Year: 2022
Type: review
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.
Source: Genome Medicine
Language: English
Topic: Genomics and Rare Diseases
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $4,480
Awards: G154717235, G2074245903, Center for Undiagnosed Diseases at Stanford, Mechanisms of NAT2 Regulation of Insulin Resistance and Mitochondrial Dysfunction, Characterization of novel insulin resistance genes by gene editing, high-throughput phenotyping and in vivo studies, G9000126707, G9000198530, Stanford Diabetes Research Center
OpenAlex ID: W4214613209