Association Study between Candidate Genes and Obesity-Related Phenotypes Using a Sample of Lumberjacks
Bibliographic record
Abstract
INTRODUCTION: Complex traits such as obesity are modulated by genetic and environmental factors and lead to varied clinical presentations.The aim of this study was to investigate associations between candidate genes and obesity-related phenotypes using a sample of 252 lumberjacks issued from a founder population and sharing a common and circumscribed environment. METHODS: Thirty-seven variants in 18 genes were genotyped. The restriction fragment length polymorphism method and the template-directed dye-terminator incorporation assay with fluorescence polarization detection were employed for the genotyping assays. Multivariate logistic regression models were built in order to calculate the relative odds of exhibiting obesity-related phenotypes associated with the presence of the studied polymorphism. Among them, 21 single nucleotide polymorphisms were tested for associations with obesity phenotypes. RESULTS: Significant associations were found between carriers of the minor alleles of APOE-epsilon2, FABP2-A54T, UCP1-L229M, LPL-HindIII, LPL-S447X and LPL-T1973C, patients bearing a combination of LPL-D9N, LPL-N291S and LPL-P207L and obesity-related phenotypes. CONCLUSION: The present results suggest that a particular population such as lumberjacks, sharing the same environment, could help target genes involved in complex traits.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".