» Articles » PMID: 37435434

Integrating Genetic Structural Variations and Whole-Genome Sequencing Into Clinical Neurology

Overview
Journal Neurol Genet
Date 2023 Jul 12
PMID 37435434
Authors
Affiliations
Soon will be listed here.
Abstract

Advances in genome sequencing technologies have unlocked new possibilities in identifying disease-associated and causative genetic markers, which may in turn enhance disease diagnosis and improve prognostication and management strategies. With the capability of examining genetic variations ranging from single-nucleotide mutations to large structural variants, whole-genome sequencing (WGS) is an increasingly adopted approach to dissect the complex genetic architecture of neurologic diseases. There is emerging evidence for different structural variants and their roles in major neurologic and neurodevelopmental diseases. This review first describes different structural variants and their implicated roles in major neurologic and neurodevelopmental diseases, and then discusses the clinical relevance of WGS applications in neurology. Notably, WGS-based detection of structural variants has shown promising potential in enhancing diagnostic power of genetic tests in clinical settings. Ongoing WGS-based research in structural variations and quantifying mutational constraints can also yield clinical benefits by improving variant interpretation and disease diagnosis, while supporting biomarker discovery and therapeutic development. As a result, wider integration of WGS technologies into health care will likely increase diagnostic yields in difficult-to-diagnose conditions and define potential therapeutic targets or intervention points for genome-editing strategies.

Citing Articles

Detection of antimicrobial resistance via state-of-the-art technologies versus conventional methods.

Elbehiry A, Marzouk E, Abalkhail A, Abdelsalam M, Mostafa M, Alasiri M Front Microbiol. 2025; 16:1549044.

PMID: 40071214 PMC: 11893576. DOI: 10.3389/fmicb.2025.1549044.


Revolutionizing Personalized Medicine: Synergy with Multi-Omics Data Generation, Main Hurdles, and Future Perspectives.

Molla G, Bitew M Biomedicines. 2025; 12(12.

PMID: 39767657 PMC: 11673561. DOI: 10.3390/biomedicines12122750.


Scrutinizing neurodegenerative diseases: decoding the complex genetic architectures through a multi-omics lens.

Cocos R, Popescu B Hum Genomics. 2024; 18(1):141.

PMID: 39736681 PMC: 11687004. DOI: 10.1186/s40246-024-00704-7.


Large-scale neurophysiology and single-cell profiling in human neuroscience.

Lee A, Chang E, Paredes M, Nowakowski T Nature. 2024; 630(8017):587-595.

PMID: 38898291 DOI: 10.1038/s41586-024-07405-0.


Education and training in neurology: developments and future challenges.

van der Meulen M, Wijnenga M Eur J Neurol. 2024; 31(11):e16332.

PMID: 38773718 PMC: 11464398. DOI: 10.1111/ene.16332.


References
1.
Huang C, Schneider A, Lu Y, Niranjan T, Shen P, Robinson M . Mobile interspersed repeats are major structural variants in the human genome. Cell. 2010; 141(7):1171-82. PMC: 2943426. DOI: 10.1016/j.cell.2010.05.026. View

2.
Logsdon G, Vollger M, Eichler E . Long-read human genome sequencing and its applications. Nat Rev Genet. 2020; 21(10):597-614. PMC: 7877196. DOI: 10.1038/s41576-020-0236-x. View

3.
Cummings B, Karczewski K, Kosmicki J, Seaby E, Watts N, Singer-Berk M . Transcript expression-aware annotation improves rare variant interpretation. Nature. 2020; 581(7809):452-458. PMC: 7334198. DOI: 10.1038/s41586-020-2329-2. View

4.
Zhao X, Collins R, Lee W, Weber A, Jun Y, Zhu Q . Expectations and blind spots for structural variation detection from long-read assemblies and short-read genome sequencing technologies. Am J Hum Genet. 2021; 108(5):919-928. PMC: 8206509. DOI: 10.1016/j.ajhg.2021.03.014. View

5.
Truty R, Paul J, Kennemer M, Lincoln S, Olivares E, Nussbaum R . Prevalence and properties of intragenic copy-number variation in Mendelian disease genes. Genet Med. 2018; 21(1):114-123. PMC: 6752305. DOI: 10.1038/s41436-018-0033-5. View