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Review Open access

Long-read sequencing for neurological disorders: opportunities, challenges, and future directions

Aug 2026 · Neurological Sciences · Vol 47 · 0 citations · 76 references
Medicine

TL;DR

Current strengths, limitations, and emerging applications of LRS are outlined to guide clinicians and researchers in understanding how the technology can be applied today and what is needed for broader adoption.

Abstract

Many neurological disorders (NDs) have a genetic basis, yet traditional diagnostic tools such as EEGs, EMGs, and neuroimaging primarily capture downstream manifestations. Although short-read sequencing (SRS) has advanced genetic diagnostics, significant gaps remain. Large repeat expansions, complex structural variants, mitochondrial variants, transcript splicing alterations, and epigenetic changes, all common contributors to NDs, are difficult to resolve with SRS. This review examines the capabilities of long-read sequencing (LRS) technologies in addressing these limitations. We evaluate studies leveraging LRS for genetic diagnosis in NDs and assess current barriers to clinical adoption, including technological, analytical, cost-related, and ethical considerations. By producing read lengths of tens of kilobases or more, LRS enables detection of variant types often inaccessible to SRS. Recent work has demonstrated its power in conditions such as Duchenne muscular dystrophy, fragile X syndrome, spinocerebellar ataxias, and unresolved mitochondrial syndromes. These findings highlight the potential of LRS to substantially increase diagnostic yield in NDs. However, major challenges persist: the need for high-quality DNA, demanding analytic pipelines, limited access outside major research centers, high costs, and ethical concerns including equity and management of incidental findings. LRS offers advantages for identifying complex genomic contributors to NDs and holds promise for improving diagnostic accuracy. Nonetheless, key technical, logistical, and ethical barriers must be addressed before widespread implementation is feasible. This review outlines current strengths, limitations, and emerging applications of LRS to guide clinicians and researchers in understanding how the technology can be applied today and what is needed for broader adoption.

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