Genetic Distribution of the <i>LTA</i> +252 A>G and <i>TNFA</i> −308 G > A Polymorphisms in the Moroccan Population
Bibliographic record
Abstract
Introduction: The LTA and TNFA genes encode key proinflammatory cytokines with diverse activities in the immune responses. Single nucleotide polymorphisms (SNPs) in the LTA rs909253 (+252 A > G) and TNFA rs1800629 (−308 G > A) genes have been associated with susceptibility to many complex diseases. The aim of this study was to assess the frequency for these two key polymorphisms in the Moroccan population. Materials and Methods: A total of 338 unrelated healthy Moroccan subjects were genotyped for the two alleles using a restriction fragment length polymorphism–polymerase chain reaction method. Results: The LTA (+252 A > G) and TNFA (−308 G > A) were the most common alleles with 67.9% and 74.8% frequencies, respectively. In addition to the linkage disequilibrium between the two SNPs, significant differences in allele frequencies were observed in Moroccan population compared with Mediterraneans, Europeans, Africans, South Americans, and Asians (p < 0.05). Finally, genetic proximities between Moroccan, European, and West African populations were found by means of the principal component analysis. Conclusion: The LTA +252 A>G and TNFA −308 G > A polymorphisms among Moroccan population follow the patterns commonly encountered in other Mediterranean, European, and African populations. The result of this study could contribute in developing a genetic database on the healthy Moroccan population.
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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 | 0.000 | 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) | 0.002 | 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".