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

Authors

Yunseo Han, Danbi Song, Minsoo Noh, Mikyung Kang, Young Sil Eom, Hunsang Lee, Sihoon Lee

Published in

Frontiers in endocrinology. Volume 17. Pages 1840651. Epub Jul 01, 2026.

Abstract

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).
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.
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 ~3 h, reducing manual data review by >99.6%.
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.

PMID:
42460321
Bibliographic data and abstract were imported from PubMed on 16 Jul 2026.

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