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Human leukocyte antigen (HLA) genes are associated with a variety ofdiseases, however direct typing of HLA is time and cost consuming. Thus variousimputation methods using sequential SNPs data have been proposed based onstatistical or deep learning models, e.g. CNN-based model, named DEEP*HLA.However, imputation efficiency is not sufficient for in frequent alleles and alarge size of reference panel is required. Here, we developed aTransformer-based model to impute HLA alleles, named "HLA Reliable IMputatioNby Transformer (HLARIMNT)" to take advantage of sequential nature of SNPs data.We validated the performance of HLARIMNT using two different reference panels;Pan-Asian reference panel (n = 530) and Type 1 Diabetes Genetics Consortium(T1DGC) reference panel (n = 5,225), as well as the mixture of those two panels(n = 1,060). HLARIMNT achieved higher accuracy than DEEP*HLA by severalindices, especially for infrequent alleles. We also varied the size of dataused for training, and HLARIMNT imputed more accurately among any size oftraining data. These results suggest that Transformer-based model may imputeefficiently not only HLA types but also any other gene types from sequentialSNPs data.

Efficient HLA imputation from sequential SNPs data by Transformer