Genome-Wide Association Study Identifies IFIH1 and HLA-DQB1*05:02 Loci Associated With Anti-NMDAR Encephalitis.

BACKGROUND AND OBJECTIVES: Anti-N-methyl-d-aspartate receptor (NMDAR) encephalitis is a rare autoimmune neurologic disorder, the genetic etiology of which remains poorly understood. Our study aims to investigate the genetic basis of this disease in the Chinese Han population. METHODS: We performed a genome-wide association study and fine-mapping study within the major histocompatibility complex (MHC) region of 413 Chinese patients with anti-NMDAR encephalitis recruited from 6 large tertiary hospitals and 7,127 healthy controls. RESULTS: Our genome-wide association analysis identified a strong association at the IFIH1 locus on chromosome 2q24.2 (rs3747517, p = 1.06 × 10-8, OR = 1.55, 95% CI, 1.34-1.80), outside of the human leukocyte antigen (HLA) region. Furthermore, through a fine-mapping study of the MHC region, we discovered associations for 3 specific HLA class I and II alleles. Notably, HLA-DQB1*05:02 (p = 1.43 × 10-12; OR, 2.10; 95% CI 1.70-2.59) demonstrates the strongest association among classical HLA alleles, closely followed by HLA-A*11:01 (p = 4.36 × 10-7; OR, 1.52; 95% CI 1.29-1.79) and HLA-A*02:07 (p = 1.28 × 10-8; OR, 1.87; 95% CI 1.50-2.31). In addition, we uncovered 2 main HLA amino acid variation associated with anti-NMDAR encephalitis including HLA-DQß1-126H (p = 1.43 × 10-12; OR, 2.10; 95% CI 1.70-2.59), exhibiting a predisposing effect, and HLA-B-97R (p = 3.40 × 10-8; OR, 0.63; 95% CI 0.53-0.74), conferring a protective effect. Computational docking analysis suggested a close relationship between the NR1 subunit of NMDAR and DQB1*05:02. DISCUSSION: Our findings indicate that genetic variation in IFIH1, involved in the type I interferon signaling pathway and innate immunity, along with variations in the HLA class I and class II genes, has substantial implications for the susceptibility to anti-NMDAR encephalitis in the Chinese Han population.

Characterization of the novel HLA-DQB1*02:01:01:21Q allele by sequencing-based typing.

HLA-DQB1*02:01:01:21Q differs from HLA-DQB1*02:01:01:01 by one nucleotide substitution in the splice site in the beginning of intron 3.

Next-generation sequencing identifies two novel HLA-DQB1 alleles, HLA-DQB1*03:519 and HLA-DQB1*06:01:35.

We identified two novel HLA-DQB1 alleles by NGS, HLA-DQB1*03:519 and HLA-DQB1*06:01:35.

Family-based association of HLA-DRB1 and DQB1 alleles and haplotypes in a group of Iranian Type 1 diabetes children.

