Imputation-based HLA typing with SNPs in GWAS studies

Abstract

SNP-based imputation approaches for human leukocyte antigen (HLA) typing take advantage of the extended haplotype structure within the major histocompatibility complex (MHC) to predict classical HLA alleles using dense SNP genotypes, such as those available on chip panels of genome-wide association study (GWAS). These methods enable HLA analyses of classical alleles on existing SNP datasets genotyped in GWAS studies at no extra cost. Here, I describe the workflow of HIBAG, an imputation method with attribute bagging, for obtaining a sample’s HLA class I and II genotypes of two-field resolution using SNP data. Two examples are provided to illustrate with a publicly available HLA and SNP dataset: genotype imputation with pre-fit classifiers in GWAS, and model training to build a new classifier.