Bibliographic record
Abstract
Age-related macular degeneration (AMD) is a progressive retinal disease and a leading cause of irreversible blindness in older adults, imposing a substantial global health burden. Genome-wide association studies (GWAS) have identified over 50 loci associated with AMD, yet most lie in non-coding regions, obscuring the underlying molecular mechanisms. This paper highlights the application of multiomics integration—a framework that combines genetic associations with regulatory and functional outcomes across molecular layers—to elucidate the genetic risk of AMD, with a particular focus on the CFH gene, a central player in AMD pathogenesis. This study first summarizes key omics data types (genomics, transcriptomics, epigenomics) and core analytical tools, such as colocalization analysis and expression quantitative trait loci (eQTL) mapping. This then examines CFH-centered multi-omics findings, including evidence of shared causal variants between AMD GWAS and CFH eQTLs, and tissue-specific regulatory effects observed in the liver and retinal pigment epithelium. The discussion further extends to other AMD loci (e.g., ARMS2/HTRA1, C3) and outlines technical challenges such as data heterogeneity, tissue accessibility and translational potential. Overall, despite existing complexities, multi-omics integration provides an essential bridge between genetic associations and disease mechanisms, offering a roadmap for improved risk prediction and the development of mechanism-driven, biomarker-based therapeutics for AMD.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".