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

Abstract

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.

Keywords: Diagnosis; Exome-negative; Long read; Omics; Rare.

Fig. 1

Technologies and methods to diagnose rare diseases when ES is unrevealing. Many of these technologies complement each other, fill the missing gaps, and should be analyzed in an integrated manner. The selection between metabolomics, proteomics, methyl profiling, and immune assays should be guided by the patient’s clinical presentation and existing candidate genes identified through sequencing. Functional studies can be used to validate strong candidate variants or elucidate the underlying molecular mechanism of the disease after identifying the causative gene. ES: Exome Sequencing, GS: Genome Sequencing, UN: unstimulated. Some of the graphics representing different technologies or methods have been adapted with permission from [–12]

Fig. 2

Challenges in identifying causal variants using exome or genome sequencing and the potential solutions and alternate approaches. These challenges can be at the level of interpretation or detection. GS: genome sequencing, ASE: allele-specific expression, VUS: variant of unknown significance