Diagnosis of childhood and adolescent growth hormone deficiency using transcriptomic data.

Background: Gene expression (GE) data have shown promise as a novel tool to aid in the diagnosis of childhood growth hormone deficiency (GHD) when comparing GHD children to normal children. The aim of this study was to assess the utility of GE data in the diagnosis of GHD in childhood and adolescence using non-GHD short stature children as a control group. Methods: GE data was obtained from patients undergoing growth hormone stimulation testing. Data were taken for the 271 genes whose expression was utilized in our previous study. The synthetic minority oversampling technique was used to balance the dataset and a random forest algorithm applied to predict GHD status. Results: 24 patients were recruited to the study and eight subsequently diagnosed with GHD. There were no significant differences in gender, age, auxology (height SDS, weight SDS, BMI SDS) or biochemistry (IGF-I SDS, IGFBP-3 SDS) between the GHD and non-GHD subjects. A random forest algorithm gave an AUC of 0.97 (95% CI 0.93 - 1.0) for the diagnosis of GHD. Conclusion: This study demonstrates highly accurate diagnosis of childhood GHD using a combination of GE data and random forest analysis.

Approach to the Patient With Short Stature: Genetic Testing.

The first step in the evaluation of the short child is to decide whether growth parameters in the context of the history are abnormal or a variant of normal. If growth is considered abnormal, system and hormonal tests are likely to be required, followed by more directed testing, such as skeletal survey and/or genetic screening with karyotype or microarray. In a small percentage of short children in whom a diagnosis has not been reached, this will need to be followed by detailed genetic analysis; currently, exome sequencing using targeted panels relevant to the phenotype is the commonly used test. Clinical scenarios are presented that illustrate how such genetic testing can be used to establish a molecular diagnosis, and how that diagnosis contributes to the management of the short child. New genetic causes for short stature are being recognized on a frequent basis, while the clinical spectrum for known genes is being extended. We recommend that an international repository for short stature conditions is established for new findings to aid dissemination of knowledge, but also to help in the definition of the clinical spectrum both for new and established conditions.

Novel Mutations and Genes That Impact on Growth in Short Stature of Undefined Aetiology: The EPIGROW Study.

BACKGROUND: Children with short stature of undefined aetiology (SS-UA) may have undiagnosed genetic conditions. PURPOSE: To identify mutations causing short stature (SS) and genes related to SS, using candidate gene sequence data from the European EPIGROW study. METHODS: First, we selected exonic single nucleotide polymorphisms (SNPs), in cases and not controls, with minor allele frequency (MAF) < 2%, whose carriage fitted the mode of inheritance. Known mutations were identified using Ensembl and gene-specific databases. Variants were classified as pathogenic, likely pathogenic, or variant of uncertain significance using criteria from the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. If predicted by ≥ 5/10 algorithms (eg, Polyphen2) to be deleterious, this was considered supporting evidence of pathogenicity. Second, gene-based burden testing determined the difference in SNP frequencies between cases and controls across all and then rare SNPs. For genotype/phenotype relationships, we used PLINK, based on haplotype, MAF > 2%, genotype present in > 75%, and Hardy Weinberg equilibrium P > 10-4. RESULTS: First, a diagnostic yield of 10% (27/263) was generated by 2 pathogenic (nonsense in ACAN) and a further 25 likely pathogenic mutations, including previously known missense mutations in FANCB, IGFIR, MMP13, NPR2, OBSL1, and PTPN11. Second, genes related to SS: all methods identified PEX2. Another 7 genes (BUB1B, FANCM, CUL7, FANCA, PTCH1, TEAD3, BCAS3) were identified by both gene-based approaches and 6 (A2M, EFEMP1, PRKCH, SOS2, RNF135, ZBTB38) were identified by gene-based testing for all SNPs and PLINK. CONCLUSIONS: Such panels improve diagnosis in SS-UA, extending known disease phenotypes. Fourteen genes related to SS included some known to cause growth disorders as well as novel targets.