Molecular genetic basis of non-syndromic retinal dystrophies
Bibliographic record
Abstract
The aim of this thesis was to identify and characterize genetic defects underlying retinal ciliopathies. Initially, genetic defects were detected by homozygosity mapping using high-density genome-wide single nucleotide polymorphism (SNP) arrays in combination with a positional candidate gene approach. After the advent of next generation sequencing technology, the approach shifted to a combination of homozygosity mapping with targeted next generation sequencing, which, finally, was replaced by exome sequencing.\nChapter 2 summarizes all genes, mutations and modifier alleles associated with non-syndromic retinal ciliopathies, the progress that was made in dissecting the associated retinal disease mechanisms, and an evaluation of gene augmentation approaches to antagonize retinal degeneration.\nChapter 3 describes mutation analysis of IQCB1 in 226 individuals affected by Leber congenital amaurosis (LCA). We identified frameshift and nonsense mutations in 12 individuals diagnosed with LCA.\nIn Chapter 4, we performed restriction fragment length polymorphism analysis of the BBS1 p.Met390Arg allele in 2,007 individuals affected by autosomal recessive retinitis pigmentosa (RP). We found a significant association between this BBS1 variant and non-syndromic autosomal recessive RP and relatively mild forms of Bardet-Biedl syndrome (BBS).\nChapter 5A presents C8orf37 as a new gene involved in RP and cone-rod dystrophy. In Chapter 5B the interactome of C8orf37 is described and the role of C8orf37 in photoreceptors is proposed.\nFinally, Chapter 6 is dedicated to the general discussion.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".