In-Depth Analysis of Genetic Variation Associated with Severe West Nile Viral Disease
Bibliographic record
Abstract
West Nile virus (WNV) is a mosquito-borne virus which causes symptomatic disease in a minority of infected humans. To identify novel genetic variants associated with severe disease, we utilized data from an existing case-control study of WNV and included population controls for an expanded analysis. We conducted imputation and gene-gene interaction analysis in the largest and most comprehensive genetic study conducted to date for West Nile neuroinvasive disease (WNND). Within the imputed West Nile virus dataset (severe cases n = 381 and asymptomatic/mild controls = 441), we found novel loci within the MCF.2 Cell Line Derived Transforming Sequence Like (MCF2L) gene (rs9549655 and rs2297192) through the individual loci analyses, although none reached statistical significance. Incorporating population controls from the Wisconsin Longitudinal Study on Aging (n = 9012) did not identify additional novel variants, a possible reflection of the cohort’s inclusion of individuals who could develop mild or severe WNV disease upon infection. Many of the top gene-gene interaction results were intergenic, with currently undefined biological roles, highlighting the need for further investigation into these regions and other identified gene targets in severe WNND. Further studies including larger sample sizes and more diverse populations reflective of those at risk are needed to fully understand the genetic architecture of severe WNDD and provide guidance on viable targets for therapeutic and vaccine development.
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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) | 0.000 | 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 | 0.001 |
| 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".