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      Validating genetic markers of response to recombinant human growth hormone in children with growth hormone deficiency and Turner syndrome: the PREDICT validation study

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          Abstract

          Objective

          Single-nucleotide polymorphisms (SNPs) associated with the response to recombinant human growth hormone (r-hGH) have previously been identified in growth hormone deficiency (GHD) and Turner syndrome (TS) children in the PREDICT long-term follow-up (LTFU) study (Nbib699855). Here, we describe the PREDICT validation (VAL) study (Nbib1419249), which aimed to confirm these genetic associations.

          Design and methods

          Children with GHD ( n = 293) or TS ( n = 132) were recruited retrospectively from 29 sites in nine countries. All children had completed 1 year of r-hGH therapy. 48 SNPs previously identified as associated with first year growth response to r-hGH were genotyped. Regression analysis was used to assess the association between genotype and growth response using clinical/auxological variables as covariates. Further analysis was undertaken using random forest classification.

          Results

          The children were younger, and the growth response was higher in VAL study. Direct genotype analysis did not replicate what was found in the LTFU study. However, using exploratory regression models with covariates, a consistent relationship with growth response in both VAL and LTFU was shown for four genes – SOS1 and INPPL1 in GHD and ESR1 and PTPN1 in TS. The random forest analysis demonstrated that only clinical covariates were important in the prediction of growth response in mild GHD (>4 to <10 μg/L on GH stimulation test), however, in severe GHD (≤4 μg/L) several SNPs contributed (in IGF2, GRB10, FOS, IGFBP3 and GHRHR).

          Conclusions

          The PREDICT validation study supports, in an independent cohort, the association of four of 48 genetic markers with growth response to r-hGH treatment in both pre-pubertal GHD and TS children after controlling for clinical/auxological covariates. However, the contribution of these SNPs in a prediction model of first-year response is not sufficient for routine clinical use.

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          Most cited references22

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          Chapter 11: Genome-Wide Association Studies

          Genome-wide association studies (GWAS) have evolved over the last ten years into a powerful tool for investigating the genetic architecture of human disease. In this work, we review the key concepts underlying GWAS, including the architecture of common diseases, the structure of common human genetic variation, technologies for capturing genetic information, study designs, and the statistical methods used for data analysis. We also look forward to the future beyond GWAS.
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            Gain-of-function SOS1 mutations cause a distinctive form of Noonan syndrome.

            Noonan syndrome is a developmental disorder characterized by short stature, facial dysmorphia, congenital heart defects and skeletal anomalies. Increased RAS-mitogen-activated protein kinase (MAPK) signaling due to PTPN11 and KRAS mutations causes 50% of cases of Noonan syndrome. Here, we report that 22 of 129 individuals with Noonan syndrome without PTPN11 or KRAS mutation have missense mutations in SOS1, which encodes a RAS-specific guanine nucleotide exchange factor. SOS1 mutations cluster at codons encoding residues implicated in the maintenance of SOS1 in its autoinhibited form. In addition, ectopic expression of two Noonan syndrome-associated mutants induces enhanced RAS and ERK activation. The phenotype associated with SOS1 defects lies within the Noonan syndrome spectrum but is distinctive, with a high prevalence of ectodermal abnormalities but generally normal development and linear growth. Our findings implicate gain-of-function mutations in a RAS guanine nucleotide exchange factor in disease for the first time and define a new mechanism by which upregulation of the RAS pathway can profoundly change human development.
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              Prediction of response to growth hormone treatment in short children born small for gestational age: analysis of data from KIGS (Pharmacia International Growth Database).

              A model was developed that allows physicians to individualize GH treatment in children born short for gestational age (SGA) who fail to show spontaneous catch-up growth. Data from children (n = 613) in a large pharmacoepidemiological survey, the KIGS (Pharmacia International Growth Database), or who had participated in clinical trials were used to develop the model. Another group of similar children (n = 68) from KIGS was used for validation. In the first year of GH treatment, the growth response correlated positively with GH dose, weight at the start of GH treatment, and midparental height SD score and negatively with age at treatment start. Using this model, 52% of the variability of the growth response could be explained, with a mean error SD of 1.3 cm. GH dose was the most important response predictor (35% of variability), followed by age at treatment start. The second year growth response was best predicted by a three-parameter model (height velocity in yr 1 of treatment, age at start of treatment, and GH dose), which accounted for 34% of the variability, with an error SD of 1.1 cm. The first year response to GH treatment was the most important predictor of the second year response, accounting for 29% of the variability. No statistically significant differences between the predicted and observed growth responses were found when the models were applied to the validation groups. In conclusion, using simple variables, we have developed a model that can be used in clinical practice to adjust the GH dose to achieve the desired growth response in patients born SGA. Furthermore, this model can be used to provide patients with a realistic expectation of treatment and may help to identify compliance problems or other underlying causes of treatment failure.

                Author and article information

                Journal
                Eur J Endocrinol
                Eur. J. Endocrinol
                EJE
                European Journal of Endocrinology
                Bioscientifica Ltd (Bristol )
                0804-4643
                1479-683X
                October 2016
                19 October 2016
                : 175
                : 6
                : 633-643
                Affiliations
                [1 ]Faculty of Biology Medicine and Health, University of Manchester and Manchester Academic Health Science Centre, Royal Manchester Children’s Hospital, Central Manchester University Hospitals NHS Foundation Trust, Manchester, UK
                [2 ]Quartz Bio Geneva, Switzerland
                [3 ]Genizon BioSciences St Laurent, Quebec, Canada
                [4 ]Merck KGaA Darmstadt, Germany
                [5 ]Department Pediatrie Hôpital Mère-Enfant – Université Claude Bernard, Lyon, France
                Author notes
                Correspondence should be addressed to P Clayton; Email: peter.clayton@ 123456manchester.ac.uk
                [†]

                (Details of the PREDICT Investigator Group is presented in the Acknowledgements section)

                Article
                EJE160357
                10.1530/EJE-16-0357
                5097129
                27651465
                0286bef9-cfb5-48c9-8b71-ca297355ace4
                © 2016 The authors

                This work is licensed under a Creative Commons Attribution 3.0 Unported License.

                History
                : 20 April 2016
                : 16 August 2016
                : 20 September 2016
                Categories
                Clinical Study

                Endocrinology & Diabetes
                Endocrinology & Diabetes

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