Establishment of the endocrine variant extractor and its clinical application in identifying a novel GATA3 mutation in HDR syndrome

초록

Background: Genetic diagnosis of endocrine disorders is often hampered by the complexity of analyzing Whole Exome Sequencing (WES) data. We developed the endocrine variant extractor (EVE), a streamlined, clinician-friendly bioinformatics pipeline designed for multi-tier genetic screening with a core panel for parathyroid disorders (26 genes) and an expanded endocrine panel for broader metabolic assessment (413 genes, fully encompassing the parathyroid panel). Methods: Encapsulated within a Docker container and automated via a custom Python wrapper, EVE integrates core bioinformatics engines, including BWA-MEM, GATK, and SnpEff. The pipeline employs a tiered reporting strategy, filtering and annotating variants across both panels using pathogenicity scores (SIFT, PolyPhen-2) and clinical databases (ClinVar, gnomAD). This architecture ensures cross-platform compatibility without complex manual configuration. Results: To validate the pipeline, EVE was applied to clinical datasets. EVE successfully filtered >300,000 raw variants down to a handful of actionable candidates. Using this pipeline, we precisely identified the first Korean case of a de novo GATA3 frameshift variant (p.Ala173fs) in an HDR syndrome patient, which was not previously reported in the ClinVar database. Analysis took similar to 3 h, reducing manual data review by >99.6%. Conclusion: EVE provides a streamlined, high-efficiency workflow that automates the filtering of thousands of raw WES variants into a curated list of clinically relevant variants. This robust framework enables the creation of a comprehensive "endocrine variant atlas," empowering clinicians to integrate high-throughput genetic profiling into routine diagnostics and accelerate the discovery of novel disease-causing variants. The complete source code for EVE is freely available at https://github.com/hanyunseo01/EVE.

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