title: HIBAG--HLA genotype imputation with attribute bagging.
abstract: Genotyping of classical human leukocyte antigen (HLA) alleles is an essential tool in the analysis of diseases and adverse drug reactions with associations mapping to the major histocompatibility complex (MHC). However, deriving high-resolution HLA types subsequent to whole-genome single-nucleotide polymorphism (SNP) typing or sequencing is often cost prohibitive for large samples. An alternative approach takes advantage of the extended haplotype structure within the MHC to predict HLA alleles using dense SNP genotypes, such as those available from genome-wide SNP panels. Current methods for HLA imputation are difficult to apply or may require the user to have access to large training data sets with SNP and HLA types. We propose HIBAG, HLA Imputation using attribute BAGging, that makes predictions by averaging HLA-type posterior probabilities over an ensemble of classifiers built on bootstrap samples. We assess the performance of HIBAG using our study data (n=2668 subjects of European ancestry) as a training set and HLA data from the British 1958 birth cohort study (n≈1000 subjects) as independent validation samples. Prediction accuracies for HLA-A, B, C, DRB1 and DQB1 range from 92.2% to 98.1% using a set of SNP markers common to the Illumina 1M Duo, OmniQuad, OmniExpress, 660K and 550K platforms. HIBAG performed well compared with the other two leading methods, HLA*IMP and BEAGLE. This method is implemented in a freely available HIBAG R package that includes pre-fit classifiers for European, Asian, Hispanic and African ancestries, providing a readily available imputation approach without the need to have access to large training data sets.
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA, USA.
authors.forename: X
authors.initials: X
authors.lastname: Zheng
authors.affiliation: Quantitative Sciences, GlaxoSmithKline, Research Triangle Park, NC, USA.
authors.forename: J
authors.initials: J
authors.lastname: Shen
authors.affiliation: Quantitative Sciences, GlaxoSmithKline, Stevenage, UK.
authors.forename: C
authors.initials: C
authors.lastname: Cox
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA, USA.
authors.forename: J C
authors.initials: JC
authors.lastname: Wakefield
authors.affiliation: Quantitative Sciences, GlaxoSmithKline, Research Triangle Park, NC, USA.
authors.forename: M G
authors.initials: MG
authors.lastname: Ehm
authors.affiliation: Quantitative Sciences, GlaxoSmithKline, Research Triangle Park, NC, USA.
authors.forename: M R
authors.initials: MR
authors.lastname: Nelson
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA, USA.
authors.forename: B S
authors.initials: BS
authors.lastname: Weir
mesh_terms: D000483:Alleles; D044466:Asian People / Q000235:genetics; D064420:Drug-Related Side Effects and Adverse Reactions / Q000235:genetics*; D055106:Genome-Wide Association Study; D005838:Genotype; D006680:HLA Antigens / Q000235:genetics*; D006239:Haplotypes; D006801:Humans; D008285:Major Histocompatibility Complex / Q000235:genetics*; D020641:Polymorphism, Single Nucleotide; D044465:White People / Q000235:genetics
chemical_list: D006680:HLA Antigens