra-Rare NGLY1 Deficiency
October 5, 2017
Rare Daily Staff
The Grace Science Foundation and a clinical research team from Stanford University have created an open source biobank of samples and health data from patients with the NGLY1 gene defect and their families in the hopes of accelerating research in to the ultra-rare condition.
The organization collected samples from 20 of the 36 known patients with the condition, as well as family members. Researchers have cataloged the samples, genomic data, and medical records and will provide the open-source data to researchers interested in NGLY1 without the costs associated with patient recruitment, sample collection, and biobanking, Stanford University said.
NGLY1 deficiency is a metabolic disorder caused by a faulty gene that codes for a protein needed to metabolize sugar. People with the condition suffer global development delays, life threatening liver issues, seizures, and a host of other symptoms.
It can take months to years for a researcher to find enough target patients from whom to collect biological specimens and data. For many rare diseases, a researcher may never find enough patients to justify starting the analysis phase of a study.
The Grace Science Foundation and the Stanford team designed a streamlined process to collect biospecimens and health data in a matter of days. The foundation invited NGLY1 families to its annual scientific conference in July in Palo Alto, with the promise of meeting researchers face-to-face and participating in a study that might accelerate treatments and cures.
A team of 18 nurses, phlebotomists, coordinators, and physicians from Stanford collected 325 skin, urine, stool, blood, and DNA samples during the event.
To accelerate analysis and discovery, the NGLY1 samples now can be requested by researchers through a web-based catalog hosted at Stanford. A governance board of Stanford and the Grace Science Foundation representatives reviews requests.
Researchers can search and request age-, sex- and condition-matched specimens for analysis. Ultimately, researchers will also be able to download de-identified clinical and assay data sets to apply new, advanced bioinformatics approaches to looking at this patient population.
October 5, 2017
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