miRNAs, single nucleotide polymorphisms (SNPs) and age-related macular degeneration (AMD)
Bibliographic record
Abstract
Advanced age-related macular degeneration (AAMD) is a complex sight-threating disease of public health significance. Micro RNAs (miRNAs) have been proposed as biomarkers for AAMD. The presence of certain single nucleotide polymorphisms (SNPs) may influence the explanatory value of these biomarkers. Here we present findings from an integrated approach used to determine whether AAMD-associated SNPs have the capacity to influence miRNA-mRNA pairing and, if so, to what extent such pairing may be manifested in a discrete AAMD transcriptome. Using a panel of 8854 SNPs associated with AAMD at p-values ≤5.0E-7 from a cohort of >30,000 elderly people, we identified SNPs in miRNA target-encoding constituents of: (1) regulator of complement activation (RCA) genes (rs390679, CFHR1, p≤2.14E-214 | rs12140421, CFHR3, p≤4.63E-29); (2) genes of major histocompatibility complex (MHC) loci (rs4151672, CFB, p≤8.91E-41 | rs115404146, HLA-C, p≤6.32E-12 | rs1055821, HLA-B, p≤1.93E-9 | rs1063355, HLA-DQB1, p≤6.82E-14); and (3) genes of the 10q26 AAMD locus (rs1045216, PLEKHA1, p≤4.17E-142 | rs2672603, ARMS2, p≤7.14E-46). We used these findings with existing data on AAMD-related retinal miRNA and transcript profiles for the purpose of making inferences on SNP-mRNA-miRNA-AAMD relationships. Four of 12 miRNAs significantly elevated in AAMD retina (hsa-miR-155-5p, hsa-let-7a-5p, hsa-let-7b-5p hsa-let-7d-5p) also showed strong pairing capacity (TarBase 7.1 context++ score <-0.2, miRanda 3.3 pairing score >150) with miRNA target transcripts encoded by AAMD-associated SNPs resident in HLA-DQB1 (rs1063355, hsa-miR-155-5p) and TGFBR1 (rs868, hsa-let-7). Three of the 12 miRNAs overexpressed in AAMD retina are inducible by NFkB and have high affinity targets in the complement factor H (CFH) mRNA 3' UTR. We used ENSEMBL to identify polymorphic regions in the CFH mRNA 3' UTR with the capacity to disrupt miRNA-mRNA pairing. Two variants (rs766666504 and rs459598) existed in DNA sequence encoding the seed region of hsa-miR-146a-5p in the CFH mRNA 3' UTR - as this miRNA is also elevated in both vitreous and serum of people with AAMD, it shows great value as a biomarker. Our findings suggest that knowledge on the nature of DNA sequence variation may increase the explanatory power of miRNA biomarkers in genetically diverse populations, while yielding information with which to develop: (1) mechanistic tests on processes implicated in AMD pathogenesis; and, (2) site-specific small molecules (synthetic mimetics or anti-miRNAs) with preventive or therapeutic efficacy for AAMD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".