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  • Perspective
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Next-generation sequencing and bioinformatics in rare movement disorders

Nature Reviews Neurology volume 20, pages 114–126 (2024)Cite this article

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Abstract

The ability to sequence entire exomes and genomes has revolutionized molecular testing in rare movement disorders, and genomic sequencing is becoming an integral part of routine diagnostic workflows for these heterogeneous conditions. However, interpretation of the extensive genomic variant information that is being generated presents substantial challenges. In this Perspective, we outline multidimensional strategies for genetic diagnosis in patients with rare movement disorders. We examine bioinformatics tools and computational metrics that have been developed to facilitate accurate prioritization of disease-causing variants. Additionally, we highlight community-driven data-sharing and case-matchmaking platforms, which are designed to foster the discovery of new genotype–phenotype relationships. Finally, we consider how multiomic data integration might optimize diagnostic success by combining genomic, epigenetic, transcriptomic and/or proteomic profiling to enable a more holistic evaluation of variant effects. Together, the approaches that we discuss offer pathways to the improved understanding of the genetic basis of rare movement disorders.

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Fig. 1: Next-generation sequencing data production and analysis workflow.
Fig. 2: Mutational constraint metrics to aid variant interpretation.
Fig. 3: Case matchmaking and disease gene discovery via the GeneMatcher platform.
Fig. 4: A suggested multiomic-based diagnostic strategy.

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Acknowledgements

M.Z. and J.W. receive research support from the German Research Foundation (DFG 458949627; ZE 1213/2-1; WI 1820/14-1). M.Z. acknowledges grant support from the European Joint Programme on Rare Diseases (European Joint Programme on Rare Diseases Joint Transnational Call 2022) and the German Federal Ministry of Education and Research (BMBF, Bonn, Germany), awarded to the project PreDYT (PREdictive biomarkers in DYsTonia, 01GM2302), and from the Federal Ministry of Education and Research (BMBF) and the Free State of Bavaria under the Excellence Strategy of the Federal Government and the Länder as well as by the Technical University of Munich — Institute for Advanced Study. M.Z. is a member of the Medical and Scientific Advisory Council of the Dystonia Medical Research Foundation and a member of the Governance Council of the International Cerebral Palsy Genomics Consortium. MZ’s research is supported by a “Schlüsselprojekt” grant from the Else Kröner-Fresenius-Stiftung (2022_EKSE.185).

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Authors and Affiliations

  1. Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany

    Michael Zech & Juliane Winkelmann

  2. Institute of Neurogenomics, Helmholtz Zentrum München, Munich, Germany

    Michael Zech & Juliane Winkelmann

  3. Institute for Advanced Study, Technical University of Munich, Garching, Germany

    Michael Zech

  4. Munich Cluster for Systems Neurology, SyNergy, Munich, Germany

    Juliane Winkelmann

  5. DZPG, Deutsches Zentrum für Psychische Gesundheit, Munich, Germany

    Juliane Winkelmann

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  1. Michael Zech
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Both authors designed and supervised the work and wrote the article.

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Correspondence to Juliane Winkelmann.

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Related links

ClinVar: https://www.ncbi.nlm.nih.gov/clinvar/

CNVnator: https://github.com/abyzovlab/CNVnator

Database of Genomic Variants: http://dgv.tcag.ca/dgv/app/home

dbVar: https://www.ncbi.nlm.nih.gov/dbvar/

DECIPHER: https://www.deciphergenomics.org/

DELLY: https://github.com/dellytools/delly

European Genome–Phenome Archive: https://web2.ega-archive.org/

European Joint Programme on Rare Diseases: https://www.ejprarediseases.org/

ExomeDepth: https://cran.r-project.org/package=ExomeDepth

ExpansionHunter Denovo: https://github.com/Illumina/ExpansionHunterDenovo

GeneMatcher: https://genematcher.org/

Genome Aggregation Database (gnomAD): https://gnomad.broadinstitute.org/

Genome Analysis Toolkit: https://gatk.broadinstitute.org/hc/en-us

German Human Genome–Phenome Archive: https://www.ghga.de/

Human Gene Mutation Database: https://www.hgmd.cf.ac.uk/ac/index.php

InterVar: https://wintervar.wglab.org/

Manta: https://github.com/Illumina/manta

Matchmaker Exchange (MME): https://www.matchmakerexchange.org/

MetaDome: https://stuart.radboudumc.nl/metadome/

MITOMAP: https://www.mitomap.org/MITOMAP

Movement Disorder Society Genetic mutation database: https://www.mdsgene.org/

NIH Undiagnosed Diseases Network: https://commonfund.nih.gov/Diseases

Online Mendelian Inheritance in Man: https://www.omim.org/

PanelApp: https://panelapp.genomicsengland.co.uk/

Solve-RD: https://solve-rd.eu/

Glossary

Coverage-based callers

Copy number variant detection tools that determine the presence of a deletion or duplication by comparing the read coverage in the affected genomic interval with the rest of the sequenced exome or genome. Higher sequencing depth is necessary for reliable analysis.

Digenic inheritance

A mechanism whereby the expression of a disease phenotype is determined by the presence of genetic pathologies in two different loci, often associated with epistatic interactions between these loci (encoded proteins might act in the same pathway).

Generative artificial intelligence

Algorithms that can be used to produce new content, including synthetic data.

Integrated paired-end and split-read analysis strategies

Paired-end mapping approaches can define copy number variants on the basis of alterations in the insert size of paired-end reads, whereas split-read approaches are helpful for predicting copy number changes by assessing unaligned discordant reads that were split and mapped separately from the reference genome.

Mapping certainty

A measure of the accuracy of alignment of sequencing reads to the correct location in the genome. Can be confounded by DNA characteristics such as repetitive regions.

Massive parallelization

A high-throughput approach used in next-generation sequencing studies, which allows analysis of millions of short reads (usually containing 100–150 bp) in an automated miniaturized fashion. This approach differs from traditional capillary Sanger analysis in terms of time-effective mass production of sequencing outputs.

Mendelian conditions

Clinical diseases that are caused by high-effect rare variants in single genes, in contrast to polygenic or multifactorial diseases, which are associated with many common variants with low effect sizes at various genomic loci and are influenced by other non-genetic factors.

Missense constraint

A measure of genetic intolerance to amino acid substitutions, which can aid prioritization of gene candidates involved in missense mutation-associated diseases.

Mobile element

Genomic sequences that can move between chromosomes, for example, through cut-and-paste mechanisms in DNA transposons. These elements have a role in genome evolution, and their integration into disease-associated genes can disrupt the open reading frame and cause clinical phenotypes.

Penetrance

A measure of the proportion of carriers of a specific monogenic disease predisposition who present with clinical features of the associated condition.

Phenotypic pleiotropy

A phenomenon whereby variants in a disease-related gene are associated with multiple (similar or divergent) phenotypic abnormalities.

Simplex cases

Individuals with a disease phenotype who have no relatives affected by the same condition.

Spike-in panel

A protocol that dynamically incorporates specific DNA segments into the sequencing analysis; for example, complementary interrogation of all base pairs of the mitochondrial genome in addition to the nuclear coding sequences in the form of a mitochondrial spike-in panel in diagnostic exome studies.

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Zech, M., Winkelmann, J. Next-generation sequencing and bioinformatics in rare movement disorders. Nat Rev Neurol 20, 114–126 (2024). https://doi.org/10.1038/s41582-023-00909-9

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