Introduction
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.
Download
HIBAG package: http://www.bioconductor.org/packages/HIBAG
HIBAG.gpu package: https://github.com/zhengxwen/HIBAG.gpu
Data package: data_package.zip
Files in data_package.zip
Description
1000g_hla_harmonized_pcode_alleles.csv
Harmonized HLA classical alleles in the 1000 Genomes Project from the two recent studies (Gourraud et al., 2014 & Abi-Rached et al., 2018) according the P groups
1kGP_HC_Illumina.chr6.SNV.xMHC.hg38.gds
SNP genotypes of 2702 individuals in the extended MHC region, called from the Illumina NovaSeq 6000 sequencing with a targeted depth of 30X
Illumina_Infinium_MEG_v1.0_MHC.csv.xz
Manifest file for the Illumina Infinium Multi-Ethnic Global platform (subsetting the SNPs in xMHC)
IMMPUTE_KG_HLA_2field.csv
Two-field HLA classical alleles in the ImmPute project from Gourraud et al., 2014
IMMPUTE_KG_SNP.vcf.gz
VCF file for SNP genotypes in the xMHC from the 1000 Genomes Project Phase 3
IMMPUTE_KG_SNP.vcf.gz.csi
Indexing for the VCF file
ImmunoChip-Broad-HLARES-HLA4-hg19.RData
HIBAG pre-built models for the Illumina ImmunoChip platform
Tutorials
Citation
If you use HIBAG in a published analysis, please report the HIBAG version and cite the appropriate publication or publications listed below:- Zheng & Lee (2024). “Imputation-based HLA Typing with GWAS SNPs.” Methods in Molecular Biology.
- Zheng (2018). “Imputation-based HLA Typing with SNPs in GWAS Studies.” Methods in Molecular Biology. doi: 10.1007/978-1-4939-8546-3_11.
- Zheng X, Shen J, Cox C, Wakefield J, Ehm M, Nelson M, Weir BS. HIBAG – HLA Genotype Imputation with Attribute Bagging. Pharmacogenomics Journal (2014). doi: 10.1038/tpj.2013.18.
Resources
- Pre-built HIBAG models: https://hibag.s3.amazonaws.com/hlares_index.html
- Allele Frequency Net Database (AFND): http://www.allelefrequencies.net
- IMGT/HLA Database: http://www.ebi.ac.uk/imgt/hla
- HLA Nomenclature: G Codes and P Codes for reporting of ambiguous allele typings