HLA‐A and HLA‐B in Kenya, Africa: Allele frequencies and identification of HLA‐B*1567 and HLA‐B*4426
Bibliographic record
Abstract
HLA-A and HLA-B alleles of a population from Kenya, Africa were examined by sequencing exon 2 and exon 3 DNA and typing using a Taxonomy-based Sequence-analysis (TBSA) method. Extensive diversities were observed at both HLA-A and HLA-B loci in this population. Forty-one HLA-A alleles were identified from 159 unrelated individuals. The most frequently observed alleles were A*6802 (11.64%), A*02011/09 (9.75%), A*7401/02 (9.43%), A*3001 (7.86%), A*3002 (7.23%) and A*3601 (6.6%). Forty-nine HLA-B alleles were identified in 161 unrelated individuals, including two novel alleles, B*1567 and B*4426. The most frequently observed HLA-B alleles were B*5301 (9.01%), B*5801 (8.38%), B*4201 (7.76%), B*1503 (7.14%), B*1801 (6.21%), and B*5802 (5.90%). The most frequently observed HLA-A-B haplotypes were A*3601-B*5301 (3.55%) and A*3001-B*4201 (3.19%), followed by A*7401/02-B*5801 (2.84%), A*7401/02-B*5802 (2.84%) and A*02011/09-B*1503 (2.13%). Linkage disequilibrium and chi2 analysis showed the association of these HLA-A-B haplotypes at the antigen level to be significant. The frequencies of HLA-A and HLA-B alleles from the Kenyan population were compared with that of a population from Cameroon. The difference in allele and haplotype frequency distributions partly reflected the different ethnic composition of these two African populations.
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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 | 0.001 |
| 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) | 0.002 | 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".