HLA class II genotyping of African American type 1 diabetic patients reveals associations unique to African haplotypes

Fifty years of HLA-associated type 1 diabetes risk: history, current knowledge, and future directions

More than 50 years have elapsed since the association of human leukocyte antigens (HLA) with type 1 diabetes (T1D) was first reported. Since then, methods for identification of HLA have progressed from cell based to DNA based, and the number of recognized HLA variants has grown from a few to tens of thousands. Current genotyping methodology allows for exact identification of all HLA-encoding genes in an individual's genome, with statistical analysis methods evolving to digest the enormous amount of data that can be produced at an astonishing rate. The HLA region of the genome has been repeatedly shown to be the most important genetic risk factor for T1D, and the original reported associations have been replicated, refined, and expanded. Even with the remarkable progress through 50 years and over 5,000 reports, a comprehensive understanding of all effects of HLA on T1D remains elusive. This report represents a summary of the field as it evolved and as it stands now, enumerating many past and present challenges, and suggests possible paradigm shifts for moving forward with future studies in hopes of finally understanding all the ways in which HLA influences the pathophysiology of T1D.

Does HLA explain the high incidence of childhood-onset type 1 diabetes in the Canary Islands? The role of Asp57 DQB1 molecules

The Canary Islands inhabitants, a recently admixed population with significant North African genetic influence, has the highest incidence of childhood-onset type 1 diabetes (T1D) in Spain and one of the highest in Europe. HLA accounts for half of the genetic risk of T1D.

AIMS

To characterize the classical HLA-DRB1 and HLA-DQB1 alleles in children from Gran Canaria with and without T1D.

METHODS

We analyzed classic HLA-DRB1 and HLA-DQB1 alleles in childhood-onset T1D patients (n = 309) and control children without T1D (n = 222) from the island of Gran Canaria. We also analyzed the presence or absence of aspartic acid at position 57 in the HLA-DQB1 gene and arginine at position 52 in the HLA-DQA1 gene. Genotyping of classical HLA-DQB1 and HLA-DRB1 alleles was performed at two-digit resolution using Luminex technology. The chi-square test (or Fisher's exact test) and odds ratio (OR) were computed to assess differences in allele and genotype frequencies between patients and controls. Logistic regression analysis was also used.

RESULTS

Mean age at diagnosis of T1D was 7.4 ± 3.6 years (46% female). Mean age of the controls was 7.6 ± 1.1 years (55% female). DRB1*03 (OR = 4.2; p = 2.13-13), DRB1*04 (OR = 6.6; p ≤ 2.00-16), DRB1* 07 (OR = 0.37; p = 9.73-06), DRB1*11 (OR = 0.17; p = 6.72-09), DRB1*12, DRB1*13 (OR = 0.38; p = 1.21-05), DRB1*14 (OR = 0.0; p = 0.0024), DRB1*15 (OR = 0.13; p = 7.78-07) and DRB1*16 (OR = 0.21; p = 0.003) exhibited significant differences in frequency between groups. Among the DQB1* alleles, DQB1*02 (OR: 2.3; p = 5.13-06), DQB1*03 (OR = 1.7; p = 1.89-03), DQB1*05 (OR = 0.64; p = 0.027) and DQB1*06 (OR = 0.19; p = 6.25-14) exhibited significant differences. A total of 58% of the studied HLA-DQB1 genes in our control population lacked aspartic acid at position 57.

CONCLUSIONS

In this population, the overall distributions of the HLA-DRB1 and HLA-DQB1 alleles are similar to those in other European populations. However, the frequency of the non-Asp-57 HLA-DQB1 molecules is greater than that in other populations with a lower incidence of T1D. Based on genetic, historical and epidemiological data, we propose that a common genetic background might help explain the elevated pediatric T1D incidence in the Canary Islands, North-Africa and middle eastern countries.

HLA investigation in ICI-induced T1D and isolated ACTH deficiency including meta-analysis

OBJECTIVE

Widespread use of immune checkpoint inhibitors (ICIs) in cancer treatment has led to an increase in the number of reported cases of immunotherapy-related endocrinopathies. This study aimed to analyze and compare human leukocyte antigen (HLA) signatures associated with ICI-induced type 1 diabetes (ICI-T1D) and isolated adrenocorticotropic hormone deficiency (ICI-IAD) in patients with both conditions.

METHODS

HLA signatures were examined for their frequencies of occurrence in 22 patients with ICI-T1D without concurrent IAD, including 16 patients from nationwide reports (ICI-T1D group) and 14 patients with ICI-IAD without concurrent T1D (ICI-IAD group). The HLA signatures were also compared for their respective frequencies in 11 patients with ICI-T1D and ICI-IAD, including eight from nationwide reports (ICI-T1D/IAD group).

