An integrated genotyping approach for HLA and other complex genetic systems

Nelson, Wyatt C.; Pyo, Chul Woo; Vogan, David; Wang, Ruihan; Pyon, Yoon Soo; Hennessey, Carly; Smith, Anajane G.; Pereira, Shalini; Ishitani, Akiko; Geraghty, Daniel E.

doi:10.1016/j.humimm.2015.05.001

Cited by 56 publications

(64 citation statements)

References 51 publications

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“…To evaluate the accuracy of each exome-based software, we genotyped HLA of these 12 samples using PCR amplicon-based high-resolution HLA typing on MiSeq. 25 We calculated the accuracies in each of HLA-A, HLA-B and HLA-C genes, and the accuracy of all three HLA class I genes ( HLA-A,B,C ), which was a percentage of correctly identified alleles among the total 72 alleles (2 alleles × 3 loci × 12 samples; see Materials and methods). The accuracies for HLA-A,B,C in each software varied from 36.1 to 100% at the resolution level of the second field (Supplementary Table 4).…”

Section: Resultsmentioning

confidence: 99%

“…PCR amplicon-based high-resolution typing on MiSeq (Illumina, San Diego, CA, USA) was performed for HLA-A , HLA-B and HLA-C in Scisco Genetics Inc. (Seattle, WA, USA). 25 …”

Section: Methodsmentioning

confidence: 99%

“…These techniques are labor-intensive, time-consuming, and often lead to ambiguous genotyping results because of limitation of oligonucleotide probe design or phase ambiguity for HLA allele assignment. 23, 24 Several protocols have recently been established for HLA -targeted multiplexed PCR or long-range PCR methods coupled with next-generation sequencing that enable us to obtain accurate and super high resolution of HLA information up to fourth field level, 25, 26, 27 although errors introduced by PCR artifacts, sequencing procedures, bioinformatics or inadequately genotyped sequence references are still inherent problems. 28, 29 Recently, several tools to extract HLA allele information from genome-wide sequencing data, including whole-exome, whole-genome and transcriptome data, but it is still challenging to improve the accuracy of these tools.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of exome-based HLA class I genotyping tools: identification of platform-specific genotyping errors

Kiyotani

2016

J Hum Genet

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Accurate human leukocyte antigen (HLA) genotyping is critical in studies involving the immune system. Several algorithms to estimate HLA genotypes from whole-exome data were developed. We compared the accuracy of seven algorithms, including Optitype, Polysolver and PHLAT, as well as investigated patterns and possible causes of miscalls using 12 clinical samples and 961 individuals from the 1000 Genomes Project. Optitype showed the highest accuracy of 97.2% for HLA class I alleles at the second field resolution, followed by 94.0% in Polysolver and 85.6% in PHLAT. In Optitype, 34 (21.1%) of 161 miscalls were across different serological types, and common miscalls were HLA-A*26:01 to HLA-A*25:01, HLA-B*45:01 to HLA-B*44:15 and HLA-C*08:02 to HLA-C*05:01 with error rates of 4.1%, 10.0% and 4.1%, respectively. In Polysolver, 193 (55.9%) of 345 miscalls occurred across different serological alleles, and a specific pattern of genotyping error from HLA-A*25:01 to HLA-A*26:01 was observed in 93.3% of HLA-A*25:01 carriers, due to dropping of HLA-A*25:01 sequence reads during the extraction process of HLA reads. In PHLAT, 147 (59.8%) of 246 miscalls in HLA-A were due to erroneous assignment of multiple alleles to either HLA-A*01:22 or HLA-A*01:81. These results suggest that careful considerations needed to be taken when using exome-based HLA class I genotyping data and applying these results in clinical settings.

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Section: Resultsmentioning

confidence: 99%

“…PCR amplicon-based high-resolution typing on MiSeq (Illumina, San Diego, CA, USA) was performed for HLA-A , HLA-B and HLA-C in Scisco Genetics Inc. (Seattle, WA, USA). 25 …”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of exome-based HLA class I genotyping tools: identification of platform-specific genotyping errors

Kiyotani

2016

J Hum Genet

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“…Once the beta cell autoimmune reaction is established, the pathogenesis includes spreading of the autoimmunity to additional autoantigens. Using next-generation sequencing (NGS), an integrated genotyping system of exons 1–4 was developed to type all alleles of DRB1 , DRB3 , DRB4 and DRB5 [28, 29] (Fig. 2).…”

