Differential DNA methylation identified in the blood and retina of AMD patients
Bibliographic record
Abstract
Age-related macular degeneration (AMD) is a major cause of blindness in the western world. While genetic studies have linked both common and rare variants in genes involved in regulation of the complement system to increased risk of development of AMD, environmental factors, such as smoking and nutrition, can also significantly affect the risk of developing the disease and the rate of disease progression. Since epigenetics has been implicated in mediating, in part, the disease risk associated with some environmental factors, we investigated a possible epigenetic contribution to AMD. We performed genome-wide DNA methylation profiling of blood from AMD patients and controls. No differential methylation site reached genome-wide significance; however, when epigenetic changes in and around known GWAS-defined AMD risk loci were explored, we found small but significant DNA methylation differences in the blood of neovascular AMD patients near age-related maculopathy susceptibility 2 (ARMS2), a top-ranked GWAS locus preferentially associated with neovascular AMD. The methylation level of one of the CpG sites significantly correlated with the genotype of the risk SNP rs10490924, suggesting a possible epigenetic mechanism of risk. Integrating genome-wide DNA methylation analysis of retina samples with and without AMD together with blood samples, we further identified a consistent, replicable change in DNA methylation in the promoter region of protease serine 50 (PRSS50). These methylation changes may identify sites in novel genes that are susceptible to non-genetic factors known to contribute to AMD development and progression.
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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) | 0.000 | 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".