Genetic modifiers in carriers of repeat expansions in the C9ORF72 gene
Bibliographic record
Abstract
BACKGROUND: Hexanucleotide repeat expansions in chromosome 9 open reading frame 72 (C9ORF72) are causative for frontotemporal dementia (FTD) and motor neuron disease (MND). Substantial phenotypic heterogeneity has been described in patients with these expansions. We set out to identify genetic modifiers of disease risk, age at onset, and survival after onset that may contribute to this clinical variability. RESULTS: We examined a cohort of 330 C9ORF72 expansion carriers and 374 controls. In these individuals, we assessed variants previously implicated in FTD and/or MND; 36 variants were included in our analysis. After adjustment for multiple testing, our analysis revealed three variants significantly associated with age at onset (rs7018487 [UBAP1; p-value = 0.003], rs6052771 [PRNP; p-value = 0.003], and rs7403881 [MT-Ie; p-value = 0.003]), and six variants significantly associated with survival after onset (rs5848 [GRN; p-value = 0.001], rs7403881 [MT-Ie; p-value = 0.001], rs13268953 [ELP3; p-value = 0.003], the epsilon 4 allele [APOE; p-value = 0.004], rs12608932 [UNC13A; p-value = 0.003], and rs1800435 [ALAD; p-value = 0.003]). CONCLUSIONS: Variants identified through this study were previously reported to be involved in FTD and/or MND, but we are the first to describe their effects as potential disease modifiers in the presence of a clear pathogenic mutation (i.e. C9ORF72 repeat expansion). Although validation of our findings is necessary, these variants highlight the importance of protein degradation, antioxidant defense and RNA-processing pathways, and additionally, they are promising targets for the development of therapeutic strategies and prognostic tests.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".