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GnRH deficient patients with congenital hypogonadotropic hypogonadism: Novel genetic findings in ANOS1, RNF216, WDR11, FGFR1, CHD7 and POLR3A genes


Neocleous, Vassos et al. (2020), GnRH deficient patients with congenital hypogonadotropic hypogonadism: Novel genetic findings in ANOS1, RNF216, WDR11, FGFR1, CHD7 and POLR3A genes, Dryad, Dataset,


Background: Congenital hypogonadotropic hypogonadism (CHH) is a rare genetic disease caused by Gonadotropin-Releasing Hormone (GnRH) deficiency. So far a limited number of variants in several genes have been associated with the pathogenesis of the disease. In this original research and review manuscript the retrospective analysis of known variants in ANOS1 (KAL1), RNF216, WDR11, FGFR1, CHD7 and POLR3A genes is described, along with novel variants identified in patients with CHH by the present study.

Methods: Seven GnRH deficient unrelated Cypriot patients underwent whole exome sequencing (WES) by Next Generation Sequencing (NGS). The identified novel variants were initially examined by in silico computational algorithms and structural analysis of their predicted pathogenicity at the protein level was confirmed.

Results: In four nonrelated GnRH males, a novel X-linked pathogenic variant in ANOS1 gene, two novel autosomal dominant (AD) probably pathogenic variants in WDR11 and FGFR1 genes and one rare AD probably pathogenic variant in CHD7 gene were identified. A rare autosomal recessive (AR) variant in the SRA1 gene was identified in homozygosity in a female patient, whilst two other male patients were also respectively found to carry novel or previously reported rare pathogenic variants in more than one genes; FGFR1/POLR3A and SRA1/RNF216.

Conclusion: This report embraces the description of novel and previously reported rare pathogenic variants in a series of genes known to be implicated in the biological development of CHH. Notably, patients with CHH can harbor pathogenic rare variants in more than one gene which raises the hypothesis of locus-locus interactions providing evidence for digenic inheritance. The identification of such aberrations by NGS can be very informative for the management and future planning of these patients.


Genetic Analysis

Genomic DNA was isolated from peripheral blood using the Gentra Puregene Kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s instructions. The DNA concentration and purity was measured using the Nanodrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). Prior to library preparation for whole exome sequencing (WES) genomic DNA was quantified using the Qubit dsDNA BR Assay Kit (Invitrogen, Life Technologies, Eugene, OR, USA) on a Qubit® 2.0 Fluorometer (Invitrogen, Life Technologies, Eugene, OR, USA). WES was performed by using the TruSeq Exome Kit (Illumina Inc., San Diego, CA, USA) with paired-end 150 bp reads. NGS was performed using the NextSeq 500/550 High Output Kit v2.5 (150 Cycles) on an NextSeq500 system (Illumina Inc., San Diego, CA, USA). The FastQC quality control tool ( was used to evaluate the quality of the WES procedure. The mean target coverage for the exome was 70.88X. Specifically, 10X coverage was achieved for 99.22% of the nucleotides, 20X coverage for 87.68% of the nucleotides and 30X coverage for 79.35% of the nucleotides, indicating that the WES reaction was of sufficiently high quality for subsequent analysis.

Variant analysis

The fastqc data obtained by WES were processed using an in-house bioinformatics pipeline. Briefly, all variants were inputted into the VarApp Browser and filtered. VarApp is a graphical user interface, which supports GEMINI. Variants in selected genes with biological involvement in the GnRH neuronal system and CHH were further analyzed using the Qualimap v2.2.1 tool to calculate the target coverage. Mean target coverage was greater than 20X for 93.2% of the selected genes  and greater than 30X for 89% of the selected genes . Variants in these genes were additionally filtered using the VarApp Browser for minor allele frequencies of less than 1% in public databases such as 1000 genomes, ExAC browser and Exome Sequencing Project (ESP). Moreover, variants were filtered and selected according to their impact such as frameshift, splice acceptor, splice donor, start lost, stop gained, stop lost, inframe deletion, inframe insertion, missense, protein altering and splice region. In addition, variants were filtered by the VarApp Browser for their pathogenicity by two in silico tools, SIFT and Polyphen2. Population-specific data from an in-house WES library composed of 51 randomly selected samples of Cypriot origin were used to evaluate the potential disease-causing variants. All variants identified were confirmed by Sanger sequencing. When genetic material of relatives was obtainable, familial segregation was performed. For the cases where two potentially pathogenic variants were identified in an individual, we employed the ORVAL platform for predicting pathogenicity due to digenicity. Finally, the variants were categorized for their pathogenicity using the standards and guidelines of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology.