Improving <scp>HLA</scp> typing imputation accuracy and eplet identification with local next‐generation sequencing training data

Assessing donor/recipient HLA compatibility at the eplet level requires second field DNA typings but these are not always available. These can be estimated from lower‐resolution data either manually or with computational tools currently relying, best, on containing typing ambiguities. We gathered NGS 61,393 individuals in 17 French laboratories, for loci A, B, and C (100% of typings), DRB1 DQB1 (95.5%), DQA1 (39.6%), DRB3/4/5, DPB1, DPA1 (10.5%). developed HaploSFHI, a modified iterative maximum likelihood algorithm, to impute low‐ intermediate‐resolution ones. Compared reference HaploStats, HLA‐EMMA, HLA‐Upgrade, HaploSFHI provided more accurate predictions across all two test sets four European‐independent sets. Only could DQA1, solely HaploStats DRB3/4/5 imputations. The improved performance was due our local nonambiguous data. explanations most common imputation errors pinpointed variability low number low‐resolution haplotypes. thus guidance select whom sequencing would optimize incompatibility assessment cost‐effectiveness typing, considering only well‐imputed typing(s) also eplets.