Evidence map›Paper›PMID 40269614›Full record

ArticleHLA2025

Evaluating the Antigen and Eplet Accuracy of DQA1 Imputations With the HaploSFHI Two-Field HLA Typing Inference Tool.

Romain Lhotte, Cathi Murphey, Sandra Tafulo, David Turner, Cédric Usureau, Magali Devriese, SFHI Consortium, Véronique Letort, Jean-Luc Taupin

Abstract read
In one paragraph

Article in HLA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Romain LhotteImmunology and Histocompatibility Laboratory, Saint-Louis Hospital, Paris, France.ORCID 0000-0002-6131-5415
Cathi MurpheyHistocompatibility and Immunogenetics Laboratory Southwest Immunodiagnostics, Inc, San Antonio, Texas, USA.ORCID 0000-0002-2714-7729
Sandra TafuloCentro de Sangue e Da Transplantação Do Porto, Instituto Português Do Sangue e da Transplantação, Porto, Portugal.ORCID 0000-0002-3911-0245
David TurnerHistocompatibility and Immunogenetics Laboratory, Scottish National Blood Transfusion Service, Edinburgh, UK.
Cédric UsureauImmunology and Histocompatibility Laboratory, Saint-Louis Hospital, Paris, France.ORCID 0009-0002-4366-7211
Magali DevrieseImmunology and Histocompatibility Laboratory, Saint-Louis Hospital, Paris, France.ORCID 0000-0003-1198-4677
SFHI Consortium
Véronique LetortMICS-Research Laboratory in Mathematics and Computer Science at CentraleSupélec, Gif-Sur-Yvette, France.ORCID 0000-0002-0991-2351
Jean-Luc TaupinImmunology and Histocompatibility Laboratory, Saint-Louis Hospital, Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Donor/recipient mismatched HLA antigens can lead to the production of Donor-Specific Antibodies by the recipient, which are deleterious to organ transplants. The HLA-DQ locus is the most frequent target, with both the DQ beta and alpha chains involved. For deceased donors in particular, while HLA-DQB1 has been typed in emergencies for a long time, HLA-DQA1 has only recently been included. No imputation algorithmic tool was available to impute HLA-DQA1 until the development of HaploSFHI, trained on 61,393 two-field typings by NGS methods. We evaluated the accuracy of two-field HLA-DQA1 imputation from serological and two-field level HLA-A, B, DRB1, and DQB1 typings. We report a highly accurate two-field HLA-DQA1 prediction using a French test cohort of 7696 individuals, respectively reaching 92.30% and 96.45% accuracy. The average 'False Positive eplet load' stood at 0.19 and 0.07, respectively, and the average 'False Negative eplet load' at 0.18 and 0.08, respectively. A similar performance was obtained on three independent test cohorts of European ancestry (from the USA, the UK, and Portugal). Interestingly, performance was only slightly inferior on five independent test cohorts of other ethnicities (from Hong Kong and the USA) whereas it was significantly lower for two-field DRB1 imputation from its serological level. These results suggest that DQA1 can reliably be imputed even when information is totally missing, with low error risk at both antigen and eplet levels, even if the reference population is not matched. Similar additional initiatives would be welcome to confirm these findings.

Indexed as

Histocompatibility TestingHLA-DQ alpha-ChainsAlgorithmsAllelesHaplotypesHumansHLA-DQA1 antigenHLA-DQ alpha-ChainsDQA1EpletsHLAImputationMachine LearningTransplantation

Identifiers

PMID40269614
PMCPMC12018869

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.