Retrotransposon insertion as a novel mutational event in Bardet‐Biedl syndrome
Bibliographic record
Abstract
BACKGROUND: Bardet-Biedl syndrome (BBS) is an autosomal recessive pleiotropic disorder of the primary cilia that leads to severe visual loss in the teenage years. Approximately 80% of BBS cases are explained by mutations in one of the 21 identified genes. Documented causative mutation types include missense, nonsense, copy number variation (CNV), frameshift deletions or insertions, and splicing variants. METHODS: Whole genome sequencing was performed on a patient affected with BBS for whom no mutations were identified using clinically approved genetic testing of the known genes. Analysis of the WGS was done using internal protocols and publicly available algorithms. The phenotype was defined by retrospective chart review. RESULTS: We document a female affected with BBS carrying the most common BBS1 mutation (BBS1: Met390Arg) on the maternal allele and an insertion of a ~1.7-kb retrotransposon in exon 13 on the paternal allele. This retrotransposon insertion was not automatically annotated by the standard variant calling protocols used. This novel variant was identified by visual inspection of the alignment file followed by specific genome analysis with an available algorithm for transposable elements. CONCLUSION: This report documents a novel mutation type associated with BBS and highlights the importance of systematically performing transposon detection analysis on WGS data of unsolved cases.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".