Characterisation of the novel HLA-DQA1*01:02:24 allele by sequencing-based typing.

HLA-DQA1*01:02:24 differs from HLA-DQA1*01:02:01:03 by one nucleotide substitution in codon 167 in exon 3.

Identification of the novel HLA-DQA1*03:79 allele in a candidate bone marrow donor by next generation sequencing.

A single nucleotide mismatch within exon 3 of HLA-DQA1 differentiates the novel allele DQA1*03:79 from DQA1*03:15.

Detection of the novel HLA-DQA1*05:73 allele with an amino acid substitution in the α2 extracellular domain.

A single nucleotide substitution in the exon 3 gives rise to the novel HLA-DQA1*05:73 allele.

HLA-DQA1*05:115, the first DQA1 allele described with an alanine at position 59 of alpha-1 domain.

HLA-DQA1*05:115 shows an alanine at position 59 not described previously.

Genetic association of antinuclear antibodies with HLA in JIA patients: a Swedish cohort study.

BACKGROUND: Juvenile Idiopathic Arthritis (JIA) is a complex autoimmune disease and the most common chronic rheumatological disease affecting children under the age of 16. The etiology of JIA remains poorly understood, but evidence suggests a significant genetic predisposition. METHODS: We analyzed a Swedish cohort of 329 JIA patients and 728 healthy adult controls using the Illumina OmniExpress array for genotyping. HLA alleles were imputed from GWAS data using the SNP2HLA algorithm. RESULTS: Case-control analysis yielded 12 SNPs with genome-wide significant association to JIA, all located on chromosome 6 within the MHC class II gene region. Notably, the top SNP (rs28421666) was located adjacent to HLA-DQA1 and HLA-DRB1. HLA-DRB1*08:01, HLA-DQA1*04:01, and HLA-DQB1*04:02 were the haplotypes most strongly associated with an increased risk of JIA in the overall cohort. When analyzing disease specific subtypes, these alleles were associated with oligoarthritis and RF-negative polyarthritis. Within the complex linkage disequilibrium of the HLA-DRB1-DQA1-DQB1 haplotype, our analysis suggests that HLA-DRB1*08 might be the primary allele linked to JIA susceptibility. The HLA-DRB1*11 allele group was also independently associated with JIA and specifically enriched in the oligoarthritis patient group. Additionally, our study revealed a significant correlation between antinuclear antibody (ANA) positivity and specific HLA alleles. The ANA-positive JIA group showed stronger associations with the HLA-DRB1-DQA1-DQB1 haplotype, HLA-DRB1*11, and HLA-DPB1*02, suggesting a potential connection between genetic factors and ANA production in JIA. Furthermore, logistic regression analysis reaffirmed the effects of HLA alleles, female sex, and lower age at onset on ANA positivity. CONCLUSIONS: This study identified distinct genetic associations between HLA alleles and JIA subtypes, particularly in ANA-positive patients. These findings contribute to a better understanding of the genetic basis of JIA and provide insights into the genetic control of autoantibody production in ANA-positive JIA patients. This may inform future classification and personalized treatment approaches for JIA, ultimately improving patient outcomes and management of this disease.

HLA-DQA1*01:03 and DQB1*06:01 are risk factors for severe COVID-19 pneumonia.

