Accurate imputation of human leukocyte antigens with CookHLA

Abstract

The recent development of imputation methods enabled the prediction of human leukocyte antigen (HLA) alleles from intergenic SNP data, allowing studies to fine-map HLA for immune phenotypes. Here we report an accurate HLA imputation method, CookHLA, which has superior imputation accuracy compared to previous methods. CookHLA differs from other approaches in that it locally embeds prediction markers into highly polymorphic exons to account for exonic variability, and in that it adaptively learns the genetic map within MHC from the data to facilitate imputation. Our benchmarking with real datasets shows that our method achieves high imputation accuracy in a wide range of scenarios, including situations where the reference panel is small or ethnically unmatched. Human leukocyte antigen (HLA) genes influence many immune phenotypes, however methods to impute HLA type have been limited in accuracy. Here, the authors present an HLA imputation method, CookHLA, which uses locally embedded prediction markers to adaptively impute HLA genes across a range of scenarios.