Additional file 1 of Combined epigenetic/genetic study identified an ALS age of onset modifier
Bibliographic record
Abstract
Additional file 1:Table S1. Candidate CpG-SNPs with significant association between their DNAm level and age of onset in 249 ALS patients. Table S2. Results of the subgroup analysis in Canadian ALS patients (n=469) and US ALS patients (n=4160). Table S3. Bioinformatic annotation of SNPs in strong LD with rs4970944 (R2>0.9). Table S4. eQTL analysis using the GTEx database revealed significant changes in gene expression associated with rs4970944 in different tissues (normalized effect size (NES) are listed). Fig. S1. QQ plot of the genome-wide DNAm study. Fig. S2. The 16 Kb LD-block tagged by rs4970944 (chr1:151150857–151166896), including 4 SNPs (rs11204785, rs11807075, rs11299974, rs10888406) in strong LD with rs4970944 (R2>0.9). Fig. S3. Rs10888406 genotypes are significantly associated with age of onset in ALS patients in the discovery, replication and pooled sample set. Fig. S4. Subgroup analysis in Canadian ALS patients stratified for site of onset, sex and familial history. Fig. S5. Subgroup analysis in US ALS patients stratified for site of onset, sex and familial history. Fig. S6. Subgroup analysis in US ALS patients stratified for C9orf72 status. Fig. S7. Pooled analysis of the association between rs4970944 genotypes and ALS age of onset. Fig. S8. Meta-analysis of the adjusted regression coefficient from the discovery cohort (n=469) and the replication cohort (n=3697) in C9orf72 negative ALS patients. Fig. S9. Rs4970944 genotypes are significantly associated with CTSS expression in cerebellum in the GTEx database. Fig. S10. The dimension reduction figure (UMAP) of human entorhinal cortex samples. Fig. S11. Visualization of CTSS and selected genes in the dimension reduction figure (UMAP). Supplementary acknowledgements. Acknowledgements for using the dbGap dataset.
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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 | 0.002 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.854 | 0.092 |
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".