BBS Mutational Analysis: A Strategic Approach
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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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".