Exome sequencing of Saudi Arabian patients with ADPKD
Bibliographic record
Abstract
Purpose: Autosomal dominant polycystic kidney disease (ADPKD) is characterized by progressive development of kidney cysts and enlargement and dysfunction of the kidneys. The Consortium of Radiologic Imaging Studies of the Polycystic Kidney Disease (CRISP) cohort revealed that 89.1% had either a PKD1 or PKD2 mutation. Of the CRISP patients with a genetic cause detected, mutations in PKD1 accounted for 85%, while mutations in the PKD2 accounted for the remaining 15%. Here, we report exome sequencing of 16 Saudi patients diagnosed with ADPKD and 16 ethnically matched controls.Methods: Exome sequencing was performed using combinatorial probe-anchor synthesis and improved DNA Nanoballs technology on BGISEQ-500 sequencers (BGI, China) using the BGI Exome V4 (59 Mb) Kit. Identified variants were validated with Sanger sequencing.Results: With the exception of GC-rich exon 1, we obtained excellent coverage of PKD1 (mean read depth = 88) including both duplicated and non-duplicated regions. Of nine patients with typical ADPKD presentations (bilateral symmetrical kidney involvement, positive family history, concordant imaging, and kidney function), four had protein truncating PKD1 mutations, one had a PKD1 missense mutation, and one had a PKD2 mutation. These variants have not been previously observed in the Saudi population. In seven clinically diagnosed ADPKD cases but with atypical features, no PKD1 or PKD2 mutations were identified, but rare predicted pathogenic heterozygous variants were found in cystogenic candidate genes including PKHD1, PKD1L3, EGF, CFTR, and TSC2.Conclusions: Mutations in PKD1 and PKD2 are the most common cause of ADPKD in Saudi patients with typical ADPKD. Abbreviations: ADPKD: Autosomal dominant polycystic kidney disease; CFTR: Cystic fibrosis transmembrane conductance regulator; EGF: Epidermal growth factor; MCIC: Mayo Clinic Imaging Classification; PKD: Polycystic kidney disease; TSC2: Tuberous sclerosis complex 2
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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 | 0.000 | 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) | 0.001 | 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".