Genetic co-occurrence networks identify polymorphisms within ontologies highly associated with preeclampsia
Bibliographic record
Abstract
Abstract Polygenic diseases require the co-occurrence of multiple risk variants to initiate a pathology. An example is preeclampsia, a hypertensive disease of pregnancy with no known cure or therapy other than the often-preterm delivery of the neonate and placenta. Preeclampsia is challenging to predict due to symptomatic and outcome heterogeneity. Transcriptomic and genetic analysis suggests that this is a multi-syndromic and multigenic disease. Previous research applications of traditional GWAS methods to preeclampsia identified only a few alleles with marginal differences between cases and controls. We seek to identify genetic networks related to the pathophysiology of preeclampsia as potential avenues of therapeutic investigation and early genetic testing. We created a novel systems biology-based method that identifies networks of co-occurring SNPs associated with a trait or disease. The co-occurring pairs are assembled into higher-order associations using network graphs. We validated our method using simulation modelling and tested it against maternal genetic data of a previously assessed preeclampsia cohort. The genetic co-occurrence network identified SNPs in or near genes with ontological enrichment for VEGF, immunological and hormonal pathways, among others with known physiological disruption in preeclampsia. Our findings suggests that preeclampsia is caused by relatively common alleles (<5%) that accumulate in unfavorable combinations.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".