This family-based study was conducted in a group of Iranians with Type 1 diabetes (T1D) to investigate the transmission from parents of risk and non-risk HLA alleles and haplotypes, and to estimate the genetic risk score for this disease within this population. A total of 240 T1D subjects including 111 parent-child trios (111 children with T1D, 133 siblings, and 222 parents) and 330 ethnically matched healthy individuals were recruited. High-resolution HLA typing for DRB1/DQB1 loci was performed for all study subjects (n = 925) using polymerase chain reaction-sequence-specific oligonucleotide probe method. The highest predisposing effect on developing T1D was conferred by the following haplotypes both in all subjects and in probands compared to controls: DRB1*04:05-DQB1*03:02 (Pc = 2.97e-06 and Pc = 6.04e-10, respectively), DRB1*04:02-DQB1*03:02 (Pc = 5.94e-17 and Pc = 3.86e-09, respectively), and DRB1*03:01-DQB1*02:01 (Pc = 8.26e-29 and Pc = 6.56e-16, respectively). Conversely, the major protective haplotypes included DRB1*13:01-DQB1*06:03 (Pc = 6.99e-08), DRB1*15:01-DQB1*06:02 (Pc = 2.97e-06) in the cases versus controls. Also, DRB1*03:01-DQB1*02:01/DRB1*04:02|05-DQB1*03:02 and DRB1*03:01-DQB1*02:01/DRB1*03:01-DQB1*02:01 diplotypes conferred the highest predisposing effect in the cases (Pc = 8.65e-17 and Pc = 6.26e-08, respectively) and in probands (Pc = 5.4e-15 and Pc = 0.001, respectively) compared to controls. Transmission disequilibrium test showed that the highest risk was conferred by DRB1*04:02-DQB1*03:02 (Pc = 3.26e-05) and DRB1*03:01-DQB1*02:01 (Pc = 1.78e-12) haplotypes and the highest protection by DRB1*14:01-DQB1*05:03 (Pc = 8.66e-05), DRB1*15:01-DQB1*06:02 (Pc = 0.002), and DRB1*11:01-DQB1*03:01 (Pc = 0.0003) haplotypes. Based on logistic regression analysis, carriage of risk haplotypes increased the risk of T1D development 24.5 times in the Iranian population (p = 5.61e-13). Also, receiver operating characteristic curve analysis revealed a high predictive power of those risk haplotypes in discrimination of susceptible from healthy individuals (area under curve: 0.88, p = 5.5e-32). Our study highlights the potential utility of genetic risk assessment based on HLA diplotypes for predicting T1D risk in individuals, particularly among family members of affected children in our population.

Investigation of HLA susceptibility alleles and genotypes with hematological disease among Chinese Han population.

Several genes involved in the pathogenesis have been identified, with the human leukocyte antigen (HLA) system playing an essential role. However, the relationship between HLA and a cluster of hematological diseases has received little attention in China. Blood samples (n = 123913) from 43568 patients and 80345 individuals without known pathology were genotyped for HLA class I and II using sequencing-based typing. We discovered that HLA-A*11:01, B*40:01, C*01:02, DQB1*03:01, and DRB1*09:01 were prevalent in China. Furthermore, three high-frequency alleles (DQB1*03:01, DQB1*06:02, and DRB1*15:01) were found to be hazardous in malignant hematologic diseases when compared to controls. In addition, for benign hematologic disorders, 7 high-frequency risk alleles (A*01:01, B*46:01, C*01:02, DQB1*03:03, DQB1*05:02, DRB1*09:01, and DRB1*14:54) and 8 high-frequency susceptible genotypes (A*11:01-A*11:01, B*46:01-B*58:01, B*46:01-B*46:01, C*01:02-C*03:04, DQB1*03:01-DQB1*05:02, DQB1*03:03-DQB1*06:01, DRB1*09:01-DRB1*15:01, and DRB1*14:54-DRB1*15:01) were observed. To summarize, our findings indicate the association between HLA alleles/genotypes and a variety of hematological disorders, which is critical for disease surveillance.

[The Polymorphism Analysis of HLA Class II Alleles Based on Next-Generation Sequencing and Prevention Strategy for Allele Dropout].

