Optimized Homologous Sequence Alignment for the Identification of CYP21A2 Variants in 21-Hydroxylase Deficiency Using Next-Generation Sequencing Technology.

Objective: This study aimed to develop a novel homologous sequence analysis technique using high-throughput sequencing data to enhance CYP21A2 mutation detection. The approach leverages next-generation sequencing to overcome existing limitations and improve 21-hydroxylase deficiency diagnostic accuracy. Methods: From April 21, 2022, to February 21, 2023, a total of 100 unrelated participants were enrolled at the Women and Children's Hospital of Ningbo University, selected based on clinical manifestations and genetic testing results. The study used next-generation sequencing combined with a homologous sequence alignment (HSA) algorithm, which calculated the sequencing read ratios from homologous regions to identify pathogenic or likely pathogenic variants in the CYP21A2 gene. All detected variants were further validated using long-range PCR or multiplex ligation-dependent probe amplification. The accuracy of the HSA algorithm was systematically assessed. Results: Among the 100 participants, 84 were identified as carriers of CYP21A2 mutations, while 16 were diagnosed with 21-hydroxylase deficiency. A total of 107 pathogenic mutations were detected using the homologous sequence alignment algorithm, comprising of 99 single nucleotide variants or insertions/deletions, 6 copy number variants, and 8 fusion mutations. Additionally, eight cases of CYP21A2-CYP21A1P gene conversions were identified based on HSA scores and confirmed through long-range PCR or multiplex ligation-dependent probe amplification. The algorithm demonstrated a positive predictive value of 96.26% for identifying mutations in CYP21A2. The most frequently observed mutations included c.955C > T, c.844G > T, c.293-13C > G, c.518T > A, and exon-level deletions. Conclusion: In genetic testing, particularly when addressing misalignment challenges associated with highly homologous genes such as CYP21A2, application of the HSA algorithm enables accurate mutation detection using commonly employed short-read sequencing methods. Through the characterization of homologous sequence features and optimization of the HSA algorithm, accurate mutation detection can be achieved in more homologous gene families (eg, HBA1/HBA2, SMN1/SMN2, GBA/GBAP1).