A guide for the diagnosis of rare and undiagnosed disease: beyond the exome

Genome Medicine

Joshua W. Knowles¹  Shruti Marwaha²  Euan A. Ashley³ 

[1] Department of Medicine, Division of Cardiovascular Medicine, School of Medicine, Stanford University, Stanford, CA, USA;Department of Medicine, Diabetes Research Center, Cardiovascular Institute and Prevention Research Center, Stanford, CA, USA;Department of Medicine, Division of Cardiovascular Medicine, School of Medicine, Stanford University, Stanford, CA, USA;Stanford Center for Undiagnosed Diseases, Stanford University, Stanford, CA, USA;Department of Medicine, Division of Cardiovascular Medicine, School of Medicine, Stanford University, Stanford, CA, USA;Stanford Center for Undiagnosed Diseases, Stanford University, Stanford, CA, USA;Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA;

关键词: Diagnosis;    Rare;    Omics;    Exome-negative;    Long read;   

DOI  :  10.1186/s13073-022-01026-w

来源: Springer

【 摘 要 】

Rare diseases affect 30 million people in the USA and more than 300–400 million worldwide, often causing chronic illness, disability, and premature death. Traditional diagnostic techniques rely heavily on heuristic approaches, coupling clinical experience from prior rare disease presentations with the medical literature. A large number of rare disease patients remain undiagnosed for years and many even die without an accurate diagnosis. In recent years, gene panels, microarrays, and exome sequencing have helped to identify the molecular cause of such rare and undiagnosed diseases. These technologies have allowed diagnoses for a sizable proportion (25–35%) of undiagnosed patients, often with actionable findings. However, a large proportion of these patients remain undiagnosed. In this review, we focus on technologies that can be adopted if exome sequencing is unrevealing. We discuss the benefits of sequencing the whole genome and the additional benefit that may be offered by long-read technology, pan-genome reference, transcriptomics, metabolomics, proteomics, and methyl profiling. We highlight computational methods to help identify regionally distant patients with similar phenotypes or similar genetic mutations. Finally, we describe approaches to automate and accelerate genomic analysis. The strategies discussed here are intended to serve as a guide for clinicians and researchers in the next steps when encountering patients with non-diagnostic exomes.

【 授权许可】

CC BY