  Human leukocyte antigen (HLA) genes are associated with a variety of
diseases, however direct typing of HLA is time and cost consuming. Thus various
imputation methods using sequential SNPs data have been proposed based on
statistical or deep learning models, e.g. CNN-based model, named DEEP*HLA.
However, imputation efficiency is not sufficient for in frequent alleles and a
large size of reference panel is required. Here, we developed a
Transformer-based model to impute HLA alleles, named "HLA Reliable IMputatioN
by 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 several
indices, especially for infrequent alleles. We also varied the size of data
used for training, and HLARIMNT imputed more accurately among any size of
training data. These results suggest that Transformer-based model may impute
efficiently not only HLA types but also any other gene types from sequential
SNPs data.
