Toward Clinical Implementation of Next-Generation Sequencing-Based Genetic Testing in Rare Diseases: Where Are We?. Issue 11 (November 2019)

• Record Type: Journal Article
• Authors: Liu, Zhichao; Zhu, Liyuan; Roberts, Ruth; Tong, Weida
• Is Part Of: Trends in genetics. Volume 35:Issue 11(2019)
• Page Start: 852
• Page End: 867
• Publication Date: 2019-11
• Subjects: next-generation sequencing -- rare diseases -- clinical diagnosis -- artificial intelligence; Genetics -- Periodicals; 576.5
• DOI: 10.1016/j.tig.2019.08.006
• Languages: English
• ISSNs: 0168-9525

Abstract

Abstract : Next-generation sequencing (NGS) technologies have changed the landscape of genetic testing in rare diseases. However, the rapid evolution of NGS technologies has outpaced its clinical adoption. Here, we re-evaluate the critical steps in the clinical application of NGS-based genetic testing from an informatics perspective. We suggest a 'fit-for-purpose' triage of current NGS technologies. We also point out potential shortcomings in the clinical management of genetic variants and offer ideas for potential improvement. We specifically emphasize the importance of ensuring the accuracy and reproducibility of NGS-based genetic testing in the context of rare disease diagnosis. We highlight the role of artificial intelligence (AI) in enhancing understanding and prioritization of variance in the clinical setting and propose deep learning frameworks for further investigation. Highlights: NGS-based genetic testing in the diagnosis of rare diseases holds great promise to serve as a first-tier genetic testing tool in the near future. Advancement of NGS technologies provides many options for diagnosing rare disorders associated with different types of genetic variants. Factors must be balanced in a 'fit-for-purpose' implementation. The accuracy and reproducibility of NGS should be evaluated in a clinical setting to deliver reliable genetic testing results in different clinical stages of rare diseases. Artificial intelligence (AI) will play a central role in integrating diverse diagnosis information toward an enhanced diagnosis power for rare diseases.