RESULTS

In the ICI-T1D group, HLA-DRB1*09:01-DQB1*03:03 and DQA1*03:02, which are in linkage disequilibrium with DRB1*09:01-DQB1*03:03 and DRB1*13:02-DQB1*06:04, were susceptible to ICI-T1D, whereas DRB1*15:02-DQB1*06:01 was protective against ICI-T1D. In the ICI-IAD group, DPB1*09:01, C*12:02-B*52:01, and DRB1*15:02-DRB1*06:01, which are in strong linkage disequilibrium, were associated with susceptibility to ICI-IAD. Moreover, DRB1*15:02-DRB1*06:01 was not detected in the ICI-T1D/IAD group.

CONCLUSIONS

This study revealed specific HLA signatures associated with ICI-T1D and ICI-IAD. Moreover, HLA-DRB1*15:02-DRB1*06:01, an ICI-IAD-susceptible HLA haplotype, coincides with the ICI-T1D-protective HLA haplotype, suggesting that the presence of DRB1*15:02-DRB1*06:01 may protect against the co-occurrence of T1D in patients with ICI-IAD.

Immune responses to gut bacteria associated with time to diagnosis and clinical response to T cell-directed therapy for type 1 diabetes prevention

Immune-targeted therapies have efficacy for treatment of autoinflammatory diseases. For example, treatment with the T cell-specific anti-CD3 antibody teplizumab delayed disease onset in participants at high risk for type 1 diabetes (T1D) in the TrialNet 10 (TN-10) trial. However, heterogeneity in therapeutic responses in TN-10 and other immunotherapy trials identifies gaps in understanding disease progression and treatment responses. The intestinal microbiome is a potential source of biomarkers associated with future T1D diagnosis and responses to immunotherapy. We previously reported that antibody responses to gut commensal bacteria were associated with T1D diagnosis, suggesting that certain antimicrobial immune responses may help predict disease onset. Here, we investigated anticommensal antibody (ACAb) responses against a panel of taxonomically diverse intestinal bacteria species in sera from TN-10 participants before and after teplizumab or placebo treatment. We identified IgG2 responses to three species that were associated with time to T1D diagnosis and with teplizumab treatment responses that delayed disease onset. These antibody responses link human intestinal bacteria with T1D progression, adding predictive value to known T1D risk factors. ACAb analysis provides a new approach to elucidate heterogeneity in responses to immunotherapy and identify individuals who may benefit from teplizumab, recently approved by the U.S. Food and Drug Administration for delaying T1D onset.

Association between alleles, haplotypes, and amino acid variations in HLA class II genes and type 1 diabetes in Kuwaiti children

Type 1 diabetes (T1D) is a complex autoimmune disorder that is highly prevalent globally. The interactions between genetic and environmental factors may trigger T1D in susceptible individuals. HLA genes play a significant role in T1D pathogenesis, and specific haplotypes are associated with an increased risk of developing the disease. Identifying risk haplotypes can greatly improve the genetic scoring for early diagnosis of T1D in difficult to rank subgroups. This study employed next-generation sequencing to evaluate the association between HLA class II alleles, haplotypes, and amino acids and T1D, by recruiting 95 children with T1D and 150 controls in the Kuwaiti population. Significant associations were identified for alleles at the HLA-DRB1, HLA-DQA1, and HLA-DQB1 loci, including DRB1*03:01:01, DQA1*05:01:01, and DQB1*02:01:01, which conferred high risk, and DRB1*11:04:01, DQA1*05:05:01, and DQB1*03:01:01, which were protective. The DRB1*03:01:01~DQA1*05:01:01~DQB1*02:01:01 haplotype was most strongly associated with the risk of developing T1D, while DRB1*11:04-DQA1*05:05-DQB1*03:01 was the only haplotype that rendered protection against T1D. We also identified 66 amino acid positions across the HLA-DRB1, HLA-DQA1, and HLA-DQB1 genes that were significantly associated with T1D, including novel associations. These results validate and extend our knowledge on the associations between HLA genes and T1D in Kuwaiti children. The identified risk alleles, haplotypes, and amino acid variations may influence disease development through effects on HLA structure and function and may allow early intervention via population-based screening efforts.

