{Reference Type}: Journal Article
{Title}: Imputation-Based HLA Typing with GWAS SNPs.
{Author}: Zheng X;Lee J;
{Journal}: Methods Mol Biol
{Volume}: 2809
{Issue}: 0
{Year}: 2024
暂无{DOI}: 10.1007/978-1-0716-3874-3_9
{Abstract}: SNP-based imputation approaches for human leukocyte antigen (HLA) typing take advantage of the haplotype structure within the major histocompatibility complex (MHC) region. These methods predict HLA classical alleles using dense SNP genotypes, commonly found on array-based platforms used in genome-wide association studies (GWAS). The analysis of HLA classical alleles can be conducted on current SNP datasets at no additional cost. Here, we describe the workflow of HIBAG, an imputation method with attribute bagging, to infer a sample's HLA classical alleles using SNP data. Two examples are offered to demonstrate the functionality using public HLA and SNP data from the latest release of the 1000 Genomes project: genotype imputation using pre-built classifiers in a GWAS, and model training to create a new prediction model. The GPU implementation facilitates model building, making it hundreds of times faster compared to the single-threaded implementation.