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Genetic analysis of neurodegenerative diseases
Maurizio Grassano, Alice B. Schindler, Bryan J. Traynor, Sonja W. Scholz
Maurizio Grassano, Alice B. Schindler, Bryan J. Traynor, Sonja W. Scholz
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Review Series

Genetic analysis of neurodegenerative diseases

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Abstract

Recent advances in genomic technologies have greatly enhanced our understanding of neurodegeneration. Techniques like whole-genome sequencing, long-read sequencing, and large-scale population studies have expanded the range of identified genetic risk factors, uncovering new disease mechanisms and biological pathways that could serve as therapeutic targets. However, translating these genetic insights into clinical practice remains difficult because of challenges in interpreting variants and the limited functional validation of new discoveries. This Review highlights the key genomic technologies advancing diagnosis and research in neurodegeneration. We focus on improvements in variant classification, detection of structural variants and repeat expansions, and combining transcriptomic, proteomic, and functional data to better determine variant pathogenicity. The ongoing integration of genomics, molecular neurobiology, and data science offers great potential for more accurate, biologically informed diagnosis and treatment of neurodegenerative disorders.

Authors

Maurizio Grassano, Alice B. Schindler, Bryan J. Traynor, Sonja W. Scholz

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Figure 2

Integrative variant filtering and functional validation strategies.

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Integrative variant filtering and functional validation strategies.
Afte...
After variant detection, successive filtering steps are applied to prioritize candidate variants. Functional validation integrates multiomics and experimental approaches to assess the biological impact of prioritized variants. (A) Transcriptomic validation by RNA sequencing (RNA-seq) can reveal molecular consequences of candidate variants at the transcript level. Abnormal expression of the affected gene may indicate altered transcriptional regulation or mRNA stability. Monoallelic expression suggests allelic imbalance, often due to nonsense-mediated decay or promoter methylation. Aberrant splicing may include exon skipping (canonical exons omitted), intron retention (intronic sequences retained due to splice site disruption), pseudoexon inclusion (cryptic intronic sequences aberrantly incorporated), or shortened or extended exons, resulting from alternative splice donor or acceptor usage. (B) Epigenomic methods assess DNA and chromatin modifications that influence gene expression. Bisulfite sequencing provides base-resolution maps of cytosine methylation. Assay for transposase-accessible chromatin sequencing (ATAC-seq) identifies regions of open chromatin, marking regulatory regions, such as promoters and enhancers. Chromatin immunoprecipitation sequencing (ChIP-seq) maps histone modifications or transcription factor binding. (C) Proteomic validation: Proteomic analyses evaluate the downstream impact of variants on protein abundance and function. Mass spectrometry can be used in qualitative modes to identify protein isoforms or posttranslational modifications and in quantitative modes to measure differential protein levels across samples. Complementary affinity-based platforms, such as Olink or SomaScan, provide targeted, high-throughput quantification using specific molecular binders. (D) In vivo and cellular validation: Functional assessment of variant pathogenicity often employs patient-derived samples, such as fibroblasts or induced pluripotent stem cells, which can be differentiated into disease-relevant cell types (e.g., neurons). CRISPR-based genome editing enables introduction or correction of variants to test causality, and rescue experiments — where reexpression of the wild-type allele restores normal function — provide strong evidence of pathogenicity.

Copyright © 2026 American Society for Clinical Investigation
ISSN: 0021-9738 (print), 1558-8238 (online)

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