Performance of an allele‐level multi‐locus HLA genotype imputation tool in hematopoietic stem cell donors from Quebec
Bibliographic record
Abstract
INTRODUCTION: Donor-recipient HLA compatibility is an important determinant of transplant outcomes. Allele-group to allele-level imputations help assign HLA genotypes when allele-level genotypes are not available during donor selection. METHODS: We evaluated the performance of HaploStats, an allele-level multi-locus HLA genotype imputation tool from the National Marrow Donor Program, in a cross-sectional study including hematopoietic stem cell donors (HSCD) from Quebec, Canada. A total of 144 self-identified Caucasian HSCD genotyped at the allele-group and allele-level for HLA-A, -B, -C, -DRB1, and -DQB1 loci were studied. We compared allele-level genotypes imputed by HaploStats to those obtained by the reference standard, sequenced-based typing (SBT). RESULTS: Imputation performance, determined by allele-level genotype recall (fraction of matching imputed and sequenced genotypes) was 97%, 96%, 95%, 84%, and 81% for HLA-A, -B, -C, -DRB1, and -DQB1 loci, respectively. Our sample deviated from Hardy-Weinberg equilibrium only at the HLA-DRB1 locus. Residual ambiguity, determined by typing resolution scores (TRS), was greatest for HLA class II loci (average TRS 0.65 and 0.80 for DRB1 and DQB1, respectively). In contrast, average TRS of 0.88, 0.84, and 0.92 was observed for HLA-A, -B, and -C, respectively. CONCLUSIONS: HLA allele imputation from ambiguous genotypes demonstrate satisfactory prediction accuracy for HLA class I but modest prediction accuracy for HLA class II loci in self-identified Caucasian HSCD from Quebec. While consideration of high-resolution allele and haplotype frequencies in the Quebec population may improve the performance of available allele-level multi-locus genotype imputation tools in Quebec, this study suggests that genotyping at the first two field level should be conducted whenever possible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".