Semi-Automated System Rapidly Identifies Pathogenic Genetic Variant Causing Rare Diseases
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By LabMedica International staff writers Posted on 27 Jun 2024 |
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Image: The new technology can help solve the unsolvable in rare disease diagnoses (Photo courtesy of 123RF)
Many patients with rare diseases (50-75%) do not receive a diagnosis after genomic sequencing, often due to insufficient information on the variants identified. Reassessing data over time with new genetic insights can improve diagnostic rates. However, reanalyzing genetic data in clinical laboratories is often hampered by time and resource limitations. Now, a novel semi-automated system enables rapid reanalysis of unresolved rare disease cases by regularly comparing patient genomic data with the latest global research findings, aiming to identify elusive disease-causing genetic variants.
Developed by Mayo Clinic researchers in 2022 (Rochester, MN, USA), the system, named RENEW (REanalysis of NEgative Whole-exome/genome data), incorporates an advanced filtering system that scans new genetic data to identify the variants responsible for a patient’s condition. A recent study demonstrated RENEW’s effectiveness, providing likely diagnoses for 63 out of 1,066 previously undiagnosed cases. RENEW can review each of the 5,741 prioritized genomic variants in about 20 seconds on average, with the complete analysis per patient taking between 10 seconds to 1.5 hours. This contrasts sharply with the weeks often required for manual reanalysis, which involves detailed scrutiny of literature and patient records by researchers and clinicians.
"Considering that the majority of patients with rare diseases who undergo genomic sequencing remain without a diagnosis, this is no small accomplishment," said Alejandro Ferrer, Ph.D., a translational omics researcher at the center and lead author of the study recently published in Human Genetics. "Each successful diagnosis facilitated by RENEW signifies a profound breakthrough in providing answers and hope to people navigating the complexities of rare diseases."
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