OBJECTIVE: To investigate the accuracy of next-generation sequencing technology (NGS) in detecting the polymorphisms of HLA-DRB1, DQB1, DQA1, DRB3, DRB4, DRB5, DPA1 and DPB1 alleles in randomly-selected unrelated healthy individuals from Shenzhen Han population, investigate the potential reason for HLA-DRB1 allele dropout in routine NGS, and establish an internal quality control system. METHODS: NGS-based HLA class II genotyping was performed on 1 012 samples using the MiSeqDxTM platform. The suspected missed alleles indicated by the quality control software and HLA-DRB1 homozygotes were confirmed by PCR-SSOP or PCR-SBT methods. RESULTS: A total of 139 alleles were detected, including HLA-DRB1(45), DRB3(7), DRB4(5), DRB5(7), DQA1(17), DQB1(21), DPA1(10) and DPB1(27). HLA-DRB1*09:01(17.09%),15:01(10.72%); DRB3*02:02(25.99%),03:01(10.18%); DRB4*01:03(36.46%); DRB5*01:01(15.42%); DQA1*01:02(20.01%),03:02(17.19%); DQB1*03:01(19.47%),03:03(17.98%), 05:02(11.66%), 06:01(10.67%); DPA1*02:02(54.45%), 01:03(31.18%) and DPB1*05:01(39.13%), 02:01(16.90%) alleles were the most common alleles in Shenzhen Han population (frequencies >10%). There was no statistical difference between the gene frequencies of HLA-DRB1 and DQB1 loci in our study. The HLA Common and Well-Documented Alleles in China (CWD2.4) (χ2=12.68, P >0.05). 94 cases of HLA-DRB1 homozygous samples detected by NGS were retested by PCR-SSOP or SBT method, and one case of allele dropout at HLA-DRB1 locus was found. SBT method confirmed that the allele of DRB1*04:03 was missed. The laboratory internal quality control system was established. Two cases of new alleles were detected and named by WHO Nomenclature Committee for Factors of the HLA System. CONCLUSION: The HLA genotyping results based on NGS showed a significantly lower ambiguity rate. The HLA class II alleles exhibit genetic polymorphism in the Han population of unrelated healthy individuals in Shenzhen. The independent method based on NGS in clinical histocompatibility testing has limitations and requires internal quality control strategies to avoid allele-dropout events.

HLA Class II (DR, DQ, DP) Genes Were Separately Associated With the Progression From Seroconversion to Onset of Type 1 Diabetes Among Participants in Two Diabetes Prevention Trials (DPT-1 and TN07).

OBJECTIVE: To explore associations of HLA class II genes (HLAII) with the progression of islet autoimmunity from asymptomatic to symptomatic type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS: Next-generation targeted sequencing was used to genotype eight HLAII genes (DQA1, DQB1, DRB1, DRB3, DRB4, DRB5, DPA1, DPB1) in 1,216 participants from the Diabetes Prevention Trial-1 and Randomized Diabetes Prevention Trial with Oral Insulin sponsored by TrialNet. By the linkage disequilibrium, DQA1 and DQB1 are haplotyped to form DQ haplotypes; DP and DR haplotypes are similarly constructed. Together with available clinical covariables, we applied the Cox regression model to assess HLAII immunogenic associations with the disease progression. RESULTS: First, the current investigation updated the previously reported genetic associations of DQA1*03:01-DQB1*03:02 (hazard ratio [HR] = 1.25, P = 3.50*10-3) and DQA1*03:03-DQB1*03:01 (HR = 0.56, P = 1.16*10-3), and also uncovered a risk association with DQA1*05:01-DQB1*02:01 (HR = 1.19, P = 0.041). Second, after adjusting for DQ, DPA1*02:01-DPB1*11:01 and DPA1*01:03-DPB1*03:01 were found to have opposite associations with progression (HR = 1.98 and 0.70, P = 0.021 and 6.16*10-3, respectively). Third, DRB1*03:01-DRB3*01:01 and DRB1*03:01-DRB3*02:02, sharing the DRB1*03:01, had opposite associations (HR = 0.73 and 1.44, P = 0.04 and 0.019, respectively), indicating a role of DRB3. Meanwhile, DRB1*12:01-DRB3*02:02 and DRB1*01:03 alone were found to associate with progression (HR = 2.6 and 2.32, P = 0.018 and 0.039, respectively). Fourth, through enumerating all heterodimers, it was found that both DQ and DP could exhibit associations with disease progression. CONCLUSIONS: These results suggest that HLAII polymorphisms influence progression from islet autoimmunity to T1D among at-risk subjects with islet autoantibodies.

Four new intronic variants of HLA-DQB1 detected by next-generation sequencing.

Characterization of HLA-DQB1*02:02:01:18, -DQB1*02:02:01:19, -DQB1*03:02:01:20 and -DQB1*05:03:01:10 alleles in Spanish individuals.

[Establishment and validation of prediction models for human leukocyte antigen haplotypes and human leukocyte antigen genotypes].

