Decoding the Alphabet Soup: A Practical Guide to Genetic Testing in Hyperkinetic Movement Disorders

Abstract

Background: The diagnosis of genetic hyperkinetic movement disorders has become increasingly more complex as new genes are discovered and technologies offer new diagnostic possibilities. As a result, the choice of appropriate gene testing and the interpretation of the results can become difficult to navigate for movement disorder experts and clinicians. In parallel, research is becoming crucial to pair with clinical assessments in order to explore advanced sequencing technologies and allow new genes discovery.

Methods: Systematic review of genetic forms of hyperkinetic movement disorders and of the most relevant genetic terminology was performed.

Results: Comprehensive descriptions of genetic lexicon, testing selection, and complex genetic findings related to hyperkinetic movement disorders are reported.

Discussion: Here we discuss the terminology of genetic diagnosis that is now part of the clinical practice, the difficulties related to the interpretation of complex genetic results, and provide guidance and tips for gene testing selection in order not to miss important diagnosis of genetic hyperkinetic movement disorders.

Highlights: To review the most relevant lexicon related to genetic diagnosis, approach to gene testing, testing selection, and complex genetic findings in genetic hyperkinetic movement disorders.

Keywords: genetic tests; genotype-phenotype; hyperkinetic movement disorders; next generation sequencing; repeat expansion; variant of uncertain significance.

Figure 1

Schematic representation of genetic changes and appropriate genetic tests. The four sections of the figure depict the most important genetic variants that can be found in hyperkinetic movement disorders (upper part of the figure) and the appropriate tests to assess each genetic change (lower part of the figure). MLPA: Multiplex ligation-dependent probe amplification; NGS: next generation sequencing; WES: whole exome sequencing; WGS: whole genome sequencing. Figure created with BioRender.

Figure 2

ACMG variant classification framework: categories of evidence contributing to pathogenic vs. benign interpretation. Each type of evidence shown in the figure may support either pathogenicity (green) or benignity (red), depending on the data available. Population data support pathogenicity when the variant is absent or extremely rare in databases such as gnomAD or ExAC (e.g., PM2), and benignity when the variant is too frequent to be compatible with disease (e.g., BA1, BS1). Segregation analysis supports pathogenicity when the variant co-segregates with disease within affected family members (PP1), and benignity when found in unaffected relatives (BS4). In silico prediction tools such as SIFT, PolyPhen-2, and CADD support pathogenicity when multiple tools predict a damaging effect (PP3), and benignity when predictions are consistently benign (BP4). Functional studies provide support for pathogenicity when validated assays show loss of function or abnormal splicing (PS3), and support for benignity when normal function is demonstrated (BS3). Allelic data support pathogenicity when other known pathogenic variants affect the same residue or domain (PM5), and benignity when the variant occurs in -trans (on the other allele) with a pathogenic variant but the individual is unaffected (BP2). Phenotype consistency supports pathogenicity when the patient’s phenotype strongly matches the gene-disease relationship (PP4), and benignity when inconsistent or unrelated (BP5). De novo data contributes to pathogenicity when confirmed by parental testing in a consistent phenotype (PS2), while isolated or unconfirmed de novo findings in unrelated phenotypes may provide weak or no support. Other database or case-level data support pathogenicity when the variant is consistently reported as pathogenic in databases like ClinVar or HGMD (PP5), and benignity when consistently reported as benign without conflicting evidence (BP6). Adapted from Richards et al., Genet Med 2015, and updates by ClinGen SVI Working Group, Genet Med 2020.