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HLA imputation in an admixed population: An assessment of the 1000 Genomes data as a training set.
Nunes, Kelly; Zheng, Xiuwen; Torres, Margareth; Moraes, Maria Elisa; Piovezan, Bruno Z; Pontes, Gerlandia N; Kimura, Lilian; Carnavalli, Juliana E P; Mingroni Netto, Regina C; Meyer, Diogo.
Afiliação
  • Nunes K; University of São Paulo, Department of Genetics and Evolutionary Biology, São Paulo, Brazil.
  • Zheng X; University of Washington, Department of Biostatistics, Seattle, WA, USA.
  • Torres M; JRM-Investigações Imunológicas, Rio de Janeiro, Brazil.
  • Moraes ME; JRM-Investigações Imunológicas, Rio de Janeiro, Brazil.
  • Piovezan BZ; JRM-Investigações Imunológicas, Rio de Janeiro, Brazil.
  • Pontes GN; JRM-Investigações Imunológicas, Rio de Janeiro, Brazil.
  • Kimura L; University of São Paulo, Department of Genetics and Evolutionary Biology, São Paulo, Brazil.
  • Carnavalli JEP; University of São Paulo, Department of Genetics and Evolutionary Biology, São Paulo, Brazil.
  • Mingroni Netto RC; University of São Paulo, Department of Genetics and Evolutionary Biology, São Paulo, Brazil.
  • Meyer D; University of São Paulo, Department of Genetics and Evolutionary Biology, São Paulo, Brazil. Electronic address: diogo@ib.usp.br.
Hum Immunol ; 77(3): 307-312, 2016 Mar.
Article em En | MEDLINE | ID: mdl-26582005
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Biologia Computacional / Polimorfismo de Nucleotídeo Único / Alelos / Genética Populacional / Antígenos HLA Limite: Humans País/Região como assunto: America do sul / Brasil Idioma: En Revista: Hum Immunol Ano de publicação: 2016 Tipo de documento: Article País de afiliação: Brasil País de publicação: Estados Unidos
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Biologia Computacional / Polimorfismo de Nucleotídeo Único / Alelos / Genética Populacional / Antígenos HLA Limite: Humans País/Região como assunto: America do sul / Brasil Idioma: En Revista: Hum Immunol Ano de publicação: 2016 Tipo de documento: Article País de afiliação: Brasil País de publicação: Estados Unidos