Background Central precocious puberty (CPP) due to premature activation of GnRH secretion results in early epiphyseal fusion and to a significant compromise in the achieved final adult height.  CPP is usually idiopathic and is disproportionally observed in girls compared to boys. Currently, only few genetic determinants of children with CPP have been described and the role they exert on the development of the disorder. In this original study rare variants in MKRN3, DLK1, KISS1, KISS1R and MAGEL2 genes are reported in patients with CPP.

Methods Fifty-four index females and 2 index males with CPP underwent whole exome sequencing (WES) by Next Generation Sequencing (NGS). The identified rare variants were initially examined by in silico computational algorithms and confirmed by Sanger sequencing. Additionally, a genetic network for the MKRN3 gene mimicking a holistic regulatory depiction of the crosstalk between MKRN3 and other genes is designed.

Results Three previously described pathogenic MKRN3 variants in the coding region of the gene occurred in 12 index females with CPP. With the p.Gly312Asp pathogenic variant of the MKRN3 gene being the most prevalent and exclusively found among the Cypriot CPP cohort, it is projected to be the result of founder effect phenomenon. In seven additional CPP patients from the same cohort several other likely and rare pathogenic upstream variants in the MKRN3 gene were also observed. In addition to the MKRN3 variants, a total of 16 other rare variants in DLK1, KISS1 and MAGEL2 were also identified in other CPP patients from the same cohort. Interestingly, the frequent variant rs10407968 (p.Gly8Ter) of the KISS1R gene appeared to be less frequent in the cohort of patients with CPP.

Conclusion The results of the present study denote the key role of the imprinted MKRN3 gene in puberty. Additionally, pathogenic variants can also exist in the noncoding region of the MKRN3 gene such as the proximal promoter and 5’-UTR region and which can also be considered as contributing factors to CPP.  Overall, the results of present study have emphasised the necessity of the allied genetic and clinical approach which is necessary for the management and treatment of CPP.

Genetic Analysis

Genomic DNA was extracted from peripheral blood using the Gentra Puregene Kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s instructions. The DNA 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 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) was used to evaluate the quality of the WES procedure. The mean target coverage of the whole exome was 62.13X. Specifically, 10X coverage was reached for 92.34% of the nucleotides, 20X coverage for 86.03% of the nucleotides and 30X coverage for 76.96% of the nucleotides, indicating that the WES reaction was of sufficiently high quality for subsequent analysis.

Variant Analysis

The fastq 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 (18). Variants in selected genes involved in pubertal onset and were mutations have been reported for precocious puberty were further analyzed using the Qualimap v2.2.1 tool to calculate the target coverage. Mean target coverage was 60X of the selected genes (Supplementary Table 1). 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 43 randomly selected samples of Cypriot origin were used to evaluate the potential disease-causing variants. All variants identified were confirmed by Sanger sequencing. 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.