The application of next-generation sequencing technology in congenital haemolytic anaemia: a systematic review and meta-analysis

Congenital haemolytic anaemia (CHA) poses diagnostic challenges due to genetic heterogeneity. This systematic review and meta-analysis evaluates the diagnostic efficacy of next-generation sequencing (NGS) in CHA. We systematically searched PubMed and Web of Science until April 2025. Inclusion criteria encompassed studies applying NGS (whole-exome sequencing [WES], whole-genome sequencing [WGS], clinical exome sequencing [CES], or targeted panels) in confirmed/suspected CHA patients. Pooled positive detection rates with 95% confidence intervals (CIs) were calculated using a random-effects model. Subgroup analyses stratified by family history and disease subtypes were performed. Study quality was assessed via modified STARD criteria. Ten studies involving 885 patients were included. The pooled positive detection rate of NGS was 44.3% (95% CI: 32.4-56.3%, p < 0.001). Subgroup analysis revealed significantly higher detection rates in patients with family history (51.0%; 95% CI: 32.8-69.2%) versus sporadic cases (16.9%; 95% CI: 8.4-27.2%, p < 0.001). Disease-specific yields varied: red cell membrane disorders showed the highest rate (45.3%; 95% CI: 35.2-55.7%), followed by enzymatic disorders (26.7%; 95% CI: 18.8-35.3%). Among all positive cases, pathogenic variants in five core genes accounted for 76.82% of detected mutations: SPTB (25.06%), PKLR (17.10%), ANK1 (11.94%), SLC4A1 (11.48%), and SPTA1 (11.24%). SPTB and ANK1 mutations were most frequently identified in red cell membrane disorders, while PKLR variants were exclusive to enzymatic disorders. NGS demonstrates substantial diagnostic utility in CHA, resolving nearly half of cases overall and over 50% of familial presentations. Its efficacy is particularly pronounced in red cell membrane disorders linked to cytoskeletal genes (SPTB, ANK1, SPTA1). These findings support integrating NGS into first-line CHA diagnostics, with prioritization of core gene panels for cost-effective implementation.