BBS Mutational Analysis: A Strategic Approach
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Bibliographic record
Abstract
BACKGROUND: Bardet-Biedl syndrome (BBS, OMIM 209900) is a rare autosomal recessive, clinically and genetically heterogeneous disorder with 15 genes identified. The large amount of coding sequence challenges the cost effectiveness of mutational analysis of BBS. MATERIAL AND METHODS: We present our mutational analysis experience (83 BBS families) in the context of the literature published up to September 2010, to provide a comprehensive tabulation of all BBS1-BBS12 mutant alleles and optimize a screening approach. RESULTS: We identified two BBS disease alleles in 76% of probands. Together BBS1, BBS2, BBS10 and BBS12 account for 82.4% of published unrelated alleles. On average 82% of published alleles are private. The 267 published principal mutations were positioned and analysis of their distribution allowed the design of a mutation screening strategy. Starting by screening for recurrent mutations, for example BBS1 M390R (10% of our cohort) and BBS10 C91LfsX5 (6% of our cohort), allowed a capture of 23.5% of the principal mutated alleles. Following a hierarchy of frequently involved exons, subsequent sequencing of the 4 most commonly involved genes, BBS1, BBS10, BBS2 and BBS12 could bring this mutation detection to at least 62%. The 16 most frequently recurring alleles could be identified with the use of simple screening methods such as restriction enzyme digest and ARMS assay and require sequencing in only a few instances. CONCLUSION: Our results suggest that mutational analysis of such a "rare" genetically heterogeneous condition benefits from pooling of data. This allows the development of efficient and cost-conscious screening mutational analysis strategies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it