Objective: To establish prediction models for human leukocyte antigen (HLA) haplotypes and HLA genotypes, and verify the prediction accuracy. Methods: The prediction models were established based on the characteristic of HLA haplotype inheritance and linkage disequilibrium (LD), as well as the invention patents and software copyrights obtained. The models include algorithm and reference databases such as HLA A-C-B-DRB1-DQB1 high-resolution haplotypes database, B-C and DRB1-DQB1 LD database, G group alleles table, and NMDP Code alleles table. The prediction algorithm involves data processing, comparison with reference data, filtering results, probability calculation and ranking, confidence degree estimation, and output of prediction results. The accuracy of the predictions was verified by comparing them with the correct results, and the relationship between prediction accuracy and the probability distribution and confidence degree of the predicted results was analyzed. Results: The HLA haplotypes and genotypes prediction models were established. The prediction algorithm included the prediction of A-C-B-DRB1-DQB1 haplotypes according to HLA-A, B, DRB1, C, DQB1 genotypes, the prediction of C and DQB1 high-resolution results according to A, B and DRB1 high-resolution results, and the prediction of A, B, DRB1, C and DQB1 high resolution results according to the A, B and DRB1 intermediate or low resolution results. Validation results of "Predicting A-C-B-DRB1-DQB1 haplotypes basing on HLA-A, B, DRB1, C, DQB1 genotypes" model: for 787 data, the accuracy was 94.0% (740/787) with 740 correct predictions, 34 incorrect predictions, and 13 instances with no predicted results. For 847 data, the accuracy was 100% (847/847). The 2 411 and 2 594 haplotype combinations predicted from 787 and 847 data were grouped according to confidence degree, the accuracy was 100% (48/48, 114/114) for a confidence degree of 1, 96.2% (303/315) and 97.8% (409/418) for a confidence degree of 2 respectively. Validation results of "Predicting A, B, DRB1 and C, DQB1 high-resolution genotypes basing on HLA-A, B, DRB1 high, intermediate, or low resolution genotypes" model: when predicting C and DQB1 high resolution genotypes basing on A, B, and DRB1 high resolution genotypes, 89.3% (1 459/1 634) of the predictions were correct. The accuracy for the top 2 predicted probability (GPP) ranking was 79.2% (1 156/1 459), and for the top 10, it was 95.0% (1 386/1 459). Furthermore, when GPP≥90% and GPP 50%-90%, the prediction accuracy was 81.3% (209/257) and 72.8% (447/614) respectively. The accuracy of predicting C and DQB1 high resolution genotypes basing on the results of A, B, and DRB1 high resolution genotypes from the China Marrow Donor Program was 87.0% (20/23). The accuracy of predicting A, B, DRB1, C, and DQB1 high resolution genotypes basing on the results of A, B, and DRB1 intermediate or low-resolution genotypes was 70.0% (7/10) and 52.5% (21/40) respectively. When predicting whether the patient is likely to have a HLA 10/10 matched donor, the accuracy of the top 2 GPP combinations with a proportion of ≥50% was 85.7% (6/7). Conclusions: When using A, B, DRB1, C, DQB1 genotypes to predict A-C-B-DRB1-DQB1 haplotype combinations, the results with a confidence degree of 1 and 2 are reliable. When predicting C and DQB1 genotypes according to A, B and DRB1 genotypes, the top 10 results ranked by GPP are reliable, and the top 2 results with GPP≥50% are more reliable.

Discovery of three HLA-DQB1*03:01:01 variants: HLA-DQB1*03:01:01:54, -DQB1*03:01:01:56 and -DQB1*03:01:01:58.

Three nucleotide substitutions in intronic regions give rise to the novel alleles: HLA-DQB1*03:01:01:54, -DQB1*03:01:01:56, -DQB1*03:01:01:58.

Identification of Antibodies to DQß:DRα Interisotypic Heterodimers in Human Sera.

