Whole exome sequencing unravels disease‐causing genes in consanguineous families in Qatar
Bibliographic record
Abstract
Whole exome sequencing (WES) has greatly facilitated the identification of causal mutations for diverse human genetic disorders. We applied WES as a molecular diagnostic tool to identify disease-causing genes in consanguineous families in Qatar. Seventeen consanguineous families with diverse disorders were recruited. Initial mutation screening of known genes related to the clinical diagnoses did not reveal the causative mutations. Using WES approach, we identified the definitive disease-causing mutations in four families: (i) a novel nonsense homozygous (c.1034C>G) in PHKG2 causing glycogen storage disease type 9C (GSD9C) in a male with initial diagnosis of GSD3; (ii) a novel homozygous 1-bp deletion (c.915del) in NSUN2 in a male proband with Noonan-like syndrome; (iii) a homozygous SNV (c.1598C>G) in exon 11 of IDUA causing Hurler syndrome in a female proband with unknown clinical diagnosis; (iv) a de novo known splicing mutation (c.1645+1G>A) in PHEX in a female proband with initial diagnosis of autosomal recessive hypophosphatemic rickets. Applying WES as a diagnostic tool led to the unambiguous identification of disease-causing mutations in phenotypically complex disorders or correction of the initial clinical diagnosis in ˜25% of our cases.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".