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.
摘要：
用于人类白细胞抗原（HLA）分型的基于SNP的插补方法利用了主要组织相容性复合物（MHC）区域内的单倍型结构。这些方法使用密集的SNP基因型预测HLA经典等位基因，通常在全基因组关联研究（GWAS）中使用的基于阵列的平台上发现。HLA经典等位基因的分析可以在没有额外成本的情况下在当前SNP数据集上进行。这里,我们描述了HIBAG的工作流程，一种带有属性装袋的插补方法，使用SNP数据推断样本的HLA经典等位基因。提供了两个示例来演示使用1000Genomes项目最新版本的公共HLA和SNP数据的功能：使用GWAS中预先构建的分类器进行基因型填补，和模型训练，以创建新的预测模型。GPU实现有助于模型构建，使它比单线程实现快数百倍。