Section: Genetic Aetiologymentioning

confidence: 99%

Early prediction of autoimmune (type 1) diabetes

2017

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Underlying type 1 diabetes is a genetic aetiology dominated by the influence of specific HLA haplotypes involving primarily the class II DR-DQ region. In genetically predisposed children with the DR4-DQ8 haplotype, exogenous factors, yet to be identified, are thought to trigger an autoimmune reaction against insulin, signalled by insulin autoantibodies as the first autoantibody to appear. In children with the DR3-DQ2 haplotype, the triggering reaction is primarily against GAD signalled by GAD autoantibodies (GADA) as the first-appearing autoantibody. The incidence rate of insulin autoantibodies as the first-appearing autoantibody peaks during the first years of life and declines thereafter. The incidence rate of GADA as the first-appearing autoantibody peaks later but does not decline. The first autoantibody may variably be followed, in an apparently non-HLA-associated pathogenesis, by a second, third or fourth autoantibody. Although not all persons with a single type of autoantibody progress to diabetes, the presence of multiple autoantibodies seems invariably to be followed by loss of functional beta cell mass and eventually by dysglycaemia and symptoms. Infiltration of mononuclear cells in and around the islets appears to be a late phenomenon appearing in the multiple-autoantibody-positive with dysglycaemia. As our understanding of the aetiology and pathogenesis of type 1 diabetes advances, the improved capability for early prediction should guide new strategies for the prevention of type 1 diabetes.Electronic supplementary materialThe online version of this article (doi:10.1007/s00125-017-4308-1) contains a slideset of the figures for download, which is available to authorised users.

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“…As the HLA genes (A, B, C, DRB1 and DQB1) are more than 5 Kb, the 454 or other short read sequencing was not able to resolve haplotypes of donors. To overcome the phasing limitations across distant SNPs especially those found in class II genes [12], third generation sequencing technology has been utilised where full-length HLA genesare amplified using long range PCR and sequenced using single molecule real-time (SMRT) PacBio sequencing [13]. Pac-Bio sequencing of HLA genes provides high-resolution (6 to 8 digit) HLA allelic information for multiple HLA genes (6 genes) with phased SNPs [13].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analyses of Low, Medium and High-Resolution HLA Typing Technologies for Human Populations

Gowda¹,

Ambardar²,

Dighe³

et al. 2016

J Clin Cell Immunol

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Human Leukocyte Antigen (HLA) encoding genes are part of the major histocompatibility complex (MHC) on human chromosome 6. This region is one of the most polymorphic regions in the human genome. Prior knowledge of HLA allelic polymorphisms is clinically important for matching donor and recipient during organ/tissue transplantation. HLA allelic information is also useful in predicting immune responses to various infectious diseases, genetic disorders and autoimmune conditions. India harbors over a billion people and its population is untapped for HLA allelic diversity. In this study, we explored and compared three HLA typing methods for South Indian population, using Sequence-Specific Primers (SSP), NGS (Roche/454) and single-molecule sequencing (PacBio RS II) platforms. Over 1020 DNA samples were typed at low resolution using SSP method to determine the major HLA alleles within the South Indian population. These studies were followed up with medium resolution HLA typing of 80 samples based on exonic sequences on the Roche/454 sequencing system and high-resolution (6-8 digit) typing of 8 samples for HLA alleles of class I genes (HLA-A, B and C) and class II genes (HLA-DRB1 and DQB1) using PacBio RS II platform. The long reads delivered by SMRT technology, covered the full-length class I and class II genes/alleles in contiguous reads including untranslated regions, exons and introns, which provided phased SNP information. We have identified three novel alleles from PacBio data that were verified by Roche 454 sequencing. This is the first case study of HLA typing using second and third generation NGS technologies for an Indian population. The PacBio platform is a promising platform for large-scale HLA typing for establishing an HLA database for the untapped ethnic populations of India.

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An integrated genotyping approach for HLA and other complex genetic systems

Cited by 56 publications

References 51 publications

Comparison of exome-based HLA class I genotyping tools: identification of platform-specific genotyping errors

Comparison of exome-based HLA class I genotyping tools: identification of platform-specific genotyping errors

Early prediction of autoimmune (type 1) diabetes

Comparative Analyses of Low, Medium and High-Resolution HLA Typing Technologies for Human Populations

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