Copy Number Variants in miR-138 as a Potential Risk Factor for Early-Onset Alzheimer’s Disease
Bibliographic record
Abstract
Early-onset Alzheimer's disease (EOAD) accounts for 5-10% of all AD cases, with a heritability ranging between 92% to 100%. With the exception of rare mutations in APP, PSEN1, and PSEN2 genes causing autosomal dominant EOAD, little is known about the genetic factors underlying most of the EOAD cases. In this study, we hypothesized that copy number variations (CNVs) in microRNA (miR) genes could contribute to risk for EOAD. miRs are short non-coding RNAs previously implicated in the regulation of AD-related genes and phenotypes. Using whole exome sequencing, we screened a series of 546 EOAD patients negative for autosomal dominant EOAD mutations and 597 controls. We identified 86 CNVs in miR genes of which 31 were exclusive to EOAD cases, including a duplication of the MIR138-2 locus. In functional studies in human cultured cells, we could demonstrate that miR-138 overexpression leads to higher Aβ production as well as tau phosphorylation, both implicated in AD pathophysiology. These changes were mediated in part by GSK-3β and FERMT2, a potential risk factor for AD. Additional disease-related genes were also prone to miR-138 regulation including APP and BACE1. This study suggests that increased gene dosage of MIR138-2 could contribute to risk for EOAD by regulating different biological pathways implicated in amyloid and tau metabolism. Additional studies are now required to better understand the role of miR-CNVs in EOAD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".