Copy Number Variants in Short Children Born Small for Gestational Age
Bibliographic record
Abstract
BACKGROUND/AIMS: In addition to genome-wide association studies (GWAS), height-associated genes may be uncovered by studying individuals with extreme short or tall stature. METHODS: Genome-wide analysis for copy number variants (CNVs), using single nucleotide polymorphism (SNP) arrays, was performed in 49 index cases born small for gestational age with persistent short stature. Segregation analysis was performed, and genes in CNVs were compared with information from GWAS, gene expression in rodents' growth plates, and published information. RESULTS: CNVs were detected in 13 cases. In 5 children a known cause of short stature was found: UPD7, UPD14, a duplication of the SHOX enhancer region, an IGF1R deletion, and a 22q11.21 deletion. In the remaining 8 cases, potential pathogenic CNVs were detected, either de novo (n = 1), segregating (n = 2), or not segregating with short stature (n = 5). Bioinformatic analysis of the de novo and segregating CNVs suggested that HOXD4, AGPS, PDE11A, OSBPL6, PRKRA and PLEKHA3, and possibly DGKB and TNFRSF11B are potential candidate genes. A SERPINA7 or NRK defect may be associated with an X-linked form of short stature. CONCLUSION: SNP arrays detected 5 known causes of short stature with prenatal onset and suggested several potential candidate genes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".