Biallelic <scp> <i>TMEM260</i> </scp> variants cause truncus arteriosus, with or without renal defects
Bibliographic record
Abstract
Only two families have been reported with biallelic TMEM260 variants segregating with structural heart defects and renal anomalies syndrome (SHDRA). With a combination of genome, exome sequencing and RNA studies, we identified eight individuals from five families with biallelic TMEM260 variants. Variants included one multi-exon deletion, four nonsense/frameshifts, two splicing changes and one missense change. Together with the published cases, analysis of clinical data revealed ventricular septal defects (12/12), mostly secondary to truncus arteriosus (10/12), elevated creatinine levels (6/12), horse-shoe kidneys (1/12) and renal cysts (1/12) in patients. Three pregnancies were terminated on detection of severe congenital anomalies. Six patients died between the ages of 6 weeks and 5 years. Using a range of stringencies, carrier frequency for SHDRA was estimated at 0.0007-0.007 across ancestries. In conclusion, this study confirms the genetic basis of SHDRA, expands its known mutational spectrum and clarifies its clinical features. We demonstrate that SHDRA is a severe condition associated with substantial mortality in early childhood and characterised by congenital cardiac malformations with a variable renal phenotype.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 0.001 |
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".