Candidate exome capture identifies mutation of SDCCAG8 as the cause of a retinal-renal ciliopathy
Bibliographic record
Abstract
Friedhelm Hildebrandt and colleagues combine homozygosity mapping with candidate exome capture and high-throughput sequencing to identify SDCCAG8 mutations as the cause of a retinal-renal ciliopathy. They further show that SDCCAG8 localizes to centrioles and that its depletion causes renal cysts and cell polarity defects. Nephronophthisis-related ciliopathies (NPHP-RC) are recessive disorders that feature dysplasia or degeneration occurring preferentially in the kidney, retina and cerebellum. Here we combined homozygosity mapping with candidate gene analysis by performing 'ciliopathy candidate exome capture' followed by massively parallel sequencing. We identified 12 different truncating mutations of SDCCAG8 (serologically defined colon cancer antigen 8, also known as CCCAP) in 10 families affected by NPHP-RC. We show that SDCCAG8 is localized at both centrioles and interacts directly with OFD1 (oral-facial-digital syndrome 1), which is associated with NPHP-RC. Depletion of sdccag8 causes kidney cysts and a body axis defect in zebrafish and induces cell polarity defects in three-dimensional renal cell cultures. This work identifies loss of SDCCAG8 function as a cause of a retinal-renal ciliopathy and validates exome capture analysis for broadly heterogeneous single-gene disorders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".