A genomic data archive from the Network for Pancreatic Organ donors with Diabetes

The Network for Pancreatic Organ donors with Diabetes (nPOD) is the largest biorepository of human pancreata and associated immune organs from donors with type 1 diabetes (T1D), maturity-onset diabetes of the young (MODY), cystic fibrosis-related diabetes (CFRD), type 2 diabetes (T2D), gestational diabetes, islet autoantibody positivity (AAb+), and without diabetes. nPOD recovers, processes, analyzes, and distributes high-quality biospecimens, collected using optimized standard operating procedures, and associated de-identified data/metadata to researchers around the world. Herein describes the release of high-parameter genotyping data from this collection. 372 donors were genotyped using a custom precision medicine single nucleotide polymorphism (SNP) microarray. Data were technically validated using published algorithms to evaluate donor relatedness, ancestry, imputed HLA, and T1D genetic risk score. Additionally, 207 donors were assessed for rare known and novel coding region variants via whole exome sequencing (WES). These data are publicly-available to enable genotype-specific sample requests and the study of novel genotype:phenotype associations, aiding in the mission of nPOD to enhance understanding of diabetes pathogenesis to promote the development of novel therapies.

Lessons and Gaps in the Prediction and Prevention of Type 1 Diabetes

Type 1 diabetes (T1D) is a serious chronic autoimmune condition. Even though the root cause of T1D development has yet to be determined, enough is known about the natural history of T1D pathogenesis to allow study of interventions that may delay or even prevent the onset of hyperglycemia and clinical T1D. Primary prevention aims to prevent the onset of beta cell autoimmunity in asymptomatic people at high genetic risk for T1D. Secondary prevention strategies aim to preserve functional beta cells once autoimmunity is present, and tertiary prevention aims to initiate and extend partial remission of beta cell destruction after the clinical onset of T1D. The approval of teplizumab in the United States to delay the onset of clinical T1D marks an impressive milestone in diabetes care. This treatment opens the door to a paradigm shift in T1D care. People with T1D risk need to be identified early by measuring T1D related islet autoantibodies. Identifying people with T1D before they have symptoms will facilitate better understanding of pre-symptomatic T1D progression and T1D prevention strategies that may be effective.

Association between HLA Class II Alleles/Haplotypes and Genomic Ancestry in Brazilian Patients with Type 1 Diabetes: A Nationwide Exploratory Study

We aimed to identify HLA-DRB1, -DQA1, and -DQB1 alleles/haplotypes associated with European, African, or Native American genomic ancestry (GA) in admixed Brazilian patients with type 1 diabetes (T1D). This exploratory nationwide study enrolled 1599 participants. GA percentage was inferred using a panel of 46 ancestry informative marker-insertion/deletion. Receiver operating characteristic curve analysis (ROC) was applied to identify HLA class II alleles related to European, African, or Native American GA, and showed significant (p < 0.05) accuracy for identifying HLA risk alleles related to European GA: for DRB1*03:01, the area under the curve was (AUC) 0.533; for DRB1*04:01 AUC = 0.558, for DRB1*04:02 AUC = 0.545. A better accuracy for identifying African GA was observed for the risk allele DRB1*09:01AUC = 0.679 and for the protective alleles DRB1*03:02 AUC = 0.649, DRB1*11:02 AUC = 0.636, and DRB1*15:03 AUC = 0.690. Higher percentage of European GA was observed in patients with risk haplotypes (p < 0.05). African GA percentage was higher in patients with protective haplotypes (p < 0.05). Risk alleles and haplotypes were related to European GA and protective alleles/haplotypes to African GA. Future studies with other ancestry markers are warranted to fill the gap in knowledge regarding the genetic origin of T1D in highly admixed populations such as that found in Brazil.

The phenotype of type 1 diabetes in sub-Saharan Africa

The phenotype of type 1 diabetes in Africa, especially sub-Saharan Africa, is poorly understood. Most previously conducted studies have suggested that type 1 diabetes may have a different phenotype from the classical form of the disease described in western literature. Making an accurate diagnosis of type 1 diabetes in Africa is challenging, given the predominance of atypical diabetes forms and limited resources. The peak age of onset of type 1 diabetes in sub-Saharan Africa seems to occur after 18-20 years. Multiple studies have reported lower rates of islet autoantibodies ranging from 20 to 60% amongst people with type 1 diabetes in African populations, lower than that reported in other populations. Some studies have reported much higher levels of retained endogenous insulin secretion than in type 1 diabetes elsewhere, with lower rates of type 1 diabetes genetic susceptibility and HLA haplotypes. The HLA DR3 appears to be the most predominant HLA haplotype amongst people with type 1 diabetes in sub-Saharan Africa than the HLA DR4 haplotype. Some type 1 diabetes studies in sub-Saharan Africa have been limited by small sample sizes and diverse methods employed. Robust studies close to diabetes onset are sparse. Large prospective studies with well-standardized methodologies in people at or close to diabetes diagnosis in different population groups will be paramount to provide further insight into the phenotype of type 1 diabetes in sub-Saharan Africa.

