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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".