The clinical spectrum of COVID-19 includes a wide range of manifestations, from mild symptoms to severe pneumonia. HLA system plays a pivotal role in immune responses to infectious diseases. The purpose of our study was to investigate the association between HLA and COVID-19 severity in a Japanese population. The study included 209 Japanese COVID-19 patients aged ≥20 years. Saliva samples were collected and used to determine the HLA genotype by HLA imputation through genome-wide association analyses. The association between HLA genotype and COVID-19 severity was then evaluated. The allele frequency was compared between patients with respiratory failure (severe group: 91 cases) and those without respiratory failure (non-severe group: 118 cases), categorising the data into three time periods: pre-Omicron epidemic period, Omicron epidemic period, and total period of this study (from January 2021 to May 2023). In comparing the severe and non-severe groups, the frequencies of the HLA-DQA1*01:03 (35.1% vs. 10.5%, odds ratio [OR] = 4.57, corrected p [pc] = 0.041) and -DQB1*06:01 (32.4% vs. 7.9%, OR = 5.54, pc = 0.030) alleles were significantly higher in the severe group during the pre-Omicron epidemic period. During the Omicron epidemic period, HLA-DQB1*06 (32.4% vs. 7.9%, OR = 5.54, pc = 0.030) was significantly higher in the severe group. During total period of this study, HLA-DQA1*01:03 (30.2% vs. 14.4%, OR = 2.57, corrected pc = 0.0013) and -DQB1*06:01 (44.5% vs. 26.7%, OR = 2.20, pc = 0.013) alleles were significantly higher in the severe group. HLA-DQB1*06:01 and -DQA1*01:03 were in strong linkage disequilibrium with each other (r2 = 0.91) during total period of this study, indicating that these two alleles form a haplotype. The frequency of the HLA-DQA1*01:03-DQB1*06:01 in the severe group was significantly higher than in the non-severe group during pre-Omicron epidemic period (32.4% vs. 7.9%, OR = 5.59, pc = 0.00072), and total period of this study (28.6% vs. 13.1%, OR = 2.63, pc = 0.0013). During Omicron epidemic period, the haplotype did not demonstrate statistical significance, although the odds ratio indicated a value greater 1. Frequencies of the HLA-DQA1*01:03 and -DQB1*06:01 alleles were significantly higher in severe COVID-19 patients, suggesting that these alleles are risk factors for severe COVID-19 pneumonia in the Japanese population.

Association between HLA alleles and haplotypes with age at diagnosis of type 1 diabetes in an admixed Brazilian population: A nationwide study.

To investigate the potential relationship between HLA alleles and haplotypes and the age at diagnosis of type 1 diabetes (T1DAgeD) in an admixed Brazilian population. This nationwide study was conducted in public clinics across 12 Brazilian cities. We collected demographic and genetic data from 1,600 patients with T1D. DNA samples were utilised to determine genomic ancestry (GA) and perform HLA typings for DRB1, DQA1 and DQB1. We explored allele and haplotype frequencies and GA in patients grouped by T1DAgeD categories (<6 years, ≥6-<11 years, ≥11-<19 years and ≥19 years) through univariate and multivariate analyses and primary component analyses. Additionally, we considered self-reported colour-race and identified a familiar history of T1D in first-degree relatives. The homozygosity index for DRB1~DQA1~DQB1 haplotypes exhibited the highest variation among T1DAgeD groups, and the percentages of Sub-Saharan African and European ancestries showed opposite trends in principal component analysis (PCA) analyses. Regarding the association of alleles and haplotypes with T1DAgeD, risk alleles such as HLA-DQB1*03:02g, -DQA1*03:01g, -02:01g, DRB1*04:05g and -04:02g were more frequently observed in heterozygosity or homozygosity in T1D patients with an early disease onset. Conversely, alleles such as DRB1*07:01g, -13:03g, DQB1*06:02g and DQA1*02:01 were more prevalent in older T1D patients. The combination DR3/DR4.5 was significantly associated with early disease onset. However, gender, GA, familiar history of T1D and self-reported colour-race identity did not exhibit significant associations with the onset of T1D. It is worth noting that the very common risk haplotype DRB1*03:01g~DQA1*05:01g~DQB1*02:01g did not differentiate between T1DAgeD groups. In the admixed Brazilian population, the high-risk haplotype DRB1*04:05~DQA1*03:01~DQB1*03:02 was more prevalent in individuals diagnosed before 6 years of age. In contrast, the protective alleles DQA1*01:02g, DQB1*06:02g, DRB1*07:01g and DRB1*13:03g and haplotypes DRB1*13:03g~DQA1*05:01g~DQB1*03:01g and DRB1*16:02g~DQA1*01:02g~DQB1*05:02g were more frequently observed in patients diagnosed in adulthood. Notably, these associations were independent of factors such as sex, economic status, GA, familiar history of T1D and region of birth in Brazil. These alleles and haplotypes contribute to our understanding of the disease onset heterogeneity and may have implications for early interventions when detected in association with well-known genomic risk or protection factors for T1D.

