Analysis of six SNPs of NAT2 in Ngawbe and Embera Amerindians of Panama and determination of the Embera acetylation phenotype using caffeine
Bibliographic record
Abstract
Six NAT2 single-nucleotide polymorphisms (SNPs) were analysed in 105 unrelated Ngawbe and 136 unrelated Embera Amerindians (482 chromosomes) by SNP-specific polymerase chain reaction analysis. 282C>T was the most common synonymous mutation, while 857G>A was the most frequent nonsynonymous inactivating exchange. The allelic frequency of the NAT2*5 series (containing the 341T>C exchange) was 2.4% and 9.9% for Ngawbe and Embera, respectively, five- to 20-times lower than that in Caucasians. The NAT2*6 series (590G>A) showed allelic frequencies of 0% and 3.7%, eight- to 30-times lower than in Caucasians. On the other hand, the NAT2*7 series, characterized by mutation 857G>A, had allelic frequencies (23.3% and 22.8%) that were 10-20-times higher in Amerindians than in Caucasians. Amerindians are characterized by decreased genetic diversity because they display a low number of mutated alleles (four and five for Ngawbe and Embera, respectively) that are present at low proportions (27.6% and 39%), reduced genotypic variability (seven out of 15 and 12 out of 21 possible genotypes) and low heterozygosity (40% and 55.1%) at the NAT2 locus. The NAT2 phenotype was evaluated with caffeine in a subset of 72 Embera. There were no disagreements between genotype and phenotype among rapid and slow acetylators (13/72, 18%). We conclude that, in the Embera, the analysis of three inactivating mutations was sufficient in predicting the phenotype in more than 99.5% of these subjects. NAT2 would appear to be of a selectively neutral character given that there is no evidence of adaptation to the prevailing ecology in Amerindians.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".