Spinocerebellar ataxia in monozygotic twins.

CONTEXT: Although phenotypic heterogeneity in autosomal dominant spinocerebellar ataxia (SCA) has been explained in part by genotypic heterogeneity, clinical observations suggest the influence of additional factors. OBJECTIVES: To demonstrate, quantitate, and localize physiologic abnormalities attributable to nongenetic factors in the development of hereditary SCA. DESIGN: Quantitative assessments of ocular motor function and postural control in 2 sets of identical twins, one with SCA type 2 and the other with episodic ataxia type 2. SETTING: University laboratory. MAIN OUTCOME MEASURES: Saccadic velocity and amplitude, pursuit gain, and dynamic posturography. RESULTS: We found significant differences in saccade velocity, saccade metrics, and postural stability between each monozygotic twin. The differences point to differential involvement between twins of discrete regions in the cerebellum and brainstem. CONCLUSIONS: These results demonstrate the presence of quantitative differences in the severity, rate of progression, and regional central nervous system involvement in monozygotic twins with SCA that must be owing to the existence of nongermline or external factors.

Spinocerebellar ataxia type 26 maps to chromosome 19p13.3 adjacent to SCA6.

The dominantly inherited spinocerebellar ataxias (SCA) are a clinically and genetically heterogeneous group of neurodegenerative disorders characterized by progressive gait ataxia, upper limb incoordination, and dysarthria. We studied a six-generation kindred of Norwegian ancestry with pure cerebellar ataxia inherited in an autosomal dominant pattern. All affected family members had a slowly progressive cerebellar ataxia, with an age of onset range from 26 to 60 years. Brain magnetic resonance imaging study of 11 affected patients showed that atrophy was confined to the cerebellum. After excluding all the known SCAs using linkage analysis or direct mutation screen, we conducted a genomewide genetic linkage scan. With the aid of a novel linkage analysis strategy, we found linkage between the disease locus and marker D19S591 and D19S1034. Subsequent genetic and clinical analysis identified a critical region of 15.55cM interval on chromosome 19p13.3, flanked by markers D19S886 and D19S894, and have established a new genetic locus designated SCA26. The SCA26 locus is adjacent to the genes for Cayman ataxia and SCA6. The region consists of 3.3 million base pairs (Mb) of DNA sequences with approximately 100 known and predicted genes. Identification of the responsible gene for SCA26 ataxia will provide further insight into mechanisms of neurodegeneration.

Clinical feature profile of spinocerebellar ataxia type 1-8 predicts genetically defined subtypes.

An increasing number of genetically defined types of spinocerebellar ataxia (SCA) have been reported in the past decade. Phenotype--genotype correlation studies have suggested a broad overlap between SCA types. The aim of the present study was to identify patterns of clinical features that were likely to distinguish between SCA types and to test the specificity and sensitivity of these signs and symptoms using a Bayesian classifier. In total, 127 patients from 50 families with SCA types 1 to 8 were examined using a worksheet with a panel of 33 symptoms and signs. By computing the probabilities of each trait for each SCA type, we rated the predictive value of each feature for each form of ataxia and then combined the probabilities for the entire panel of traits to construct a Bayesian classifier. Results of this analysis were summarized in a simpler, more operator-based algorithm. Patients with SCA5, SCA6, and SCA8 demonstrated a predominant cerebellar syndrome, whereas patients with SCA1, SCA2, SCA3, SCA4, and SCA7 frequently had clinical features indicating an extracerebellar involvement. The Bayesian classifier predicted the SCA type in 78% of patients with sensitivities between 60 and 100% and specificities between 94 and 98.2%. The highest sensitivity to correctly predict the true SCA type was found for SCA5, SCA7, and SCA8. Sensitivities and specificities found in the present study validate the use of algorithms to help to prioritize specific SCA gene testing, which will help to reduce costs for gene testing.