Genetic Diversity and Expanded Phenotypes in Dystonia: Insights From Large-Scale Exome Sequencing

Abstract

Objective: Dystonia is one of the most prevalent movement disorders, characterized by significant clinical and etiological heterogeneity. Despite considerable heritability (~25%), the etiology in most patients remains elusive. Moreover, understanding correlations between clinical manifestations and genetic variants has become increasingly complex.

Methods: Exome sequencing was conducted on 1924 genetically unsolved, mainly late-onset isolated dystonia patients, recruited primarily from two dystonia registries (DysTract and the Dystonia Coalition). Rare variants in genes previously linked to dystonia (n = 406) were examined, confirmed via Sanger sequencing, and analyzed for segregation when possible.

Results: We identified 137 distinct likely pathogenic/pathogenic variants (according to ACMG criteria) across 51 genes in 163/1924 patients, including 153/1895 index patients (diagnostic yield 8.1%). The strongest predictors of a genetic diagnosis were generalized dystonia (28.6% yield) and age at onset (20.4% yield in patients with onset < 30 years). Notably, 56.2% of these variants were novel, with recurrent variants in EIF2AK2, VPS16, KCNMA1, and SLC2A1. Additionally, 321 index patients (16.9%) harbored variants of uncertain significance in 102 genes. The most frequently implicated genes included VPS16, THAP1, GCH1, SGCE, GNAL, and KMT2B. Presumably pathogenic variants in less well-established dystonia genes were also found, including KCNMA1, KIF1A, and ZMYND11. At least six variants (in ADCY5, GNB1, IR2BPL, KCNN2, KMT2B, and VPS16) occurred de novo, supporting pathogenicity.

Interpretation: This study provides valuable insights into the genetic landscape of dystonia, underscores the utility of exome sequencing for diagnosis, substantiates several candidate genes, and expands the phenotypic spectrum of some genes to include prominent, sometimes isolated dystonia.

Keywords: dystonia; exome sequencing; genetic heterogeneity; pathogenic variants.

FIGURE 1

Overview of exome sequencing results. (A) Proportion of index patients for whom a diagnostic variant was identified, who carry a variant of uncertain significance (VUS), and who remain unsolved after searching for pathogenic variants in dystonia‐linked genes. (B) Proportion of diagnostic variants that were previously reported (known variant), not previously reported and detected in a single patient or pedigree (novel variant single), and not previously reported and found recurrently in at least two unrelated dystonia patients (novel variant recurrent). (C) Distribution of diagnostic variants by variant type.

FIGURE 2

Genetic landscape in our dystonia sample (n = 1895 index patients) identified by exome sequencing. The number of individuals harboring a presumably pathogenic variant in genes previously linked to dystonia is shown, representing 51 distinct genetic forms and a total diagnostic yield of 8.1%. Truncating variants include stop‐gain and frameshift variants.

FIGURE 3

Variants of uncertain significance in our dystonia sample (n = 1895 index patients) identified by exome sequencing. The number of individuals harboring variants of uncertain significance in genes previously linked to dystonia is shown. This represents 329 distinct variants in 102 genes found in 321 index patients (16.9%). Truncating variants include stop‐gain and frameshift variants.

FIGURE 4

Diagnostic yield across patient groups and predictive performance of clinical factors for genetic diagnosis. (A) Bar plot showing the diagnostic yield (%) across different patient groups, considering index patients only. The segmental dystonia group includes patients with multifocal dystonia. (B) ROC curves illustrating the performance of individual predictors and a combined model for genetic diagnosis. The predictors include age at onset, generalized dystonia, additional features, positive family history, and a combined model incorporating all four predictors. Each curve shows the balance between sensitivity (true positive rate) and 1‐specificity (false positive rate), with the Area Under the Curve (AUC) indicating predictive performance.