Short tandem repeat (STR) expansions are a major cause of neurodegenerative disorders; however, their genetic and clinical heterogeneity complicates diagnosis. STR detection remains limited in routine short-read next-generation sequencing (NGS) workflows. We evaluated the diagnostic yield and clinical utility of computational STR genotyping in a large Turkish neurodegenerative disease cohort. ExpansionHunter was applied to NGS data from 3150 patients and 146 controls, targeting 15 disease-associated STR loci. To improve genotyping of poorly captured exonic regions in exome data, the default locus coverage threshold was reduced from 10× to 3×. Candidate expansions were visually inspected using REViewer and validated by conventional molecular methods. Computational analysis detected 28 pathogenic and 160 intermediate expansions. Of these, 23 were confirmed as pathogenic, and eight initially classified as intermediate were reclassified as pathogenic after conventional validation, resulting in 31 pathogenic cases across 28 families: HTT (n = 8), ATXN2 (n = 5), ATXN1 (n = 4), DMPK (n = 3), PABPN1 (n = 3), TBP (n = 2), and single cases in AR, ATN1, and CACNA1A. Lowering the coverage threshold markedly increased genotyping rates at low-coverage loci in exome data, particularly in ATXN2. Genetic findings were largely consistent with clinical pre-diagnosis and the additional diagnostic yield was 0.95%. These findings support integrating STR analysis into routine neurogenetic diagnostics.

Short tandem repeat (STR) expansions are a major cause of neurodegenerative disorders; however, their genetic and clinical heterogeneity complicates diagnosis. STR detection remains limited in routine short-read next-generation sequencing (NGS) workflows. We evaluated the diagnostic yield and clinical utility of computational STR genotyping in a large Turkish neurodegenerative disease cohort. ExpansionHunter was applied to NGS data from 3,150 patients and 146 controls, targeting 15 disease-associated STR loci. To improve genotyping of poorly captured exonic regions in exome data, the default locus coverage threshold was reduced from 10X to 3X. Candidate expansions were visually inspected using REViewer and validated by conventional molecular methods. Computational analysis identified 28 pathogenic and 160 intermediate expansions. Of these, 23 were confirmed as pathogenic, and eight initially classified as intermediate were reclassified as pathogenic after conventional validation, resulting in 31 pathogenic cases across 28 families: HTT (n=8), ATXN2 (n=5), ATXN1 (n=4), DMPK (n=3), PABPN1 (n=3), TBP (n=2), and single cases in AR, ATN1, and CACNA1A. Lowering the coverage threshold markedly increased genotyping rates at low-coverage loci in exome, particularly in ATXN2. Genetic findings were largely consistent with clinical pre-diagnosis and the additional diagnostic yield was 0.95%. These findings support integrating STR analysis into routine neurogenetic diagnostics.

Purpose: Next-generation sequencing (NGS) is routinely used in the diagnostic workup of neurological diseases, enabling systematic screening for SMA with tailored bioinformatic tools, further enhancing diagnostic speed and accuracy. Methods: We leveraged SMNCopyNumberCaller, SMAca, and SMAFinder in our NGS cohort (n = 3493), including 74 MLPA-validated SMA cases (one compound heterozygous) in the exome dataset. Putative SMA cases were validated using PCR-RFLP and MLPA. Results: With default settings of SMA Finder in exome cohort (n = 2437), 16.4% of samples were uncallable including 40 known SMA cases. Lowering read thresholds markedly improved callability and identified 71/73 known SMA cases, two cases remaining uncallable. SMAca correctly detected 73/73 SMA cases. Both tools had a positive predictive value of 100% and identified two missed cases (DM1, MND), subsequently molecularly confirmed. After inclusion of correction value to scale factor, SMAca showed high concordance with MLPA for SMN2 copy number estimation in SMA cases. Carrier frequencies were estimated as 1:36 and 1:47, in genome and exome respectively. Using SMNCopyNumberCaller, we provided detailed SMN profiling in a Turkish genome cohort (n = 1056). Conclusions: NGS-based SMN analysis enables robust detection of SMA and supports systematic cohort screening to identify missed cases.