Impact of whole exome sequencing among Iranian patients with autosomal recessive retinitis pigmentosa.
Bibliographic record
Abstract
BACKGROUND: Non-syndromic autosomal recessive Retinitis Pigmentosa (arRP) is a highly heterogeneous genetic visual disorder with a large number of causative genes. We aimed to determine the power of Whole Exome Sequencing (WES) in the identification of the genes responsible for non-syndromic arRP among Iranian patients. METHODS: We used WES, followed by the Sanger sequencing to identify the underlying gene mutations causing non-syndromic arRP. RESULTS: Our study revealed disease-causing mutations in known arRP genes for 10 of the 13 families studied (76.9%). These mutations included two-frameshift insertion/deletion in CRB1 and ABCA4, one splicing mutation in PDE6B, four missense mutations in RP1, CRB1, PANK2 and IFT140, as well as three stop codon mutations in RDH12, PRCD, and C2orf71. Three remaining families harbored no mutation in previously known RP genes. Of the 10 diseases causing mutations identified among the investigated Iranian patients with non-syndromic arRP, eight variants had not been reported previously. We confirmed segregation of all 10 mutations with disease phenotypes in our studied population. CONCLUSION: This study supports the genetic heterogeneity of non-syndromic arRP in Iranian patients, and provides an opportunity to show the effectiveness of WES in the identification of pathogenic mutations among patients with non-syndromic arRP born to consanguineous parents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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".