Characterisation of the novel HLA-DQA1*05:112 allele by sequencing-based typing.

HLA-DQA1*05:112 differs from HLA-DQA1*05:05:01:01 by one nucleotide substitution in codon -7 in exon 1.

The association between dietary, physical activity and the DNA methylation of PPARGC1A, HLA-DQA1 and ADCY3 in pregnant women with gestational diabetes mellitus: a nest case-control study.

BACKGROUND: Gestational diabetes mellitus (GDM) is associated with DNA methylation and lifestyle. The effects of DNA methylation on GDM, and the interaction between DNA methylation and lifestyle factors are not well elucidated. The objective of this study was to explore the association between GDM, DNA methylation and lifestyle factors. METHODS: A nest case-control design was performed. Sociodemographic data, dietary intake and daily physical activity information of pregnant women were collected. Bisulfate pyrosequencing was used to detect the DNA methylation level of PPARGC1A, HLA-DQA1, and ADCY3 genes. The differences of DNA methylation levels between the GDM group and the control group were compared. The correlation between clinical characteristics, dietary, physical activity and DNA methylation level was analyzed. RESULTS: A total of 253 pregnant women were enrolled, of which, 60 participants (GDM: 30; control: 30) were included in the final analysis. There were no significant differences in DNA methylation levels of six methylated sites between the two groups in this study (P > 0.05). Daily intake of potato and poultry were associated with DNA methylation level of the CpG 1 site of the ADCY3 gene in all participants and the control group (P < 0.05). Duration of folic acid intake before pregnancy was correlated with the methylation level of the CpG 1 site of the ADCY3 gene in all participants (r = 0.341, P = 0.04) and the control group (r = 0.431, P = 0.025). Daily oil intake was correlated with the methylation level of CpG 2 (r = 0.627, P = 0.016) and CpG 3 (r = 0.563, P = 0.036) of PPARGC1A in the GDM group. CONCLUSION: The association between the DNA methylation levels and GDM wasn't validated. There were associations between dietary and DNA methylation in pregnant women. A large-sample-sized and longitudinal study is warranted to further investigate the impacts of lifestyle on DNA methylation.

The novel HLA-DQA1*03:75 allele identified in a Korean kidney donor.

HLA-DQA1*03:75 differs from HLA-DQA1*03:02:01:01 by a single non-synonymous nucleotide substitution in exon 2.

Identification of the novel HLA-DQA1*05:03:03 allele by next-generation sequencing.

HLA-DQA1*05:03:03, a novel HLA class II allele detected by next-generation sequencing.

Characterisation of the novel HLA-DQA1*04:14N allele by next-generation sequencing.

HLA-DQA1*04:14N differs from HLA-DQA1*04:01:01:03 by a single nucleotide deletion in codon 113 in exon 3.

Multiple polygenic risk scores can improve the prediction of systemic lupus erythematosus in Taiwan.

OBJECTIVE: To identify new genetic variants associated with SLE in Taiwan and establish polygenic risk score (PRS) models to improve the early diagnostic accuracy of SLE. METHODS: The study enrolled 2429 patients with SLE and 48 580 controls from China Medical University Hospital in Taiwan. A genome-wide association study (GWAS) and PRS analyses of SLE and other three SLE markers, namely ANA, anti-double-stranded DNA antibody (dsDNA) and anti-Smith antibody (Sm), were conducted. RESULTS: Genetic variants associated with SLE were identified through GWAS. Some novel genes, which have been previously reported, such as RCC1L and EGLN3, were revealed to be associated with SLE in Taiwan. Multiple PRS models were established, and optimal cut-off points for each PRS were determined using the Youden Index. Combining the PRSs for SLE, ANA, dsDNA and Sm yielded an area under the curve of 0.64 for the optimal cut-off points. An analysis of human leucocyte antigen (HLA) haplotypes in SLE indicated that individuals with HLA-DQA1*01:01 and HLA-DQB1*05:01 were at a higher risk of being classified into the SLE group. CONCLUSIONS: The use of PRSs to predict SLE enables the identification of high-risk patients before abnormal laboratory data were obtained or symptoms were manifested. Our findings underscore the potential of using PRSs and GWAS in identifying SLE markers, offering promise for early diagnosis and prediction of SLE.

