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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
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]
Total Authors: 10
Affiliation:
[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
Total Affiliations: 3
Document type: Journal article
Source: HUMAN IMMUNOLOGY; v. 77, n. 3, p. 307-312, MAR 2016.
Web of Science Citations: 12
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 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)

FAPESP's process: 13/08028-1 - CEGH-CEL - Human Genome and Stem Cell Research Center
Grantee:Mayana Zatz
Support type: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 12/18010-0 - Balancing selection in the human genome: detection, causes and consequences
Grantee:Diogo Meyer
Support type: Regular Research Grants
FAPESP's process: 12/09950-9 - Evolution of HLA genes: population differentiation and signatures of recent selection in native and admixed populations from Brazil
Grantee:Kelly Nunes
Support type: Scholarships in Brazil - Post-Doctorate