SMN Profiling in a Turkish Cohort: NGS-Based Tools Uplift Diagnostic Precision and Carrier Detection in Spinal Muscular Atrophy

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.