Identification of the novel HLA-DQA1*02:32 allele by next-generation sequencing.

HLA-DQA1*02:32 differs from DQA1*02:01:01 by one nucleotide substitution in codon 210 in exon 4.

Genomic sequence of five new variants of HLA-DQA1 in non-coding regions.

Characterisation of HLA-DQA1*01:02:01:30, HLA-DQA1*01:03:01:14, HLA-DQA1*03:03:01:20, HLA-DQA1*04:01:01:13, HLA-DQA1*05:05:01:40 alleles in Spanish individuals.

Identification of the novel HLA-DQA1*05:107 allele.

Full length sequence characterisation of the novel HLA-DQA1*05:107 allele from whole genome sequencing data.

The novel HLA-DQA1*05:05:17:03 allele, identified in a potential organ donor.

HLA-DQA1*05:05:17:03 differs from HLA-DQA1*05:05:01:02 by a single base substitution in exon 1 and HLA-DQA1*05:05:17:01 within introns 1 and 2.

A multi-ancestry genome-wide association study in type 1 diabetes.

Type 1 diabetes (T1D) is an autoimmune disease caused by destruction of the pancreatic ß-cells. Genome-wide association (GWAS) and fine mapping studies have been conducted mainly in European ancestry (EUR) populations. We performed a multi-ancestry GWAS to identify SNPs and HLA alleles associated with T1D risk and age at onset. EUR families (N = 3223), and unrelated individuals of African (AFR, N = 891) and admixed (Hispanic/Latino) ancestry (AMR, N = 308) were genotyped using the Illumina HumanCoreExome BeadArray, with imputation to the TOPMed reference panel. The Multi-Ethnic HLA reference panel was utilized to impute HLA alleles and amino acid residues. Logistic mixed models (T1D risk) and frailty models (age at onset) were used for analysis. In GWAS meta-analysis, seven loci were associated with T1D risk at genome-wide significance: PTPN22, HLA-DQA1, IL2RA, RNLS, INS, IKZF4-RPS26-ERBB3, and SH2B3, with four associated with T1D age at onset (PTPN22, HLA-DQB1, INS, and ERBB3). AFR and AMR meta-analysis revealed NRP1 as associated with T1D risk and age at onset, although NRP1 variants were not associated in EUR ancestry. In contrast, the PTPN22 variant was significantly associated with risk only in EUR ancestry. HLA alleles and haplotypes most significantly associated with T1D risk in AFR and AMR ancestry differed from that seen in EUR ancestry; in addition, the HLA-DRB1*08:02-DQA1*04:01-DQB1*04:02 haplotype was 'protective' in AMR while HLA-DRB1*08:01-DQA1*04:01-DQB1*04:02 haplotype was 'risk' in EUR ancestry, differing only at HLA-DRB1*08. These results suggest that much larger sample sizes in non-EUR populations are required to capture novel loci associated with T1D risk.

Characterization of the novel HLA-DQA1*01:03:11 allele by sequencing-based typing.

HLA-DQA1*01:03:11 differs from HLA-DQA1*01:03:01:02 by one nucleotide substitution in codon 59 in exon 2.

Identification of the novel HLA-DQA1*05:101 allele by next-generation sequencing in a family.

HLA-DQA1*05:101 differs from HLA-DQA1*05:01:01:02 by one nucleotide substitution at codon 221 (CGT>TGT) in exon 4.