Sex Distributions in the Most Frequent Autosomal Genetic Causes of Retinitis Pigmentosa
Bibliographic record
Abstract
Purpose: The purpose of this study was to explore whether sex imbalances are detectable in the most frequent genetic causes of retinitis pigmentosa (RP). Methods: Databases from centers in three countries (Moorfields Eye Hospital, London; Hospital for Sick Children, Toronto; and Australian Inherited Retinal Disease Registry, Perth, Australia) were searched, quantifying numbers of male and female patients with disease attributed to variants in the six most frequently involved autosomal RP genes. Proportions of female patients (with 95% confidence intervals [CIs]) were calculated for each gene. Two-tailed binomial testing was performed (Bonferroni corrected threshold, P = 0.008) to investigate whether proportions differed significantly from an underlying male:female ratio of 1:1. For genes where the 95% CI did not include 50%, sex distributions were also explored in previously published cohorts. Results: Our search yielded 1454 patients with disease attributable to variants in USH2A (n = 550), RP1 (n = 277), RHO (n = 246), PRPF31 (n = 158), EYS (n = 124), and MYO7A (n = 99). Proportions of female patients (95% CI) for each gene were 46.2% (42.0-50.5%), 49.5% (43.4-55.5%), 55.3% (48.8-61.6%), 63.9% (55.9-71.3%), 39.5% (31.0-48.7%), and 42.4% (32.7-52.8%), respectively. The 95% CI did not include 50% for PRPF31 and EYS; binomial testing revealed P values of 6.24 × 10-4 and 0.025, respectively. Combining with data extracted from previously published cohorts yielded P values of 1.62 × 10-6 and 0.0084, respectively. Conclusions: We observed a significant preponderance of female patients for PRPF31-associated RP and a preponderance of male patients in those with EYS-associated RP. Our findings suggest that sex is likely to be a modifier affecting penetrance in PRPF31-associated disease and might act in the opposite direction in disease associated with EYS.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".