Comprehensive Identification of Pathogenic Gene Variants in Patients With Neuroendocrine Disorders

Purpose: 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. Methods: 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. Results: 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). Conclusion: 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.

Automated summary

This study in Argentina identified mutations in known and candidate genes for congenital hypopituitarism using genetic screening. The findings show that congenital hypopituitarism is a genetically heterogeneous disease with high phenotypic variation and incomplete penetrance, highlighting the importance of further gene discovery in the field. Identification of population-specific pathogenic variants can enhance the predictive value of genetic data for eventual clinical outcomes.