Common variation in the miR-659 binding-site of GRN is a major risk factor for TDP43-positive frontotemporal dementia
Bibliographic record
Abstract
Loss-of-function mutations in progranulin (GRN) cause ubiquitin- and TAR DNA-binding protein 43 (TDP-43)-positive frontotemporal dementia (FTLD-U), a progressive neurodegenerative disease affecting approximately 10% of early-onset dementia patients. Here we expand the role of GRN in FTLD-U and demonstrate that a common genetic variant (rs5848), located in the 3'-untranslated region (UTR) of GRN in a binding-site for miR-659, is a major susceptibility factor for FTLD-U. In a series of pathologically confirmed FTLD-U patients without GRN mutations, we show that carriers homozygous for the T-allele of rs5848 have a 3.2-fold increased risk to develop FTLD-U compared with homozygous C-allele carriers (95% CI: 1.50-6.73). We further demonstrate that miR-659 can regulate GRN expression in vitro, with miR-659 binding more efficiently to the high risk T-allele of rs5848 resulting in augmented translational inhibition of GRN. A significant reduction in GRN protein was observed in homozygous T-allele carriers in vivo, through biochemical and immunohistochemical methods, mimicking the effect of heterozygous loss-of-function GRN mutations. In support of these findings, the neuropathology of homozygous rs5848 T-allele carriers frequently resembled the pathological FTLD-U subtype of GRN mutation carriers. We suggest that the expression of GRN is regulated by miRNAs and that common genetic variability in a miRNA binding-site can significantly increase the risk for FTLD-U. Translational regulation by miRNAs may represent a common mechanism underlying complex neurodegenerative disorders.
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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) | 0.001 | 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".