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HLA-VBSeq V2: Improved HLA Calling Accuracy with Full-length Japanese Class-I Panel

Overview
Journal Hum Genome Var
Date 2019 Jun 27
PMID 31240105
Citations 6
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Abstract

HLA-VBSeq is an HLA calling tool developed to infer the most likely HLA types from high-throughput sequencing data. However, there is still room for improvement in specific genetic groups because of the diversity of HLA alleles in human populations. Here, we present HLA-VBSeq v2, a software application that makes use of a new Japanese HLA reference panel to enhance calling accuracy for Japanese HLA class-I genes. Our analysis showed significant improvements in calling accuracy in all HLA regions, with prediction accuracies achieving over 99.0, 97.8, and 99.8% in HLA-A, B and C, respectively.

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References
1.
Dilthey A, Gourraud P, Mentzer A, Cereb N, Iqbal Z, McVean G . High-Accuracy HLA Type Inference from Whole-Genome Sequencing Data Using Population Reference Graphs. PLoS Comput Biol. 2016; 12(10):e1005151. PMC: 5085092. DOI: 10.1371/journal.pcbi.1005151. View

2.
Itoh Y, Mizuki N, Shimada T, Azuma F, Itakura M, Kashiwase K . High-throughput DNA typing of HLA-A, -B, -C, and -DRB1 loci by a PCR-SSOP-Luminex method in the Japanese population. Immunogenetics. 2005; 57(10):717-29. DOI: 10.1007/s00251-005-0048-3. View

3.
Robinson J, Halliwell J, Hayhurst J, Flicek P, Parham P, Marsh S . The IPD and IMGT/HLA database: allele variant databases. Nucleic Acids Res. 2014; 43(Database issue):D423-31. PMC: 4383959. DOI: 10.1093/nar/gku1161. View

4.
Flomenberg N, Baxter-Lowe L, Confer D, Fernandez-Vina M, Filipovich A, Horowitz M . Impact of HLA class I and class II high-resolution matching on outcomes of unrelated donor bone marrow transplantation: HLA-C mismatching is associated with a strong adverse effect on transplantation outcome. Blood. 2004; 104(7):1923-30. DOI: 10.1182/blood-2004-03-0803. View

5.
Mimori T, Yasuda J, Kuroki Y, Shibata T, Katsuoka F, Saito S . Construction of full-length Japanese reference panel of class I HLA genes with single-molecule, real-time sequencing. Pharmacogenomics J. 2018; 19(2):136-146. PMC: 6462828. DOI: 10.1038/s41397-017-0010-4. View