Low population penetrance of variants associated with inherited retinal degenerations
Bibliographic record
Abstract
Inherited retinal degenerations (IRDs) are the leading cause of blindness in working-age adults and are thought to be monogenic with near-complete penetrance. However, traditional variant discovery based on phenotypic ascertainment may inflate penetrance estimates and obscure the true genotype-phenotype spectrum. We used large biobanks with linked genomic and clinical data to quantify the population-level penetrance of IRD-associated variants. We screened 317,964 All of Us (AoU) participants for loss-of-function or pathogenic IRD variants to curate a cohort with definite IRD-compatible genotypes. We defined three nested International Classification of Diseases (ICD)-9/10 code sets ("IRD," "retinopathy," and "screening") to derive lower- and upper-bound penetrance estimates via disease annotation frequencies (DAFs). Within a cohort of 481 AoU participants with definite IRD-compatible genotypes, DAFs ranged from 9.4% (IRD) to 28.1% (screening), which were enriched relative to the prevalence of the code sets in AoU (p < 0.001). For validation, we examined retinal imaging of UK Biobank (UKB) participants who shared variants with the AoU cohort. In the UKB, 16.1%-27.9% of participants with shared variants exhibited definite or possible IRD features, concordant with AoU estimates. Participant demographics, smoking, socioeconomic status, and comorbidities did not predict penetrance. These results show that the population penetrance of IRD-associated genotypes is markedly lower than traditionally assumed. This suggests that genetic or environmental modifiers are required to manifest disease and that IRD genotypes are more prevalent (0.7%-2.1%) than expected. These findings inform our understanding of the genetic causality of IRDs, impact the clinical use of genetic testing, and have implications for the development of therapies for IRDs.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".