Interaction of HLA-DRB1*1501 and TNF-Alpha in a Population-based Case-control Study of Multiple Sclerosis
Bibliographic record
Abstract
This study was conducted to determine whether single nucleotide polymorphisms (SNPs) in nine genes (human leukocyte antigen (HLA), T cell receptor beta (TCA receptor β), tumor necrosis factor α (TNF α), tumor necrosis factor β (TNF β), apolipoprotein E (APOE), interleukin 7 receptor alpha chain (IL7RA) interleukin 2 receptor alpha chain (IL2RA) myelin basic protein (MBP) and vitamin D receptor (VDR)) associated with multiple sclerosis (MS) could be replicated in a population-based sample, and to determine if these associations are modified by presence of HLA DRB1*1501. DNA was available from 722 individuals (223 with MS and 499 controls) who participated in a population-based case-control study. Cases and controls were matched on ancestry, age, gender and geographic area. HLA DRB1*1501 risk allele (T) was confirmed in this population using a genotypic test, controlling for multiple comparisons. Examining the effect of each SNP in the presence or absence of the HLA DRB1*1501 risk allele identified significant associations with TNF α-1031 (rs1799964) among those without the HLA risk allele. No additional interactions were significant in a cases-only analysis. Our results indicate that an interaction between SNPs in TNF α and HLA DRB1*1501 may influence the risk of developing MS.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".