Array Genotyping of Transfusion Relevant Blood Cell Antigens in 6946 Ancestrally Diverse Subjects
Bibliographic record
Abstract
Blood transfusions save millions of lives worldwide each year, yet formation of antibodies against non-self antigens remains a significant problem, particularly in frequently transfused patients. We designed and tested the Universal Blood Donor Typing (UBDT_PC1) array for automated high-throughput simultaneous typing of human erythroid, platelet, leukocyte, and neutrophil antigens (HEA, HPA, HLA, and HNA, respectively) to support selection of blood products matched beyond ABO/Rh. Typing samples from 6946 donors of European, African, Admixed American, South Asian, and East Asian ancestry at two different laboratories showed a genotype reproducibility of ≥99% for 17 244 variants, translating to 99.98%, 99.90%, and 99.93% concordance across 338 372 HEA, 53 270 HPA, and 107 094 HLA genotypes, respectively. Compared to previous clinical typing data, concordance was 99.9% and 99.6% for 245 874 HEA and 3726 HPA comparisons, respectively. HLA types were 99.1% concordant with clinical typing across 8130 comparisons, with imputation accuracy higher in Europeans versus non-Europeans. Seven variant RHD alleles, a GYPB deletion underlying the U− phenotype, and 14 high-frequency antigen negative types were also detected. Beyond blood typing, hereditary hemochromatosis-associated HFE variants were identified in 276 donors. We found that the UBDT_PC1 array can reliably type a wide range of blood cell antigens across diverse ancestries. Reproducibility and accuracy were retained when transfusion-relevant targets from the UBDT_PC1 array were incorporated into the UKBB_v2.2 genome-wide typing array. The results represent the potential for significant advancement towards improved patient care by reducing harm in transfused patients through extended matching.
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 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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".