Identification of the Genetic Causes of Inherited Diseases in a North African Biobank: Implications for Genetic Diagnosis
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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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 | 0.002 | 0.005 |
| 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) | 0.002 | 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".