Overcoming the Pitfalls of Next-Generation Sequencing-Based Molecular Diagnosis of Shwachman-Diamond Syndrome

Shwachman-Diamond syndrome (SDS) is the second most common cause of exocrine pancreatic insufficiency, and 90% of patients carry mutations in the SBDS gene, the most common being the c.183_184delinsCT and c.258 thorn 2T>C variants. However, precise detection of these most contributory variants by conventional short-read next-generation sequencing data analysis is limited because of the SBDS/SBDSP1 highly homologous sequences. In this study, an efficient approach was established to infer the haplotype of SBDS based on the expectation-maximization algorithm. The workflow was retrospectively applied to detect the two most common SBDS variants in a Chinese SDS high-risk cohort, and a systematic comparison of variant detection results was performed between the workflow and conventional next-generation sequencing analysis based on Sanger sequencing validation. Among the Chinese SDS high-risk cohort (n = 47) and their available parents (n = 64), the established workflow improved the diagnostic rate for these two variants by 27.7% (95% CI, 15.6%-42.6%) compared with conventional analysis. For overall variant detection, the established workflow achieved 100% (95% CI, 92.5%-100%) concordance with Sanger sequencing, whereas conventional analysis showed only 65.8% accuracy; these results included 25.2% with missed variant calls, 7.2% with diagnosed but inaccurate variant calls, and 1.8% with false-positive calls. With its favorable result in both SDS patient diagnosis and carrier detection performance, the provided workflow showed its potential in clinical application for SDS molecular diagnosis.