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Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
Published on: August 15, 2019
Updated compendium of genes and variants associated with congenital hypogonadotropic hypogonadism: systematic review,
Franco G Brunello1,2,3, Sebastián Castro1, Jonathan Zaiat2,3
1Centro de Investigaciones Endocrinológicas "Dr. César Bergadá" (CEDIE), CONICET - FEI - División de Endocrinología, Hospital de Niños Ricardo Gutiérrez, Gallo 1330, C1425EFD Buenos Aires, Argentina.
Abstract:
To advance the understanding of Congenital Hypogonadotropic Hypogonadism (CHH), we aimed to refine the catalog of causal genes and variants. We systematically collected variants reported in the literature and created CHH_vd, a curated database. In parallel, we developed CHH_vip, a custom-built computational pipeline for variant annotation and classification, which integrates data from CHH_vd and external sources. Gene network and term enrichment analyses were applied to generate disease-specific gene panels. Our systematic review retrieved 352 scientific studies, documenting 1937 patients carrying a total of 2603 variants, of which 1518 were unique, distributed across 143 genes. All variants were incorporated into CHH_vd (publicly available) and reclassified using CHH_vip according to ACMG/AMP guidelines and ClinGen SVI working group recommendations. Changes in classifications of clinical relevance [from/to Pathogenic, Likely Pathogenic, or High_VUS (Variant of Uncertain Significance with High Probability of Pathogenicity)] were identified for 238 variants when comparing with InterVar, a bioinformatics tool for clinical interpretation of genetic variants. The genes GNRHR, ANOS1, PLXNA1, and SEMA7A were particularly implicated in pathogenicity reassignment, having a comparatively high number of variants downgraded (to more benign). Through CHH_vd, we provide an updated view of the genes impacted by genetic variation in CHH, highlighting marked genetic heterogeneity. The implementation of curated gene panels, combined with CHH_vip, a reproducible platform for compiling and interpreting variation data, may optimize the filtering and classification processes, thereby reducing diagnostic turnaround time.
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