title: HLA imputation in an admixed population: An assessment of the 1000 Genomes data as a training set.
abstract: Methods to impute HLA alleles based on dense single nucleotide polymorphism (SNP) data provide a valuable resource to association studies and evolutionary investigation of the MHC region. The availability of appropriate training sets is critical to the accuracy of HLA imputation, and the inclusion of samples with various ancestries is an important pre-requisite in studies of admixed populations. We assess the accuracy of HLA imputation using 1000 Genomes Project data as a training set, applying it to a highly admixed Brazilian population, the Quilombos from the state of São Paulo. To assess accuracy, we compared imputed and experimentally determined genotypes for 146 samples at 4 HLA classical loci. We found imputation accuracies of 82.9%, 81.8%, 94.8% and 86.6% for HLA-A, -B, -C and -DRB1 respectively (two-field resolution). Accuracies were improved when we included a subset of Quilombo individuals in the training set. We conclude that the 1000 Genomes data is a valuable resource for construction of training sets due to the diversity of ancestries and the potential for a large overlap of SNPs with the target population. We also show that tailoring training sets to features of the target population substantially enhances imputation accuracy.
authors.affiliation: University of São Paulo, Department of Genetics and Evolutionary Biology, São Paulo, Brazil.
authors.forename: Kelly
authors.initials: K
authors.lastname: Nunes
authors.affiliation: University of Washington, Department of Biostatistics, Seattle, WA, USA.
authors.forename: Xiuwen
authors.initials: X
authors.lastname: Zheng
authors.affiliation: JRM-Investigações Imunológicas, Rio de Janeiro, Brazil.
authors.forename: Margareth
authors.initials: M
authors.lastname: Torres
authors.affiliation: JRM-Investigações Imunológicas, Rio de Janeiro, Brazil.
authors.forename: Maria Elisa
authors.initials: ME
authors.lastname: Moraes
authors.affiliation: JRM-Investigações Imunológicas, Rio de Janeiro, Brazil.
authors.forename: Bruno Z
authors.initials: BZ
authors.lastname: Piovezan
authors.affiliation: JRM-Investigações Imunológicas, Rio de Janeiro, Brazil.
authors.forename: Gerlandia N
authors.initials: GN
authors.lastname: Pontes
authors.affiliation: University of São Paulo, Department of Genetics and Evolutionary Biology, São Paulo, Brazil.
authors.forename: Lilian
authors.initials: L
authors.lastname: Kimura
authors.affiliation: University of São Paulo, Department of Genetics and Evolutionary Biology, São Paulo, Brazil.
authors.forename: Juliana E P
authors.initials: JEP
authors.lastname: Carnavalli
authors.affiliation: University of São Paulo, Department of Genetics and Evolutionary Biology, São Paulo, Brazil.
authors.forename: Regina C
authors.initials: RC
authors.lastname: Mingroni Netto
authors.affiliation: University of São Paulo, Department of Genetics and Evolutionary Biology, São Paulo, Brazil. Electronic address: diogo@ib.usp.br.
authors.forename: Diogo
authors.initials: D
authors.lastname: Meyer
mesh_terms: D000483:Alleles; D001938:Brazil; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D005828:Genetics, Population; D015894:Genome, Human; D055106:Genome-Wide Association Study; D006680:HLA Antigens / Q000235:genetics*; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D015203:Reproducibility of Results; D012984:Software; D064878:Web Browser
chemical_list: D006680:HLA Antigens
keywords: 1000 Genomes; Admixed populations; HLA; Imputation; Relatedness