Identification of the Genetic Causes of Inherited Diseases in a North African Biobank: Implications for Genetic Diagnosis
Why this work is in the frame
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Bibliographic record
Abstract
Introduction: In North Africa, genetic diseases are widespread but under-studied due to limited research resources. This study used exome sequencing to identify disease-causing variants in a large series of Moroccan patients with suspected genetic diseases. Methods: A cohort of 30 patients with genetic diseases from the BRO Biobank underwent exome sequencing. Candidate variants were evaluated by segregation analysis and molecular modeling. Results: Thirty-one variants were identified in 27 known genes. Interestingly, 54.8% of these variants were novel and therefore could be specific to the Moroccan population. Pathogenic or likely pathogenic disease-causing variants were identified in 22 of 30 patients, leading to a genetic testing yield of 73.3%. Moreover, the identified variants, classified as of uncertain significance, likely benign or benign, were predicted to alter protein structure using in silico modeling of 3D protein structure. The diagnosis was changed in 23% of patients with suspected genetic syndromes, and the etiology was determined in all patients with unrecognizable genetic disorder. Conclusion: This study represents the largest biobank-based study of inherited diseases in a North African country. It illustrates the genetic variability of the Moroccan population and improves our understanding of genotype-phenotype correlations. Furthermore, the relatively high yield of genetic testing obtained in this study justifies the need to implement exome sequencing in the clinical setting in Morocco for better genetic diagnosis.
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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