Sebastian Alexis Vishnopolska, Maria Florencia Mercogliano, Maria Andrea Camilletti, Amanda Helen Mortensen, Debora Braslavsky, Ana Keselman, Ignacio Bergadá, Federico Olivieri, Lucas Miranda, Roxana Marino, Pablo Ramírez, Natalia Pérez Garrido, Helen Patiño Mejia, Marta Ciaccio, Maria Isabel Di Palma, Alicia Belgorosky, Marcelo Adrian Martí, Jacob Otto Kitzman, Sally Ann Camper, Maria Ines Pérez-Millán
The Journal of Clinical Endocrinology & Metabolism, Volume 106, Issue 7, July 2021, Pages 1956–1976
https://doi.org/10.1210/clinem/dgab177
Congenital hypopituitarism (CH) can present in isolation or with other birth defects. Mutations in multiple genes can cause CH, and the use of a genetic screening panel could establish the prevalence of mutations in known and candidate genes for this disorder. It could also increase the proportion of patients that receive a genetic diagnosis.
We conducted target panel genetic screening using single-molecule molecular inversion probes sequencing to assess the frequency of mutations in known hypopituitarism genes and new candidates in Argentina. We captured genomic deoxyribonucleic acid from 170 pediatric patients with CH, either alone or with other abnormalities. We performed promoter activation assays to test the functional effects of patient variants in LHX3 and LHX4.
We found variants classified as pathogenic, likely pathogenic, or with uncertain significance in 15.3% of cases. These variants were identified in known CH causative genes (LHX3, LHX4, GLI2, OTX2, HESX1), in less frequently reported genes (FOXA2, BMP4, FGFR1, PROKR2, PNPLA6) and in new candidate genes (BMP2, HMGA2, HNF1A, NKX2-1).
In this work, we report the prevalence of mutations in known CH genes in Argentina and provide evidence for new candidate genes. We show that CH is a genetically heterogeneous disease with high phenotypic variation and incomplete penetrance, and our results support the need for further gene discovery for CH. Identifying population-specific pathogenic variants will improve the capacity of genetic data to predict eventual clinical outcomes.
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