A recessive ataxia diagnosis algorithm for the next generation sequencing era

Area of Science:

• Neurology
• Genetics
• Bioinformatics

Background:

• Autosomal recessive cerebellar ataxias (ARCAs) present diagnostic challenges.
• Accurate molecular diagnosis is crucial for effective management and genetic counseling.

Purpose of the Study:

• To develop and validate a predictive algorithm (RADIAL) for identifying molecular diagnoses of ARCAs.
• To aid in guiding genetic testing and interpreting sequencing data in the context of clinical presentation.

Main Methods:

• Literature review of 67 ARCAs and clinical expertise informed algorithm development.
• Incorporated patient history, clinical, MRI, EMG, and biomarker data.
• Validated using a multinational cohort of 834 genetically confirmed ARCA patients.

Main Results:

• RADIAL achieved >90% sensitivity and specificity for 84% and 91% of genes, respectively, ranking the correct diagnosis in the top 3.
• Mean sensitivity and specificity for the top 3 diagnoses were 92% and 95%.
• The algorithm significantly outperformed a panel of ataxia experts (p=0.001).

Conclusions:

• The RADIAL algorithm demonstrates high sensitivity and specificity in predicting ARCA molecular diagnoses.
• It effectively guides targeted gene sequencing and aids in interpreting next-generation sequencing results.
• RADIAL represents a valuable tool for improving diagnostic workflows in ARCA.