Combined epigenetic/genetic study identified an ALS age of onset modifier
Bibliographic record
Abstract
-expansion in C9orf72). It might be influenced by environmental and genetic factors via the modulation of DNA methylation (DNAm) at CpG-sites. Hence, we combined an epigenetic and genetic approach to test the hypothesis that some common single nucleotide polymorphisms (SNPs) at CpG-sites (CpG-SNPs) could modify ALS age of onset. Our genome-wide DNAm analysis suggested three CpG-SNPs whose DNAm levels are significantly associated with age of onset in 249 ALS patients (q < 0.05). Next, genetic analysis validated the association of rs4970944 with age of onset in the discovery (n = 469; P = 0.025) and replication (n = 4160; P = 0.007) ALS cohorts. A meta-analysis of the cohorts combined showed that the median onset in AA-carriers is two years later than in GG-carriers (n = 4629; P = 0.0012). A similar association was observed with its tagging SNPs, implicating a 16 Kb region at the 1q21.3 locus as a modifier of ALS age of onset. Notably, rs4970944 genotypes are also associated with age of onset in C9orf72-carriers (n = 333; P = 0.025), suggesting that each A-allele delays onset by 1.6 years. Analysis of Genotype-Tissue Expression data revealed that the protective A-allele is linked with the reduced expression of CTSS in cerebellum (P = 0.00018), which is a critical brain region in the distributed neural circuits subserving motor control. CTSS encodes cathepsin S protein playing a key role in antigen presentation. In conclusion, we identified a 16 Kb locus tagged by rs4970944 as a modifier of ALS age of onset. Our findings support the role of antigen presenting processes in modulating age of onset of ALS and suggest potential drug targets (eg, CTSS). Future replication studies are encouraged to validate the link between the locus tagged by rs4970944 and age of onset in independent ALS cohorts, including different ethnic groups.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".