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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

HLA imputation in an admixed population: An assessment of the 1000 Genomes data as a training set

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Autor(es):
Nunes, Kelly [1] ; Zheng, Xiuwen [2] ; Torres, Margareth [3] ; Moraes, Maria Elisa [3] ; Piovezan, Bruno Z. [3] ; Pontes, Gerlandia N. [3] ; Kimura, Lilian [1] ; Carnavalli, Juliana E. P. [1] ; Mingroni Netto, Regina C. [1] ; Meyer, Diogo [1]
Número total de Autores: 10
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, Dept Genet & Evolutionary Biol, Sao Paulo - Brazil
[2] Univ Washington, Dept Biostat, Seattle, WA 98195 - USA
[3] JRM Invest Imunol, Rio De Janeiro - Brazil
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: HUMAN IMMUNOLOGY; v. 77, n. 3, p. 307-312, MAR 2016.
Citações Web of Science: 12
Resumo

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 Sao 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. (C) 2016 American Society for Histocompatibility and Immunogenetics. Published by Elsevier Inc. All rights reserved. (AU)

Processo FAPESP: 13/08028-1 - CEGH-CEL - Centro de Estudos do Genoma Humano e de Células-Tronco
Beneficiário:Mayana Zatz
Linha de fomento: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
Processo FAPESP: 12/18010-0 - Seleção balanceadora no genoma humano: detecção, causas e consequências
Beneficiário:Diogo Meyer
Linha de fomento: Auxílio à Pesquisa - Regular
Processo FAPESP: 12/09950-9 - Evolução de genes HLA: diferenciação populacional e sinais de seleção recente em populações nativas e miscigenadas do Brasil
Beneficiário:Kelly Nunes
Linha de fomento: Bolsas no Brasil - Pós-Doutorado