Simultaneous Detection of Common Founder Mutations Using a Cost-Effective Deep Sequencing Panel
Bibliographic record
Abstract
Inherited retinal diseases (IRDs) are a clinically and genetically heterogeneous group of diseases which cause visual loss due to Mendelian mutations in over 250 genes, making genetic diagnosis challenging and time-consuming. Here, we developed a new tool, CDIP (Cost-effective Deep-sequencing IRD Panel) in which a simultaneous sequencing of common mutations is performed. CDIP is based on simultaneous amplification of 47 amplicons harboring common mutations followed by next-generation sequencing (NGS). Following five rounds of calibration of NGS-based steps, CDIP was used in 740 IRD samples. The analysis revealed 151 mutations in 131 index cases. In 54 (7%) of these cases, CDIP identified the genetic cause of disease (the remaining were single-heterozygous recessive mutations). These include a patient that was clinically diagnosed with retinoschisis and found to be homozygous for NR2E3-c.932G>A (p.R311Q), and a patient with RP who is hemizygous for an RPGR variant, c.292C>A (p.H98N), which was not included in the analysis but is located in proximity to one of these mutations. CDIP is a cost-effective deep sequencing panel for simultaneous detection of common founder mutations. This protocol can be implemented for additional populations as well as additional inherited diseases, and mainly in populations with strong founder effects.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".