A concurrent dual analysis of genomic data augments diagnoses: Experiences of 2 clinical sites in the Undiagnosed Diseases Network

Purpose: Next-generation sequencing (NGS) has revolutionized the diagnostic process for rare/ ultrarare conditions. However, diagnosis rates differ between analytical pipelines. In the Na-tional Institutes of Health-Undiagnosed Diseases Network (UDN) study, each individual's NGS data are concurrently analyzed by the UDN sequencing core laboratory and the clinical sites. We examined the outcomes of this practice.Methods: A retrospective review was performed at 2 UDN clinical sites to compare the variants and diagnoses/candidate genes identified with the dual analyses of the NGS data.Results: In total, 95 individuals had 100 diagnoses/candidate genes. There was 59% concor-dance between the UDN sequencing core laboratories and the clinical sites in identifying di-agnoses/candidate genes. The core laboratory provided more diagnoses, whereas the clinical sites prioritized more research variants/candidate genes (P < .001). The clinical sites solely identified 15% of the diagnoses/candidate genes. The differences between the 2 pipelines were more often because of variant prioritization disparities than variant detection.Conclusion: The unique dual analysis of NGS data in the UDN synergistically enhances out-comes. The core laboratory provided a clinical analysis with more diagnoses and the clinical sites prioritized more research variants/candidate genes. Implementing such concurrent dual analyses in other genomic research studies and clinical settings can improve both variant detection and prioritization.(c) 2022 American College of Medical Genetics and Genomics. Published by Elsevier Inc. All rights reserved.

Automated summary

Next-generation sequencing has transformed the diagnostic process for rare/ultrarare conditions. However, the diagnosis rates vary depending on the analytical pipelines. A dual analysis approach combining the expertise of core laboratories and clinical sites can improve both variant detection and prioritization.