Inaccuracies in epitope repertoire estimations when using multilocus allele‐level HLA genotype imputation tools
Bibliographic record
Abstract
BACKGROUND: Limited availability of allele-level HLA genotypes prompts their imputation from allele-group genotypes to estimate epitope mismatches. We evaluated the accuracy of epitope load and repertoire assignment when imputing allele-level HLA genotypes. METHODS: Analyses were conducted on 175 hematopoietic stem cell (HSC) donors from the Héma-Québec registry (HQR) and 57 HSC donor-recipient pairs from McGill University Health Centre (MUHC), Québec, Canada, genotyped for HLA-A, -B, -C, -DRB1 and -DQB1. Multi-locus allele-level imputation was performed using HaploStats. Disagreement in B- and T-cell epitope assignment and epitope mismatches were ascertained for imputed vs. measured allele-level HLA genotypes in HSC donors and donor-recipient pairs, respectively. RESULTS: Imputation resulted in no differences in overall eplet mismatches and PIRCHE-II for HLA-A, -B, and -C in 83.4% and 93.7% of HQR donors and 87.7% and 87.7% of MUHC donors, respectively. HLA-DRB1- and -DQB1-derived eplet mismatches and PIRCHE-II were correctly assigned in 72.0% and 85.1% of HQR donors and 70.2% and 71.9% of MUHC donors, respectively. No discrepancies in eplet load or PIRCHE-II were observed in 96.5% and 86.0% of HSC donor-recipient pairs and in 70.2% and 70.1% of pairs for HLA-A, -B and -C and -DRB1 and -DQB1, respectively. Kappa statistics of 0.9708 and 0.9725, 0.8724 and 0.8177, 0.9827 and 0.9022, 0.5644 and 0.4939, 0.5085 and 0.6361 were demonstrated when assessing agreement between eplet mismatches and PIRCHE-II of imputed vs. measured HLA-A, -B, -C, -DRB1 and -DQB1 types, respectively. CONCLUSIONS: To avoid inaccuracies in epitope compatibility estimation, mainly for class II HLA, multi-locus allele-level genotype measurement is recommended. This article is protected by copyright. All rights reserved.
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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 | 0.046 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".