HLA-check: evaluating HLA data from SNP information

Abstract

BackgroundThe 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.ResultsIn 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.ConclusionOverall, 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.

Fine mapping of MHC region in lung cancer highlights independent susceptibility loci by ethnicity

“…To confirm imputed HLA alleles, we re-imputed 34 a random 10% subset of the samples (3000 European samples and 1000 Asian samples from Oncoarray) using HIBAG 18 ,which employs another pre-trained referenced panel and a different statistical method based on multiple expectation-maximization-based classifiers to estimate the likelihood of HLA alleles ( http://www.biostat.washington.edu/~bsweir/HIBAG/ ). Previous studies comparing the accuracy of HIBAG and SNP2HLA (among others imputation methods) to sequence data, concluded that they are the most robust programs with respect to maintaining accuracy 35 , 36 . In order to assess the accuracy of the imputation, we compared the imputed data for HLA alleles of class I ( HLA-A , HLA-B and HLA-C ) and class II ( HLA-DRB1 , HLA-DQB1 , HLA-DQA1 ), of those HLA genotypes obtained in the same individuals with the two methods described above.…”

HLA associations in inflammatory arthritis: emerging mechanisms and clinical implications

“…For clinical purposes, HLA typing is now performed by a combination of molecular and immunological techniques222 . When microarrays are employed for genome-wide analysis of single-nucleotide polymorphisms (SNPs), HLA gene region SNPs together with data in reference panels can be used to infer a reasonably accurate HLA genotype by a process called imputation223 (for example, using the HLA-check tool, a software tool for imputation of HLA genotype from SNP data, and the genome-wide SNP database). Complete nucleotide sequencing is the gold standard for HLA genotyping; the technology continues to evolve224 .…”

Low copy numbers of complement C4 and C4A deficiency are risk factors for myositis, its subgroups and autoantibodies

“…Genotyping for HLA-DRB1 alleles for samples from the USA and Sweden was performed at low resolution using the sequence-specific primer-PCR methods (eg, DR low-resolution kit: Olerup SSP, Saltsjobaden, Sweden) 41 42. The HLA-DRB1 genotypes for samples from the UK were deduced from single-nucleotide polymorphisms data using SNP2HLA software 20 43 44. High concordance of imputed data from DNA sequencing and conventional HLA typing techniques was obtained 20…”