Genetic analysis of inherited bone marrow failure syndromes from one prospective, comprehensive and population-based cohort and identification of novel mutations
Bibliographic record
Abstract
INTRODUCTION: Inherited bone marrow failure syndromes (IBMFSs) often have substantial phenotypic overlap, thus genotyping is often critical for establishing a diagnosis. OBJECTIVES AND METHODS: To determine the genetic characteristics and mutation profiles of IBMFSs, a comprehensive population-based study that prospectively enrols all typical and atypical cases without bias is required. The Canadian Inherited Marrow Failure Study is such a study, and was used to extract clinical and genetic information for patients enrolled up to May 2010. RESULTS: Among the 259 primary patients with IBMFS enrolled in the study, the most prevalent categories were Diamond-Blackfan anaemia (44 patients), Fanconi anaemia (39) and Shwachman-Diamond syndrome (35). The estimated incidence of the primary IBMFSs was 64.5 per 10(6) births, with Fanconi anaemia having the highest incidence (11.4 cases per 10(6) births). A large number of patients (70) had haematological and non-haematological features that did not fulfil the diagnostic criteria of any specific IBMFS category. Disease-causing mutations were identified in 53.5% of the 142 patients tested, and in 16 different genes. Ten novel mutations in SBDS, RPL5, FANCA, FANCG, MPL and G6PT were identified. The most common mutations were nonsense (31 alleles) and splice site (28). Genetic heterogeneity of most IBMFSs was evident; however, the most commonly mutated gene was SBDS, followed by FANCA and RPS19. CONCLUSION: From this the largest published comprehensive cohort of IBMFSs, it can be concluded that recent advances have led to successful genotyping of about half of the patients. Establishing a genetic diagnosis is still challenging and there is a critical need to develop novel diagnostic tools.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".