Applying Polygenic Models to Disentangle Genotype-phenotype Associations across Common Human Diseases
Bibliographic record
Abstract
In recent years, much human genetics research has focused on modeling the polygenic architecture of common human diseases using summary statistics from genome-wide association studies (GWAS). Utilizing longitudinal population-level data from participants in the UK Biobank (UKB) and the Canadian Partnership for Tomorrow’s Health (CanPath), I investigate how innovations in polygenic risk modelling may be applied to help disentangle genotype-phenotype associations for common human diseases represented across both these populations. By performing stratified GWAS, I capture the shared and distinct polygenic architecture of disease subgroups even among cases of a single ancestry. Subsequently, I compare the efficacy of polygenic risk modeling against more dynamic approaches such as transcriptional risk models which rely on gene expression profiles from tissue and may synergistically capture both individual-level genetic and environmental risk factors. Together, these findings present genetic risk factors for early-onset disease and provide insights into biomarkers for improving personalized disease risk prediction.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".