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BMC Bioinformatics. 2017 Jul 11;18(1):334. doi: 10.1186/s12859-017-1746-1.

HLA-check: evaluating HLA data from SNP information.

Author information

1
Laboratoire Génomique, Bioinformatique et Applications, EA 4627, Conservatoire National des Arts et Métiers, 292 rue Saint-Martin, Paris, 75003, France.
2
Laboratoire Génomique, Bioinformatique et Applications, EA 4627, Conservatoire National des Arts et Métiers, 292 rue Saint-Martin, Paris, 75003, France. zagury@cnam.fr.

Abstract

BACKGROUND:

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.

RESULTS:

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.

CONCLUSION:

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.

KEYWORDS:

Human leukocyte antigen; Imputation; Major histocompatibility complex

PMID:
28697761
PMCID:
PMC5504728
DOI:
10.1186/s12859-017-1746-1
[Indexed for MEDLINE]
Free PMC Article

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