Race, ethnicity, ancestry, and aspects that impact HLA data and matching for transplant.

Race, ethnicity, and ancestry are terms that are often misinterpreted and/or used interchangeably. There is lack of consensus in the scientific literature on the definition of these terms and insufficient guidelines on the proper classification, collection, and application of this data in the scientific community. However, defining groups for human populations is crucial for multiple healthcare applications and clinical research. Some examples impacted by population classification include HLA matching for stem-cell or solid organ transplant, identifying disease associations and/or adverse drug reactions, defining social determinants of health, understanding diverse representation in research studies, and identifying potential biases. This article describes aspects of race, ethnicity and ancestry information that impact the stem-cell or solid organ transplantation field with particular focus on HLA data collected from donors and recipients by donor registries or transplant centers.

HLA Genotype Imputation Results in Largely Accurate Epitope Mismatch Risk Categorization Across Racial Groups.

Background: Biomarkers that predict posttransplant alloimmunity could lead to improved long-term graft survival. Evaluation of the number of mismatched epitopes between donor and recipient HLA proteins, termed molecular mismatch analysis, has emerged as an approach to classify transplant recipients as having high, intermediate, or low risk of graft rejection. When high-resolution genotypes are unavailable, molecular mismatch analysis requires algorithmic assignment, or imputation, of a high-resolution genotyping. Although imputation introduces inaccuracies in molecular mismatch analyses, it is unclear whether these inaccuracies would impact the clinical risk assessment for graft rejection. Methods: Using renal transplant patients and donors from our center, we constructed cohorts of surrogate donor-recipient pairs with high-resolution and low-resolution HLA genotyping that were racially concordant or discordant. We systemically assessed the impact of imputation on molecular mismatch analysis for cohorts of 180-200 donor-recipient pairs for each of 4 major racial groups. We also evaluated the effect of imputation for a racially diverse validation cohort of 35 real-world renal transplant pairs. Results: In the surrogate donor-recipient cohorts, imputation preserved the molecular mismatch risk category for 90.5%-99.6% of racially concordant donor-recipient pairs and 92.5%-100% of racially discordant pairs. In the validation cohort, which comprised 72% racially discordant pairs, we found that imputation preserved the molecular mismatch risk category for 97.1% of pairs. Conclusions: Overall, these data demonstrate that imputation preserves the molecular mismatch risk assessment in the vast majority of cases and provides evidence supporting imputation in the performance of molecular mismatch analysis for clinical assessment.