Epitopes as characterized by antibody-verified eplet mismatches determine risk of kidney transplant loss.

To optimize strategies that mitigate the risk of graft loss associated with HLA incompatibility, we evaluated whether sequence defined HLA targets (eplets) that result in donor-specific antibodies are associated with transplant outcomes. To define this, we fit multivariable Cox proportional hazard models in a cohort of 118 382 United States first kidney transplant recipients to assess risk of death-censored graft failure by increments of ten antibody-verified eplet mismatches. To verify robustness of our findings, we conducted sensitivity analysis in this United States cohort and assessed the role of antibody-verified eplet mismatches as autonomous predictors of transplant glomerulopathy in an independent Canadian cohort. Antibody-verified eplet mismatches were found to be independent predictors of death-censored graft failure with hazard ratios of 1.231 [95% confidence interval 1.195, 1. 268], 1.268 [1.231, 1.305] and 1.411 [1.331, 1.495] for Class I (HLA-A, B, and C), -DRB1 and -DQB1 loci, respectively. To address linkage disequilibrium between HLA-DRB1 and -DQB1, we fit models in a subcohort without HLA-DQB1 eplet mismatches and found hazard ratios for death-censored graft failure of 1.384 [1.293, 1.480] for each additional antibody-verified HLA-DRB1 eplet mismatch. In a subcohort without HLA-DRB1 mismatches, the hazard ratio was 1.384 [1.072, 1.791] for each additional HLA-DQB1 mismatch. In the Canadian cohort, antibody-verified eplet mismatches were independent predictors of transplant glomerulopathy with hazard ratios of 5.511 [1.442, 21.080] for HLA-DRB1 and 3.640 [1.574, 8.416] for -DRB1/3/4/5. Thus, donor-recipient matching for specific HLA eplets appears to be a feasible and clinically justifiable strategy to mitigate risk of graft loss.

GRIMM: GRaph IMputation and matching for HLA genotypes.

MOTIVATION: For over 10 years allele-level HLA matching for bone marrow registries has been performed in a probabilistic context. HLA typing technologies provide ambiguous results in that they could not distinguish among all known HLA alleles equences; therefore registries have implemented matching algorithms that provide lists of donor and cord blood units ordered in terms of the likelihood of allele-level matching at specific HLA loci. With the growth of registry sizes, current match algorithm implementations are unable to provide match results in real time. RESULTS: We present here a novel computationally-efficient open source implementation of an HLA imputation and match algorithm using a graph database platform. Using graph traversal, the matching algorithm runtime is practically not affected by registry size. This implementation generates results that agree with consensus output on a publicly-available match algorithm cross-validation dataset. AVAILABILITY AND IMPLEMENTATION: The Python, Perl and Neo4j code is available at https://github.com/nmdp-bioinformatics/grimm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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

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.

Predicting HLA-DPB1 permissive probabilities through a DPB1 prediction service towards the optimization of HCT donor selection.

Despite its demonstrated importance in hematopoietic cell transplantation, the HLA-DPB1 locus is only typed in one in five unrelated donors in the United States. Addressing this issue, we developed a DPB1 Prediction Service that leverages seven-locus haplotype frequencies (HLA-A ∼ C ∼ B ∼ DRB3/4/5 ∼ DRB1 ∼ DQB1 ∼ DPB1) to extend the imputation of six-locus HLA typing (HLA-A ∼ C ∼ B ∼ DRB3/4/5 ∼ DRB1 ∼ DQB1) to the HLA-DPB1 locus, including the novel prediction of HLA-DPB1 TCE groups to calculate donor-recipient TCE permissive match probabilities. Simulations of current-day patient searches reveal the service can fill in missing gaps for another four in five donors that appears on lists. To validate its performance, samples of 206,328 registered donors and 5,218 donor-recipient pairs with known high-resolution HLA-DPB1 typing were used for predicted-versus-observed comparisons. These comparisons demonstrated that the predictions were correct for 11.9-19.7% of HLA-DPB1 genotypes, 64.9-70.0% of TCE groups, and 61.0% of permissive match categories. Although HLA-DPB1 match predictions must be confirmed by additional typing, knowledge of TCE match probabilities facilitates rapid and improved identification of best donor options, especially for populations of color. Thus, we developed the TCE Prediction Tool user interface for a pilot program with several transplant centers to preview the accuracy and utility of this prediction framework, which provides valuable upfront optimization of donor selection.