Whole-Exome Sequencing in Congenital Hypothyroidism Due to Thyroid Dysgenesis
Bibliographic record
Abstract
Context: Congenital hypothyroidism due to thyroid dysgenesis (CHTD) is a predominantly sporadic and nonsyndromic (NS) condition of unknown etiology. NS-CHTD shows a 40-fold increase in relative risk among first-degree relatives (1 in 100 compared with a birth prevalence of 1 in 4000 in the general population), but a discordance rate between monozygotic (MZ) twins of 92%. This suggests a two-hit mechanism, combining a genetic predisposition (incomplete penetrance of inherited variants) with postzygotic events (accounting for MZ twin discordance). Objective: To evaluate whether whole-exome sequencing (WES) allows to identify new predisposing genes in NS-CHTD. Methods: We performed a case–control study by comparing the whole exome of 36 nonconsanguineous cases of NS-CHTD (33 with lingual thyroid ectopy and 3 with athyreosis, based on technetium pertechnetate scintigraphy at diagnosis) with that of 301 unaffected controls to assess for enrichment in rare protein-altering variants. We performed an unbiased approach using a gene-based burden with a false discovery rate correction. Moreover, we identified all rare pathogenic and likely pathogenic variants, based on in silico prediction tools, in 27 genes previously associated with congenital hypothyroidism (CH) (thyroid dysgenesis [TD] and dyshormonogenesis). Results: After correction for multiple testing, no enrichment in rare protein-altering variants was observed in NS-CHTD. Pathogenic or likely pathogenic variants (21 variants in 12 CH genes) were identified in 42% of cases. Eight percent of cases had variants in more than one gene (oligogenic group); these were not more severely affected than monogenic cases. Moreover, cases with protein-altering variants in dyshormonogenesis-related genes were not more severely affected than those without. Conclusions: No new predisposing genes were identified following an unbiased analysis of WES data in a well-characterized NS-CHTD cohort. Nonetheless, the discovery rate of rare pathogenic or likely pathogenic variants was 42%. Eight percent of the cases harbored multiple variants in genes associated with TD or dyshormonogenesis, but these variants did not explain the variability of hypothyroidism observed in dysgenesis. WES did not identify a genetic cause in NS-CHTD cases, confirming the complex etiology of this disease. Additional studies in larger cohorts and/or novel discovery approaches are required.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".