Academic Journal
GRIMM-II: A two-stage real-time algorithm for nine-locus HLA imputation and matching with up to three mismatches.
| Τίτλος: | GRIMM-II: A two-stage real-time algorithm for nine-locus HLA imputation and matching with up to three mismatches. |
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| Συγγραφείς: | Kirshenboim O; Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel., Kabya A; Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel., Yehezkel-Imra R; Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel., Tshuva Y; Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel., Maiers M; CIBMTR (Center for Blood and Marrow Transplant Research), NMDP, Minneapolis, MN, USA., Gragert L; Tulane University, LA, USA., Bashyal P; Tulane University, LA, USA., Israeli S; Adult Bone Marrow Transplant Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA. Electronic address: sapir9003@gmail.com., Louzoun Y; Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel. Electronic address: louzouy@math.biu.ac.il. |
| Πηγή: | Human immunology [Hum Immunol] 2026 Oct; Vol. 87 (10), pp. 111810. Date of Electronic Publication: 2026 Aug 09. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier/North-Holland Country of Publication: United States NLM ID: 8010936 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-1166 (Electronic) Linking ISSN: 01988859 NLM ISO Abbreviation: Hum Immunol Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: [New York] Elsevier/North-Holland. |
| Ιατρικοί όροι (MeSH): | Histocompatibility Testing*/methods , HLA Antigens*/genetics , HLA Antigens*/immunology , Algorithms* , Hematopoietic Stem Cell Transplantation*, Histocompatibility/genetics ; Humans ; Genotype ; Software ; Alleles ; Tissue Donors |
| Περίληψη: | Background: The success of hematopoietic stem cell transplantation (HSCT) depends critically on human leukocyte antigen (HLA) matching between donor and recipient. While traditional matching focuses on five classical HLA loci (A, B, C, DRB1, DQB1), clinical practice increasingly considers extended typing at nine loci, including DPA1, DQA1, DPB1, and DRB3/4/5. Furthermore, emerging evidence supports transplantation with up to three HLA mismatches under post-transplant cyclophosphamide (PTCy) regimens. However, current donor search algorithms cannot efficiently identify donors with multiple mismatches across extended HLA loci in real-time. Methods: We developed GRIMM-II (GRaph IMputation and Matching, version II), which comprises two novel algorithms: ML-GRIM (Multi-Locus GRIM) for HLA imputation across multiple loci, and ML-GRMA (Multi-Locus GRMA) for real-time donor-patient matching with up to three mismatches. Both algorithms employ a two-stage approach that combines efficient candidate reduction through graph-theoretic frameworks with detailed genotype comparison. ML-GRIM partitions genotypes into class I (HLA-A, B, C) and class II (remaining loci) components, enabling memory-efficient storage and rapid candidate identification. ML-GRMA searches a pre-imputed donor graph composed of donor genotypes and their sub-components, then computes asymmetric graft-versus-host (GvH) and host-versus-graft (HvG) mismatch probabilities to provide clinically relevant compatibility assessments. Both imputation and matching tools are available as a web application at https://grimmard.math.biu.ac.il/ and through GitHub repositories at https://github.com/nmdp-bioinformatics/py-graph-imputation (imputation) and https://github.com/nmdp-bioinformatics/py-graph-match (matching). Results: We validated ML-GRMA and ML-GRIM using the WMDA3 (World Marrow Donor Association) validation dataset, successfully reproducing all previously reported matches while identifying numerous additional candidate donors not detected by previous algorithms. Further validation of ML-GRMA using 10,000 patients with artificially introduced mismatches (0-3 allele substitutions) demonstrated 100% sensitivity and specificity in identifying matching donors at expected mismatch levels. We validated ML-GRIM using simulated nine-locus typings derived from 8,078,224 US donors in the NMDP registry. The algorithm successfully imputed genotypes across variable numbers of typed loci while incorporating multi-ethnic haplotype frequencies. The algorithm achieved real-time performance with typical imputation times under one second and matching times of 1-13 s per patient for up to three mismatches, even when searching databases exceeding 8 million donors. Notably, ML-GRMA identified substantially more potentially suitable donors than traditional algorithms by accounting for the biological reality that GvH and HvG mismatches often differ, particularly for donors homozygous at specific loci. To evaluate ML-GRIM performance with low-resolution typing, we tested it on simulated 3-locus typings from the same population. The resulting imputation accuracy correlated with the mutual information between typed loci and complete genotypes. Conclusions: GRIMM-II provides a scalable, memory-efficient solution for nine-locus HLA imputation and real-time identification of donors with up to three mismatches. The graph-based framework supports dynamic registry updates and can readily accommodate additional HLA loci and matching criteria as clinical knowledge evolves. By expanding the pool of acceptable donors while maintaining computational efficiency, GRIMM-II addresses a critical need in contemporary transplantation practice, particularly for patients from underrepresented ethnic minorities who face lower probabilities of finding perfectly matched donors. (Copyright © 2026 The Authors. Published by Elsevier Inc. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Σχόλια: | Update of: bioRxiv. 2026 Mar 31:2026.03.28.715027. doi: 10.64898/2026.03.28.715027.. (PMID: 41959293) |
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| Grant Information: | P30 CA008748 United States CA NCI NIH HHS; R01 AI173095 United States AI NIAID NIH HHS; R01 DK139240 United States DK NIDDK NIH HHS |
| Contributed Indexing: | Keywords: GRIMM; Graph; Imputation; Matching |
| Substance Nomenclature: | 0 (HLA Antigens) |
| Entry Date(s): | Date Created: 20260809 Date Completed: 20260923 Latest Revision: 20261007 |
| Update Code: | 20261007 |
| PubMed Central ID: | PMC13504538 |
| DOI: | 10.1016/j.humimm.2026.111810 |
| PMID: | 42571736 |
| Βάση Δεδομένων: | MEDLINE |
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