Diversity and Frequencies of HLA Class I and Class II Genes of an East African Population
Bibliographic record
Abstract
Human Leukocyte Antigens (HLAs) play an important role in host immune responses to infectious pathogens, and influence organ transplantation, cancer and autoimmune diseases. In this study we conducted a high resolution, sequence-based genotyping of HLA class I and class II genes of more than 2000 women from Kenya, eastern Tanzania and southern Uganda around Lake Victoria and analyzed their allele, phenotype and haplotype frequencies. A considerable genetic diversity was observed at both class I and II loci. A total of 79 HLA-A, 113 HLA-B, 53 HLA-C, 25 HLA-DPA1, 60 HLA-DPB1, 15 HLA-DQA1, 44 HLA-DQB1 and 38 HLA-DRB1 alleles have been identified. The most common class I alleles were A * 02:01:01 (10.90%), B * 58:02 (8.79%), and C * 06:02:01 (16.98%). The most common class II alleles were DPA1*01:03:01 (40.60%), DPB1 * 01:01:01 (23.45%), DQA1 * 01:02:01 (31.03%), DQB1 * 03:01:01 (21.79%), DRB1 * 11:01:02 (11.65%), DRB3 * 02:02:01 (31.65%), DRB4 * 01:01:01 (10.50%), and DRB5 * 01:01:01 (10.50%). Higher than expected homozygosity was observed at HLA-B (P = 0.022), DQA1 (P = 0.004), DQB1 (P = 0.023), and DRB1 (P = 0.0006) loci. The allele frequency distribution of this population is very similar to the ones observed in other sub-Saharan populations with the exception of lower frequencies of A * 23 (5.55% versus 11.21%) and DQA1 * 03 (4.79% versus 11.72%), and higher frequencies of DPB1 * 30 (2.26% versus 0.37%) and DRB1 * 11 (21.51% versus 15.89%). The knowledge of the diversity and allele/ phenotype frequencies of the HLA alleles of this east African population, can contribute to the understanding of how host genetic factors influence disease susceptibility and effective anti-retroviral treatment of HIV infections and future vaccine trials.
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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 | 0.000 | 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".