Analysis of genes, pathways and networks involved in disease severity and age at onset in primary-progressive multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: The role of genetic factors in influencing the clinical expression of multiple sclerosis (MS) is unclear. OBJECTIVE: The objective of this paper is to identify genes, pathways and networks implicated in age at onset (AAO) and severity, measured using the Multiple Sclerosis Severity Score (MSSS), of primary-progressive MS (PPMS). METHODS: We conducted a genome-wide association study (GWAS) of 470 PPMS patients of Italian origin:. Allelic association of 296,589 SNPs with AAO and MSSS was calculated. Pathway and network analyses were also conducted using different tools. RESULTS: No single association signal exceeded genome-wide significance in AAO and MSSS analyses. Nominally associated genes to AAO and MSSS were enriched in both traits for 10 pathways, including: "oxidative phosphorylation" (FDRAAO=9*10(-4); FDRMSSS=3.0*10(-2)), "citrate (TCA) cycle" (FDRAAO=1.6*10(-2); FDRMSSS=3.2*10(-3)), and "B cell receptor signaling" (FDRAAO=3.1*10(-2); FDRMSSS=2.2*10(-3)). In addition, an enrichment of "chemokine signaling pathway" (FDR=9*10(-4)) for AAO and of "leukocyte transendothelial migration" (FDR=2.4*10(-3)) for MSSS trait was observed, among others. Network analysis revealed that p53 and CREB1 were central hubs for AAO and MSSS traits, respectively. CONCLUSIONS: Despite the fact that no major effect signals emerged in the present GWAS, our data suggest that genetic variants acting in the context of oxidative stress and immune dysfunction could modulate the onset and severity of PPMS.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".