Novel mutations in the small leucine-rich repeat protein/proteoglycan (SLRP) genes in high myopia
Bibliographic record
Abstract
The importance of the genetic component in high myopia has been well established in population and family studies, but only a few candidate genes have been explored to date. The extracellular matrix small leucine-rich repeat proteins/proteoglycans (SLRPs) regulate collagen fibril diameter and spacing. Given their role in extracellular matrix assembly and expression in the eye, they are likely to regulate its shape and size. Analysis of 85 English and 40 Finnish subjects with high myopia (refractive error of -6 diopters [D] or greater) resulted in 23 sequence variations in four SLRP genes, LUM, FMOD, PRELP, and OPTC. We observed higher number of variations in OPTC in English patients than in controls (p=0.042), and a possibly protective variation in LUM (c.893-105G>A) with p-value of 0.0043. Two intronic variations, six nonsynonymous and one synonymous amino acid changes, were not found in any of the nonmyopic controls. Five changes were detected in opticin, Thr177Arg, Arg229His, Arg325Trp, Gly329Ser, and Arg330His, and all but one (Arg229His) were shown to cosegregate with high myopia in families with incomplete penetrance. A homology model for opticin revealed that Arg229His and Arg325Trp are likely to disrupt the protein structure, and PolyPhen analysis suggested that Thr177Arg, Arg325Trp, and Gly329Ser changes may be damaging. A Leu199Pro change in lumican and Gly147Asp and Arg324Thr variations in fibromodulin are located in the highly conserved leucine-rich repeat (LRR) domains. This study provides new insight into the genetics of high myopia, suggesting that sequence variations in the SLRP genes expressed in the eye may be among the genetic risk factors underlying the pathogenesis of high myopia.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".