Next generation sequencing to determine HLA class II genotypes in a cohort of hematopoietic cell transplant patients and donors

Smith, Anajane G.; Pyo, Chul Woo; Nelson, Wyatt C.; Gow, Edward; Wang, Ruihan; Shěn, Shū; Sprague, Maggie; Pereira, Shalini; Geraghty, Daniel E.

doi:10.1016/j.humimm.2014.08.206

Cited by 36 publications

(32 citation statements)

References 30 publications

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“…The Plasmid Maxiprep Kit (Qiagen, Stockholm, Sweden) was used to isolate DNA from frozen whole blood samples according to the manufacturer's instructions. The NGTS HLA typing approach utilized PCR‐based amplification of HLA and sequencing using Illumina MiSeq technology as described in detail elsewhere . In brief, the laboratory steps consisted of consecutive PCR reactions with bar coding incorporated in the PCRs for individual sample tracking, followed by application to the MiSeq.…”

Section: Methodsmentioning

confidence: 99%

“…Using an amplicon‐based approach, the phase within each amplicon was determined directly by single read coverage, while phase between the 2 exons was deduced from database comparisons using the IMGT HLA version 3.10 (http://www.ebi.ac.uk/ipd/imgt/hla). To date, these tools have been tested—with 100% accuracy—on >2000 control samples genotyped with the Scisco Genetics IGS approach …”

Section: Methodsmentioning

confidence: 99%

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Building and validating a prediction model for paediatric type 1 diabetes risk using next generation targeted sequencing of class II HLA genes

Zhao

Carlsson

Larsson

et al. 2017

Diabetes Metabolism Res

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Building and validating a prediction model for paediatric type 1 diabetes risk using next generation targeted sequencing of class II HLA genes

Zhao

Carlsson

Larsson

et al. 2017

Diabetes Metabolism Res

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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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“…Smith et al used the NGS for resequencing exon 2 and 3 of DRB1/B3/B4/B5, DQA1 and DQB1, and exon 2 of DPA1 and DPB1 of 2605 hematopoietic cell transplant recipients and donors and obtain 99.6% accuracy for DRB1 assignment and 99.5% for DQB1 compared to the alleles genotyped pre-transplant. They were also able to eliminate diploid ambiguities by in-phase sequencing [45]. In a study 79000 samples from China Marrow Donor Registry were genotyped for HLA -A, -B, -C, DRB1 and DQB1 loci.…”

Section: Hla Genotyping Using Ngsmentioning

confidence: 99%

HLA Genotyping using Next Generation Sequencing

Lucan

Pop

Florian

et al. 2016

Romanian Journal of Internal Medicine

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* Authors with equal contributionFrom an oncological perspective, the second most common malignancies in children are brain tumors. Despite the recent therapeutic breakthroughs in this field, concerning surgery, radiotherapy and chemotherapy alike, some cases still have poor outcomes in curability. This is especially the case in patients with high-risk histological types of tumors, and those suffering from residual, remitting and disseminated diseases. Due to the unique neuroanatomical emplacement of brain tumors and their aggressive infiltrative behavior, their total removal remains a demanding task. This can be perceived in the high rates of failure treatment and disease recurrence. Furthermore, the adjacent healthy brain tissue is inevitably damaged in the surgical process of effectively removing these tumors. Thus, stem cell transplantation may be a viable solution for the clinical management of these malignancies, as proven by various recent breakthroughs. In the current concise review, we present the role of next generation sequencing in HLA typing for stem cell transplantation in primary CNS pediatric malignancies.

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Next generation sequencing to determine HLA class II genotypes in a cohort of hematopoietic cell transplant patients and donors

Cited by 36 publications

References 30 publications

Building and validating a prediction model for paediatric type 1 diabetes risk using next generation targeted sequencing of class II HLA genes

Building and validating a prediction model for paediatric type 1 diabetes risk using next generation targeted sequencing of class II HLA genes

Early prediction of autoimmune (type 1) diabetes

HLA Genotyping using Next Generation Sequencing

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