Gene Interaction Network Architecture of Human Polygenic Traits Reveals Domain-Specific Connectivity and Evolutionary Pressures
Bibliographic record
Abstract
Abstract Human polygenic phenotypes arise from complex interactions among genes within regulatory networks. To gain insights into the structural and evolutionary characteristics of these networks, we analyzed gene interaction networks across 4756 human polygenic phenotypes, contrasting the network properties of genes associated with polygenic phenotypes against those of non-associated genes. Our results indicate that genes associated with polygenic phenotypes exhibit a significantly higher connectivity in their corresponding gene-interaction networks, with connectivity varying markedly across different trait domains. Notably, genes within highly connected networks are enriched for immune-related biological processes, whereas genes in less connected networks are more involved in neurogenesis. Furthermore, we found that genes embedded in highly connected networks are, on average, under weaker selective constraints than those in lowly connected networks. Overall, our findings provide a systematic analysis of gene interaction networks underlying human polygenic traits and reveal how selective constraints vary with network connectivity. These insights offer a framework for prioritizing genes based on their connectivity within trait-associated networks. To support this effort, we developed the online portal http://netpolygen.com enabling researchers to generate and explore hypotheses by identifying genes that are both highly associated and highly connected across thousands of polygenic phenotypes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".