Characterization of the novel HLA-C*14:124 allele by next generation sequencing.
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ABSTRACT:
SUBMITTER: Ulrich S
PROVIDER: S-EPMC8252514 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The highest genetic type 1 diabetes risk is conferred by HLA class II haplotypes defined by alleles at the HLA-DR and -DQ loci. The combination of HLA-DQA1*03:01 and DQB1*03:02 alleles (summarized as 'HLA-DQ8') is reported to be among the two most prevalent HLA class II haplotypes in Caucasian type 1 diabetes patients. This classification is based on conventional genotyping of exon 2 of the DQ gene locus and excludes exon 3. In this study, HLA genotyping on the type 1 diabetes susceptibility loci HLA-DRB1, DQA1 and DQB1 was performed using a high-resolution next generation sequencing method. In addition to the routinely examined exon 2, exon 3 was also sequenced. Samples from 229 children with type 1 diabetes were included and compared to a cohort of 9,786 controls. In addition to previously described HLA-DQ haplotypes in type 1 diabetes patients, we found that as well as HLA-DQA1*03:01,HLA-DQA1*03:03 also contributed to HLA-DQ8. HLA-DQA1*03:03 differs from HLA-DQA1*03:01 by one nucleotide substitution in exon 3 at position 160, leading to a single amino acid replacement. DRB1*04:05 was exclusively associated with DQA1*03:03 whereas the DRB1*04:01 haplotype comprised either DQA1*03:01 or DQA1*03:03. Significantly increased type 1 diabetes risk was confirmed for all these haplotypes with only minor differences between DQA1*03:01 and DQA1*03:03 alleles. This study identified the HLA-DQA1*03:03 allele as an addition to the already known type 1 diabetes risk haplotypes, and can contribute to more precise HLA genotyping approaches.
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Project description:The human leukocyte antigen (HLA) genes are extremely polymorphic and are useful molecular markers to make inferences about human population history. However, the accuracy of the estimation of genetic diversity at HLA loci very much depends on the technology used to characterize HLA alleles; high-resolution genotyping of long-range HLA gene products improves the assessment of HLA population diversity as well as other population parameters compared to lower resolution typing methods. In this study we examined allelic and haplotype HLA diversity in a large healthy European American population sourced from the UCSF-DNA bank. A high-resolution next-generation sequencing method was applied to define non-ambiguous 3- and 4-field alleles at the HLA-A, HLA-C, HLA-B, HLA-DRB1, HLA-DRB3/4/5, HLA-DQA1, HLA-DQB1, HLA-DPA1, and HLA-DPB1 loci in samples provided by 2248 unrelated individuals. A number of population parameters were examined including balancing selection and various measurements of linkage disequilibrium were calculated. There were no detectable deviations from Hardy-Weinberg proportions at HLA-A, HLA-DRB1, HLA-DQA1 and HLA-DQB1. For the remaining loci moderate and significant deviations were detected at HLA-C, HLA-B, HLA-DRB3/4/5, HLA-DPA1 and HLA-DPB1 loci mostly from population substructures. Unique 4-field associations were observed among alleles at 2 loci and haplotypes extending large intervals that were not apparent in results obtained using testing methodologies with limited sequence coverage and phasing. The high diversity at HLA-DPA1 results from detection of intron variants of otherwise well conserved protein sequences. It may be speculated that divergence in exon sequences may be negatively selected. Our data provides a valuable reference source for future population studies that may allow for precise fine mapping of coding and non-coding sequences determining disease susceptibility and allo-immunogenicity.
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Project description:MOTIVATION: The human leukocyte antigen (HLA) gene cluster plays a crucial role in adaptive immunity and is thus relevant in many biomedical applications. While next-generation sequencing data are often available for a patient, deducing the HLA genotype is difficult because of substantial sequence similarity within the cluster and exceptionally high variability of the loci. Established approaches, therefore, rely on specific HLA enrichment and sequencing techniques, coming at an additional cost and extra turnaround time. RESULT: We present OptiType, a novel HLA genotyping algorithm based on integer linear programming, capable of producing accurate predictions from NGS data not specifically enriched for the HLA cluster. We also present a comprehensive benchmark dataset consisting of RNA, exome and whole-genome sequencing data. OptiType significantly outperformed previously published in silico approaches with an overall accuracy of 97% enabling its use in a broad range of applications.
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2017-04-03 | PXD003804 | Pride
Project description:The progression and clonal development of tumors often involve amplifications and deletions of genomic DNA. Estimation of allele-specific copy number, which quantifies the number of copies of each allele at each variant loci rather than the total number of chromosome copies, is an important step in the characterization of tumor genomes and the inference of their clonal history. We describe a new method, falcon, for finding somatic allele-specific copy number changes by next generation sequencing of tumors with matched normals. falcon is based on a change-point model on a bivariate mixed Binomial process, which explicitly models the copy numbers of the two chromosome haplotypes and corrects for local allele-specific coverage biases. By using the Binomial distribution rather than a normal approximation, falcon more effectively pools evidence from sites with low coverage. A modified Bayesian information criterion is used to guide model selection for determining the number of copy number events. Falcon is evaluated on in silico spike-in data and applied to the analysis of a pre-malignant colon tumor sample and late-stage colorectal adenocarcinoma from the same individual. The allele-specific copy number estimates obtained by falcon allows us to draw detailed conclusions regarding the clonal history of the individual's colon cancer.
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