BACKGROUND: HLA class II antigens, DR, DQ, and DP, comprised an α and ß chains, which typically combine, within the same isotype, to form the major histocompatibility complex:peptide complex. Interisotypic pairing is not commonly observed. Although reports of DQß:DRα heterodimers exist, the pairing was reported to be unstable and, therefore, not studied to any extent. METHODS: DQß:DRα single antigens were produced through transfectant cell lines and used to identify and characterize positive reactive human sera by a multiplex bead-based assay. RESULTS: Stable DQß:DRα transfectants were constructed. Cell surface staining with class II-specific monoclonal antibodies revealed that some DQB1 alleles appear to be more efficient in expressing DQß:DRα heterodimers. Interestingly, alleles within the same serological group varied in their efficiency of forming dimers on the cell surface. For example, DQß0601:DRα had the highest transfection and cell membrane expression efficiency among 16 common DQB1 alleles tested. In contrast, DQß0603:DRα-positive transfectants demonstrated minimal surface expression. Assembly of DQß0601:DRα was not affected by the presence of a DQα chain. DQß0601:DRα and DQß0603:DRα single-antigen beads were used to screen human sera. Positive sera were identified that reacted to the unique epitopes of DQß0601:DRα protein on the cell surface of the transfectants. CONCLUSIONS: Our studies have demonstrated that unique DQß:DRα heterodimers can be formed and are stably expressed on the cell surface. Such antigenic combinations, presented on single-antigen beads, demonstrated that patient sera can react with such heterodimers. Investigations on the potential clinical roles of antibodies against such interisotypic heterodimers are now possible.

Next generation sequencing identifies two novel HLA alleles, HLA-B*51:395 and HLA-DQB1*06:478.

We identified two novel HLA alleles by NGS, HLA-B*51:395 and HLA-DQB1*06:478.

Identification of the novel HLA-DQB1*03:512 allele by next-generation sequencing.

HLA-DQB1*03:512 differs from DQB1*03:02:01 by one nucleotide substitution in codon 89 in exon 2.

Characterization of the novel HLA-DQB1*03:516 allele by next-generation sequencing.

HLA-DQB1*03:516 differs from DQB1*03:03:02:03 by one nucleotide substitution at position 197G>A in exon 2.

Saliva direct PCR protocol for HLA-DQB1*02 genotyping.

Celiac disease (CD) is an immune disorder, that is triggered by gluten ingestion in genetically predisposed individuals. The HLA-DQB1*02 allele is the main predisposing genetic factor and a candidate for first-line genotyping screening. We designed and validated a simple, DNA purification-free PCR protocol directly from crude saliva, enabling the detection of the DQB1*02 allele. This assay also distinguishes homozygous from heterozygous carriers. We propose this method for use in mass screening and/or epidemiological studies.

HLA class II polymorphisms as prognostic biomarkers for right and left-sided colon cancer.

BACKGROUND: Colon cancer (CC) is one of the most common malignancies worldwide. Characterization of new prognostic biomarkers for right-sided CC (RCC) and left-sided CC (LCC) may contribute to improving early detection. An association of human leukocyte antigens class II (HLA-II) with the predisposition to CC was suggested. AIM OF THE STUDY: We evaluated the association of DRB1 and DQB1 with the risk of LCC and RCC. PATIENTS AND METHODS: Our study comprised 93 CC patients and 100 healthy controls. Genotyping of HLA class II alleles were performed by the Polymerase Chain Reaction Sequence-Specific Primers (PCR-SSP). RESULTS: DRB1*03 was positively associated with CC. In contrast, DRB1*11, DRB1*13, DQB1*03, and DQB1*05 were negatively linked to CC. Haplotype analysis revealed that DRB1*04-DQB1*04 and DRB1*09-DQB1*02 were positive, while DRB1*01-DQB1*05, DRB1*04-DQB1*03, DRB1*07-DQB1*02, DRB1*11-DQB1*03, DRB1*11-DQB1*05, and DRB1*13-DQB1*06 were negatively associated with CC. For sigmoid CC, DRB1*13, DRB1*11, and DQB1*05 were negative, while DRB1*04-DQB1*02, and DRB1*07-DQB1*03 were positively associated. DRB1*03 and DRB1*04-DQB1*04 were positive, while DRB1*11 and DQB1*03 were negatively linked to RCC. According to the LCC, DRB1*07, DRB1*11, DQB1*03, DQB1*05, and DRB1*07-DQB1*02 were negative. In contrast, DRB1*09-DQB1*02 was positively associated with LCC. Stratified analysis revealed that DRB1*11 is associated with higher risk of metastasis in CC and sigmoid CC, and tolerance to treatment in RCC. DQB1*03 was associated with lymph-node invasion in CC. CONCLUSION: DRB1 and DQB1 polymorphisms could be used as future biomarkers for the early detection of subjects at a higher risk of developing RCC and LCC, metastasis in sigmoid CC, and tolerance to treatment in RCC.

