Combined imputation of HLA genotype and self-identified race leads to better donor-recipient matching

Sapir Israeli; Loren Gragert; Abeer Madbouly; Pradeep Bashyal; Joel Schneider; Martin Maiers; Yoram Louzoun

doi:10.1016/j.humimm.2023.110721

Combined imputation of HLA genotype and self-identified race leads to better donor-recipient matching

Sapir Israeli, Loren Gragert, Abeer Madbouly, Pradeep Bashyal, Joel Schneider, Martin Maiers, Yoram Louzoun

Department of Mathematics

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

Allogeneic Hematopoietic Cell Transplantation (HCT) is a curative therapy for hematologic disorders and often requires human leukocyte antigen (HLA)-matched donors. Donor registries have recruited donors utilizing evolving technologies of HLA genotyping methods. This necessitates in-silico ambiguity resolution and statistical imputation based on haplotype frequencies estimated from donor data stratified by self-identified race and ethnicity (SIRE). However, SIRE has limited genetic validity and presents a challenge for individuals with unknown or mixed SIRE. We present MR-GRIMM “Multi-Race Graph IMputation and Matching” that simultaneously imputes the race/ethnic category and HLA genotype using a SIRE based prior. Additionally, we propose a novel method to impute HLA typing inconsistent with current haplotype frequencies. The performance of MR-GRIMM was validated using a dataset of 170,000 donor-recipient pairs. MR-GRIMM has an average 20 % lower matching error (1-AUC) than single-race imputation. The recall metric (sensitivity) of the race/ethnic category imputation from HLA was measured by comparing the imputed donor race with the donor-provided SIRE. Accuracies of 0.74 and 0.55 were obtained for the prediction of 5 broad and 21 detailed US population groups respectively. The operational implementation of this algorithm in a registry search could help improve match predictions and access to HLA-matched donors.

Original language	English
Article number	110721
Journal	Human Immunology
Volume	84
Issue number	12
DOIs	https://doi.org/10.1016/j.humimm.2023.110721
State	Published - Dec 2023

Bibliographical note

Publisher Copyright:
© 2023 The Author(s)

Funding

The work of YL and SI was supported by ISF grant number 870/20 . The work of SI was supported by the BIU president scholarship. The work of LG was supported by NIAID [1U01AI152960 and 1R01AI173095] . This research was supported through a series of research grants funded by the US Office of Naval Research (N00014-20-1-2832, N00014-20-1-2705, N00014-19-1-2888, N00014-18-1-2045, N00014-17-1-2388). We thank Craig Malmberg, Kim Wadsworth, and Jane Kempenich from the NMDP for input into the approach for assessing and comparing algorithm performance. The work of YL and SI was supported by ISF grant number 870/20. The work of SI was supported by the BIU president scholarship. The work of LG was supported by NIAID [1U01AI152960 and 1R01AI173095]. This research was supported through a series of research grants funded by the US Office of Naval Research ( N00014-20-1-2832, N00014-20-1-2705, N00014-19-1-2888, N00014-18-1-2045, N00014-17-1-2388 ). We thank Craig Malmberg, Kim Wadsworth, and Jane Kempenich from the NMDP for input into the approach for assessing and comparing algorithm performance.

Funders	Funder number
Office of Naval Research	N00014-20-1-2832, N00014-19-1-2888, N00014-18-1-2045, N00014-20-1-2705, N00014-17-1-2388
National Institute of Allergy and Infectious Diseases	1U01AI152960, 1R01AI173095
National Marrow Donor Program/Be The Match
Israel Science Foundation	870/20

Keywords

EM
Ethnic groups
HLA
HSCT
Imputation

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10.1016/j.humimm.2023.110721

Cite this

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abstract = "Allogeneic Hematopoietic Cell Transplantation (HCT) is a curative therapy for hematologic disorders and often requires human leukocyte antigen (HLA)-matched donors. Donor registries have recruited donors utilizing evolving technologies of HLA genotyping methods. This necessitates in-silico ambiguity resolution and statistical imputation based on haplotype frequencies estimated from donor data stratified by self-identified race and ethnicity (SIRE). However, SIRE has limited genetic validity and presents a challenge for individuals with unknown or mixed SIRE. We present MR-GRIMM “Multi-Race Graph IMputation and Matching” that simultaneously imputes the race/ethnic category and HLA genotype using a SIRE based prior. Additionally, we propose a novel method to impute HLA typing inconsistent with current haplotype frequencies. The performance of MR-GRIMM was validated using a dataset of 170,000 donor-recipient pairs. MR-GRIMM has an average 20 % lower matching error (1-AUC) than single-race imputation. The recall metric (sensitivity) of the race/ethnic category imputation from HLA was measured by comparing the imputed donor race with the donor-provided SIRE. Accuracies of 0.74 and 0.55 were obtained for the prediction of 5 broad and 21 detailed US population groups respectively. The operational implementation of this algorithm in a registry search could help improve match predictions and access to HLA-matched donors.",

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Combined imputation of HLA genotype and self-identified race leads to better donor-recipient matching

Abstract

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Keywords

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