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.

同种异体造血细胞移植（HCT）是一种血液疾病的治疗方法，通常需要人类白细胞抗原（HLA）匹配的供体。捐献者登记处利用不断发展的 HLA 基因分型方法技术招募捐献者。这就需要根据根据自我认定的种族和民族 (SIRE) 分层的捐赠者数据估计的单倍型频率进行计算机模拟模糊解决和统计插补。然而，SIRE 的遗传有效性有限，并且对 SIRE 未知或混合的个体提出了挑战。我们提出了 MR-GRIMM“多种族图估算和匹配”，它使用基于 SIRE 的先验同时估算种族/民族类别和 HLA 基因型。此外，我们提出了一种新方法来估算与当前单倍型频率不一致的 HLA 分型。MR-GRIMM 的性能使用 170,000 个供体-受体对的数据集进行了验证。MR-GRIMM 的匹配误差 (1-AUC) 比单种族插补平均低 20%。通过将估算的供体种族与供体提供的 SIRE 进行比较，测量 HLA 种族/民族类别估算的召回指标（敏感性）。对 5 个广泛的美国人口群体和 21 个详细的美国人口群体的预测准确度分别为 0.74 和 0.55。该算法在注册表搜索中的操作实施可以帮助改进匹配预测和对 HLA 匹配捐赠者的访问。