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- Blair, J. C., et al.
(author)
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Standard and low-dose IGF-I generation tests and spontaneous growth hormone secretion in children with idiopathic short stature
- 2004
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In: Clin Endocrinol (Oxf). ; 60:2, s. 163-8
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Journal article (peer-reviewed)abstract
- OBJECTIVE: Abnormalities in the GH-IGF-I axis, consistent with GH insensitivity (GHI), have been reported in some patients with idiopathic short stature (ISS). The standard IGF-I generation test (IGFGT) has not demonstrated mild GHI in subjects with ISS. The aim of this study was to investigate the GH-IGF-I axis in ISS by performing standard and novel low-dose IGFGTs together with determination of spontaneous GH secretion. PATIENTS AND METHODS: Twenty-one (17 male) prepubertal children with ISS, mean age 8.3 years (4.5-12.2), mean height -3.48 SD (-5.40 to -1.79), mean peak GH to provocation with glucagon/clonidine 32.3 mU/l (14.1-66.0) were studied. Serum IGF-I and IGFBP-3 levels were measured during standard (GH 0.033 mg/kg/day x 4) and low (GH 0.011 mg/kg/day x 4) dose IGFGTs at 0, 12, 36 and 84 h. The low-dose IGFGT was performed in seven naive GH-deficient patients (4 male), mean age 8.5 years (range 4.1-11.1). Determination of spontaneous 24-h GH secretion was performed in the 21 ISS patients. RESULTS: Basal IGF-I and IGFBP-3 standard deviation scores (SDS) in ISS patients were -1.39 (-2.4-1.16) and -0.45 (-1.13-0.38), respectively, IGF-I being lower than IGFBP-3 (P < 0.0001). IGF-I increased in the standard IGFGT at 12 h (P < 0.005), 36 h (P < 0.001) and 84 h (P < 0.001); maximal increment 1.54 (-0.32-3.48), and in the low-dose test at 12 h (P < 0.005), 36 h (P < 0.001) and 84 h (P < 0.005); maximal increment 0.53 (0.08 to -1.23). IGFBP-3 SDS increased in the standard IGFGT at 36 h (P < 0.01) and 84 h (P < 0.001); maximal increment 0.72 (-0.44-1.96), and in the low-dose test at 84 h (P < 0.005); maximal increment 0.33 (-0.08-0.87). Five/19 patients with an IGF-I response > 2 x coefficient of variation (CV) of assay in the standard test failed to respond in the low-dose test, suggestive of mild GHI. In GH-deficient patients, IGF-I increased at each time point (P < 0.05) and IGFBP-3 at 36 h (P < 0.05). Mean GH secretion, expressed in SDS, compared with 66 normal stature controls was: basal GH -0.48 (-0.84-0.93), height of GH peaks compared with zero -0.36 (-1.26-1.51) (both P < 0.05), total GH secretion -0.76 (-1.22-0.42), total GH secretion above baseline -0.67 (-1.21-0.94) (both P < 0.01). CONCLUSIONS: In children with ISS, basal IGF-I and IGFBP-3 SDS values were below the mean, IGF-I showing a greater response in both IGFGTs. In the standard IGFGT, the IGF-I increase at 36 h was equal to that at 84 h. The low-dose IGFGT, in combination with the standard test, may identify patients with mild GHI.
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- Craddock, Nick, et al.
(author)
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Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls
- 2010
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In: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 464:7289, s. 713-720
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Journal article (peer-reviewed)abstract
- Copy number variants (CNVs) account for a major proportion of human genetic polymorphism and have been predicted to have an important role in genetic susceptibility to common disease. To address this we undertook a large, direct genome-wide study of association between CNVs and eight common human diseases. Using a purpose-designed array we typed,19,000 individuals into distinct copy-number classes at 3,432 polymorphic CNVs, including an estimated similar to 50% of all common CNVs larger than 500 base pairs. We identified several biological artefacts that lead to false-positive associations, including systematic CNV differences between DNAs derived from blood and cell lines. Association testing and follow-up replication analyses confirmed three loci where CNVs were associated with disease-IRGM for Crohn's disease, HLA for Crohn's disease, rheumatoid arthritis and type 1 diabetes, and TSPAN8 for type 2 diabetes-although in each case the locus had previously been identified in single nucleotide polymorphism (SNP)-based studies, reflecting our observation that most common CNVs that are well-typed on our array are well tagged by SNPs and so have been indirectly explored through SNP studies. We conclude that common CNVs that can be typed on existing platforms are unlikely to contribute greatly to the genetic basis of common human diseases.
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- Romagnoni, A, et al.
(author)
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Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
- 2019
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In: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 10351-
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Journal article (peer-reviewed)abstract
- Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.
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