HLA-check: evaluating HLA data from SNP information

The major histocompatibility complex (MHC) region of the human genome, and specifically the human leukocyte antigen (HLA) genes, play a major role in numerous human diseases. With the recent progress of sequencing methods (eg, Next-Generation Sequencing, NGS), the accurate genotyping of this region...

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Bibliographic Details
Published in:BMC bioinformatics 2017-07, Vol.18 (1), p.334-334, Article 334
Main Authors: Jeanmougin, Marc, Noirel, Josselin, Coulonges, Cédric, Zagury, Jean-François
Format: Article
Language:English
Subjects:
Aberration
Accuracy
Algorithms
Alleles
Antigens
Archives & records
Association analysis
Binding sites
Bioinformatics
Chips
Consortia
Diabetes
European Continental Ancestry Group - genetics
Genes
Genetic aspects
Genetics
Genomes
Genomics
Genotypes
Genotyping
Genotyping Techniques - methods
Histocompatibility antigen HLA
Histocompatibility antigens
Histocompatibility Antigens Class I - genetics
Histocompatibility Testing
HLA Antigens - genetics
HLA histocompatibility antigens
Human leukocyte antigen
Humans
Imputation
Linkage Disequilibrium
Major histocompatibility complex
Polymorphism, Single Nucleotide
Population
Single nucleotide polymorphisms
Single-nucleotide polymorphism
Software
Studies
Transplants & implants
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Summary:The major histocompatibility complex (MHC) region of the human genome, and specifically the human leukocyte antigen (HLA) genes, play a major role in numerous human diseases. With the recent progress of sequencing methods (eg, Next-Generation Sequencing, NGS), the accurate genotyping of this region has become possible but remains relatively costly. In order to obtain the HLA information for the millions of samples already genotyped by chips in the past ten years, efficient bioinformatics tools, such as SNP2HLA or HIBAG, have been developed that infer HLA information from the linkage disequilibrium existing between HLA alleles and SNP markers in the MHC region. In this study, we first used ShapeIT and Impute2 to implement an imputation method akin to SNP2HLA and found a comparable quality of imputation on a European dataset. More importantly, we developed a new tool, HLA-check, that allows for the detection of aberrant HLA allele calling with regard to the SNP genotypes in the region. Adding this tool to the HLA imputation software increases dramatically their accuracy, especially for HLA class I genes. Overall, HLA-check was able to identify a limited number of implausible HLA typings (less than 10%) in a population, and these samples can then either be removed or be retyped by NGS for HLA association analysis.
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-017-1746-1