Protein interaction network analysis reveals genetic enrichment of immune system genes in frontotemporal dementia
Bibliographic record
Abstract
Interactors of protein products of known genes for frontotemporal dementia (FTD) are likely to be involved in the molecular pathways towards disease. We therefore applied protein interaction network (PIN) analysis to prioritize candidate genes for rare variant association analysis. We created an FTD-PIN starting from known FTD genes downloading their physical interactors and performed functional enrichment analyses. We identified overrepresented processes in FTD and selected genes (n = 440) belonging to these processes for rare variant analysis in a Belgian cohort of 228 FTD patients and 345 controls. SKAT-O analysis suggested TNFAIP3 as the top gene (p = 0.7 × 10−3) reaching near test-wide significance (p = 2.5 × 10−4). We then analyzed the TNFAIP3-subnetwork within the FTD-PIN which indicated enrichment of several immune signaling networks, suggesting that disrupted immune signaling may be implicated in TNFAIP3-related FTD. Our study demonstrates that integration of PINs with genetic data is a useful approach to increase the power for rare variant association analysis. Furthermore, we present a computational pipeline for identifying potential novel therapeutic targets and risk-modifying variants.
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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 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".