Contribution of Hierarchical Clustering Techniques to the Modeling of the Geographic Distribution of Genetic Polymorphisms Associated with Chronic Inflammatory Diseases in the Québec Population
Bibliographic record
Abstract
OBJECTIVES: The purpose of this project was to evaluate the potential of the downward hierarchical clustering analysis (DHCA) for studying genetic heterogeneity, i.e. differences in allele frequency in subpopulations, such as the 15 public health regions of the province of Québec (Canada). METHODS: The study relied on an anonymized sample of 1,680 individuals who had participated in the Québec Heart Health Survey in 1990-1991. The genotyping of 11 variants in 8 candidate genes known to be involved in chronic inflammatory diseases, namely asthma and cardiovascular diseases, was performed using the amplification refractory mutation system and restriction fragment length polymorphism techniques. Only variants showing an allelic frequency >2% in the Québec Heart Health Survey (n = 8) were selected. DHCA techniques were then applied to model the geographical distribution of these 8 genetic variants in 15 Québec public health regions and to study genetic heterogeneity. RESULTS: The DHCA allowed to group public health regions and gene variants on the basis of genetic variability. For both asthma and cardiovascular diseases, 3 significant clusters of public health regions and 1 cluster of gene variants were identified. DISCUSSION: This study suggests that DHCA might be useful in studying genetic heterogeneity at the population level and for public health activities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".