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      Screening a large pediatric cohort with GH deficiency for mutations in genes regulating pituitary development and GH secretion: Frequencies, phenotypes and growth outcomes

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          Translated abstract


          Pituitary development and GH secretion are orchestrated by multiple genes including GH1, GHRHR, GLI2, HESX1, LHX3, LHX4, PROP1, POU1F1, and SOX3. We aimed to assess their mutation frequency and clinical relevance in children with severe GH deficiency (GHD).


          The Genetics and Neuroendocrinology of Short Stature International Study (GeNeSIS; Clinical Trial Registry Number: NCT01088412) was a prospective, open-label, observational research program for pediatric patients receiving GH treatment, conducted in 30 countries between 1999 and 2015. The study included a sub-study to investigate mutations in the genes listed above. PCR products from genomic blood cell DNA were analyzed by Sanger sequencing. DNA variants were classified as pathogenic according to the recommendations of the American College of Medical Genetics and Genomics. Demographic, auxologic, and endocrine data at baseline and during GH treatment were documented and related to the genotyping results.


          The analysis comprised 917 patients. In 92 patients (10%) 33 mutations were found, 16 previously described and 17 novel (52%). Mutation carriers were significantly younger, shorter, and more slowly growing than non-carriers. In general, their peak values in GH stimulation tests were very low; however, in 15/77 (20%) patients with GH1, PROP1, and SOX3 mutations they were only moderately diminished (3-6 μg/L). Two patients with a GH1 mutation developed TSH deficiency and one ADH deficiency. Using logistic multi-regression analysis, significant indicators of a mutation were combined pituitary hormone deficiency, greater patient-parent height difference (SDS), low GH peak, and young age. Final height SDS gain in mutation carriers (mean ± SD 3.4 ± 1.4) was greater than in non-carriers (2.0 ± 1.4; P < .001) and in patients with non-GHD short stature.


          DNA testing for mutations in children with severe GHD shows a positive finding in approximately 10%. Phenotypes of mutation carriers can be variable. The benefit for clinical practice justifies DNA testing as an important component in the diagnostic work-up of patients with severe GHD.


          Eli Lilly and Company, Indianapolis, IN, USA.

 registration: NCT01088412.

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          Most cited references 59

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          Analysis of protein-coding genetic variation in 60,706 humans

          Summary Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. We describe the aggregation and analysis of high-quality exome (protein-coding region) sequence data for 60,706 individuals of diverse ethnicities generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of truncating variants with 72% having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human “knockout” variants in protein-coding genes.
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            Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology

            The American College of Medical Genetics and Genomics (ACMG) previously developed guidance for the interpretation of sequence variants. 1 In the past decade, sequencing technology has evolved rapidly with the advent of high-throughput next generation sequencing. By adopting and leveraging next generation sequencing, clinical laboratories are now performing an ever increasing catalogue of genetic testing spanning genotyping, single genes, gene panels, exomes, genomes, transcriptomes and epigenetic assays for genetic disorders. By virtue of increased complexity, this paradigm shift in genetic testing has been accompanied by new challenges in sequence interpretation. In this context, the ACMG convened a workgroup in 2013 comprised of representatives from the ACMG, the Association for Molecular Pathology (AMP) and the College of American Pathologists (CAP) to revisit and revise the standards and guidelines for the interpretation of sequence variants. The group consisted of clinical laboratory directors and clinicians. This report represents expert opinion of the workgroup with input from ACMG, AMP and CAP stakeholders. These recommendations primarily apply to the breadth of genetic tests used in clinical laboratories including genotyping, single genes, panels, exomes and genomes. This report recommends the use of specific standard terminology: ‘pathogenic’, ‘likely pathogenic’, ‘uncertain significance’, ‘likely benign’, and ‘benign’ to describe variants identified in Mendelian disorders. Moreover, this recommendation describes a process for classification of variants into these five categories based on criteria using typical types of variant evidence (e.g. population data, computational data, functional data, segregation data, etc.). Because of the increased complexity of analysis and interpretation of clinical genetic testing described in this report, the ACMG strongly recommends that clinical molecular genetic testing should be performed in a CLIA-approved laboratory with results interpreted by a board-certified clinical molecular geneticist or molecular genetic pathologist or equivalent.
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              Human Splicing Finder: an online bioinformatics tool to predict splicing signals

              Thousands of mutations are identified yearly. Although many directly affect protein expression, an increasing proportion of mutations is now believed to influence mRNA splicing. They mostly affect existing splice sites, but synonymous, non-synonymous or nonsense mutations can also create or disrupt splice sites or auxiliary cis-splicing sequences. To facilitate the analysis of the different mutations, we designed Human Splicing Finder (HSF), a tool to predict the effects of mutations on splicing signals or to identify splicing motifs in any human sequence. It contains all available matrices for auxiliary sequence prediction as well as new ones for binding sites of the 9G8 and Tra2-β Serine-Arginine proteins and the hnRNP A1 ribonucleoprotein. We also developed new Position Weight Matrices to assess the strength of 5′ and 3′ splice sites and branch points. We evaluated HSF efficiency using a set of 83 intronic and 35 exonic mutations known to result in splicing defects. We showed that the mutation effect was correctly predicted in almost all cases. HSF could thus represent a valuable resource for research, diagnostic and therapeutic (e.g. therapeutic exon skipping) purposes as well as for global studies, such as the GEN2PHEN European Project or the Human Variome Project.

                Author and article information

                [a ]University Hospital for Children and Adolescents, University of Leipzig, Liebigstrasse 20a, 04103 Leipzig, Germany
                [b ]Center of Child and Adolescent Medicine, Justus Liebig University, Feulgenstrasse 12, 35392 Giessen, Germany
                [c ]Sorbonne Université, Inserm UMR_S933, Département de Génétique, Hôpital Trousseau, AP-HP, 75012 Paris, France
                [d ]Eli Lilly and Company, Windlesham, GU20 6PH, UK
                [e ]Eli Lilly and Company, Werner-Reimers-Strasse 2-4, 61352 Bad Homburg, Germany
                [f ]Eli Lilly and Company, Indianapolis, USA
                [g ]Sydney Children's Hospital, Randwick, Australia
                [h ]Gordon Cutler Consultancy LLC, Deltaville, USA
                [i ]University of Montreal and CHU Ste-Justine, Montreal, Canada
                [j ]Department of Pediatrics, 2nd Faculty of Medicine, Charles University, University Hospital Motol, V Uvalu 84, 150 06 Prague, 5, Czech Republic
                [k ]Department of Pediatrics, Oregon Health and Science University, Portland, USA
                [l ]Division of Pediatric Endocrinology and Diabetes, Emory University School of Medicine, 2015 Uppergate Dr, Atlanta, GA 30322, USA
                Author notes
                [* ]Corresponding author at: Friedrich-Stengel-Str. 14, D-61250 Usingen, Germany. wernblum@

                Present address: Sorbonne Université, Inserm UMR_S938, Hôpital Saint-Antoine, 75013 Paris, France.


                Present address: Sorbonne Université, Inserm U1127, Brain and Spine Institute, Hôpital de la Pitié-Salpétrière, 75013 Paris, France.

                25 September 2018
                October 2018
                25 September 2018
                : 36
                : 390-400
                30266296 6197701 S2352-3964(18)30384-0 10.1016/j.ebiom.2018.09.026
                © 2018 Eli LIlly and Company

                This is an open access article under the CC BY-NC-ND license (

                Research paper


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