Confirmation and extension of 18 HLA alleles identified in donors from the Russian bone marrow registry.

Eighteen HLA allele sequences were confirmed and extended: 3 HLA-A, 6 HLA-B, 3 HLA-C, 2 HLA-DRB1, and 4 HLA-DQB1 alleles.

Discovery of the HLA-DQB1*06:465 allele, a variant of HLA-DQB1*06:04:01:01.

HLA-DQB1*06:465 differs from HLA-DQB1*06:04:01:01 by a non-synonymous nucleotide substitution in codon 38.

Association of HLA class I and II genes with cervical cancer susceptibility in a Han Chinese population.

Cervical cancer (CC) is one of the leading causes of cancer-related death in females worldwide. Genome-wide association studies (GWASs) have identified CC-related susceptibility loci in HLA regions. To investigate the associations between HLA genes and cervical intraepithelial neoplasia (CIN) or cervical cancer (CC), six loci of HLA class I (HLA-A, -B, and -C) and II (HLA-DRB1, -DPB1, and -DQB1) were selected for genotyping, and the associations between these alleles or their haplotypes with CIN or CC risk or protection from disease were evaluated. In total, 2193 participants, including 909 healthy individuals in the control group, 769 patients with CC, and 515 patients with CIN2+ (CIN II and III), were enrolled in the current study. HLA genes were genotyped using the NGSgo Illumina MiSeq workflow, and the associations between these loci and CIN2+ or CC at the allele and haplotype levels were analyzed. The allele frequencies of HLA-A*33:03, B*58:01, C*03:02, DPB1*05:01, and DRB1*12:01 were lower in both the CC and CIN2+ groups than in the control group, whereas those of B*55:02, C*04:03, and DPB1*03:01 were higher in the CC group than in the control group. In the histologic CC type analysis, the differences in the frequencies of these alleles in squamous cell carcinoma (SCC) of the cervix and stage I CC showed a consistent trend. In the haplotype analysis, the frequency of A*33:03-C*03:02-B*58:01 was lower in the CC and CIN2+ groups than in the control group, and that of A*24:02-C*04:03-B*15:25 was higher in the CC group than in both the control and CIN2+ groups. These three different haplotype frequencies were also identified in the FIGO CC stage analysis. In addition, in human papilloma virus (HPV) genotype analyses, the frequencies of HLA-C*03:02 and DPB1*05:01 were significantly lower in the CC and CIN2+ groups than in the control group, and in SCC subgroup, the frequencies of HLA-DQB1*04:01 and DRB1*04:05 were higher in the HPV other genotype infection group than in the HPV16 infection group. In both HPV16 single infection and coinfection with other HPVs, the frequency of haplotype A*33:03-C*03:02-B*58:01 was lower in both CC and CIN2+ than in the control group, while the frequencies of A*11:01-C*14:02-B*51:01 and A*24:02-C*03:04-B*13:01 were higher in the CIN2+ than in CC and the control group. In the HPV16 and other HPV infection comparisons, the frequencies of DRB1*04:05-DQB1*04:01-DPB1*02:01 and DRB1*11:01-DQB1*03:01-DPB1*05:01 were lower in the HPV16 infection group than in the other HPV infection group. Our results suggest that the HLA class I and II genes may affect the risk of CIN and CC as well as the histologic CC types and FIGO stages of CC in the Han Chinese population. In addition, HLA genes were associated with HPV16 infection at both the allelic and haplotype levels.

Next-generation sequencing identifies two novel HLA class II alleles: HLA-DRB1*12:107 and HLA-DQB1*06:476.

We identified two novel HLA class II alleles by next-generation sequencing, HLA-DRB1*12:107 and HLA-DQB1*06:476.