Type 1 diabetes in diverse ancestries and the use of genetic risk scores

Over 75 genetic loci within and outside of the HLA region influence type 1 diabetes risk. Genetic risk scores (GRS), which facilitate the integration of complex genetic information, have been developed in type 1 diabetes and incorporated into models and algorithms for classification, prognosis, and prediction of disease and response to preventive and therapeutic interventions. However, the development and validation of GRS across different ancestries is still emerging, as is knowledge on type 1 diabetes genetics in populations of diverse genetic ancestries. In this Review, we provide a summary of the current evidence on the evolutionary genetic variation in type 1 diabetes and the racial and ethnic differences in type 1 diabetes epidemiology, clinical characteristics, and preclinical course. We also discuss the influence of genetics on type 1 diabetes with differences across ancestries and the development and validation of GRS in various populations.

HLA Genotypes and Type 1 Diabetes and Its Relationship to Reported Race/Skin Color in Their Relatives: A Brazilian Multicenter Study

We aimed to investigate the relationship between HLA alleles in patients with type 1 diabetes from an admixed population and the reported race/skin color of their relatives. This cross-sectional, multicenter study was conducted in public clinics in nine Brazilian cities and included 662 patients with type 1 diabetes and their relatives. Demographic data for patients and information on the race/skin color and birthplace of their relatives were obtained. Typing of the HLA-DRB1, -DQA1, and -DQB1 genes was performed. Most studied patients reported having a White relative (95.17%), and the most frequently observed allele among them was DRB1*03:01. Increased odds of presenting this allele were found only in those patients who reported having all White relatives. Considering that most of the patients reported having a White relative and that the most frequent observed allele was DRB1*03:01 (probably a European-derived allele), regardless of the race/skin color of their relatives, we conclude that the type 1 diabetes genotype comes probably from European, Caucasian ethnicity. However, future studies with other ancestry markers are needed to fill the knowledge gap regarding the genetic origin of the type 1 diabetes genotype in admixed populations such as the Brazilian.

HLA-DRB1 and -DQB1 Alleles, Haplotypes and Genotypes in Emirati Patients with Type 1 Diabetes Underscores the Benefits of Evaluating Understudied Populations

Background: HLA class II (DR and DQ) alleles and antigens have historically shown strong genetic predisposition to type 1 diabetes (T1D). This study evaluated the association of DRB1 and DQB1 alleles, genotypes, and haplotypes with T1D in United Arab Emirates. Materials and Methods: Study subjects comprised 149 patients with T1D, and 147 normoglycemic control subjects. Cases and controls were Emiratis and were HLA-DRB1 and -DQB1 genotyped using sequence-based typing. Statistical analysis was performed using Bridging Immunogenomic Data-Analysis Workflow Gaps R package. Results: In total, 15 DRB1 and 9 DQB1 alleles were identified in the study subjects, of which the association of DRB1*03:01, DRB1*04:02, DRB1*11:01, DRB1*16:02, and DQB1*02:01, DQB1*03:02, DQB1*03:01, and DQB1*06:01 with altered risk of T1D persisted after correcting for multiple comparisons. Two-locus haplotype analysis identified DRB1*03:01∼DQB1*02:01 [0.44 vs. 0.18, OR (95% CI) = 3.44 (2.33-5.1), Pc = 3.48 × 10-10]; DRB1*04:02∼DQB1*03:02 [0.077 vs. 0.014, OR = 6.06 (2.03-24.37), Pc = 2.3 × 10-3] and DRB1*04:05∼DQB1*03:02 [0.060 vs. 0.010, OR = 6.24 (1.79-33.34), Pc = 0.011] as positively associated, and DRB1*16:02∼DQB1*05:02 [0.024 vs. 0.075, OR = 0.3 (0.11-0.74), Pc = 0.041] as negatively associated with T1D, after applying Bonferroni correction. Furthermore, the highest T1D risk was observed for DR3/DR4 [0.104 vs. 0.006, OR = 25.03 (8.23-97.2), Pc = 2.6 × 10-10], followed by DR3/DR3 [0.094 vs. 0.010, OR = 8.72 (3.17-25.32), Pc = 3.18 × 10-8] diplotypes. Conclusion: While DRB1 and DQB1 alleles and haplotypes associated with T1D in Emiratis showed similarities to Caucasian and non-Caucasian populations, several alleles and haplotypes associated with T1D in European, African, and Asian populations, were not observed. This underscores the contribution of ethnic diversity and possible diverse associations between DRB1 and DQB1 and T1D across different populations.

Partners in Crime: Beta-Cells and Autoimmune Responses Complicit in Type 1 Diabetes Pathogenesis

Type 1 diabetes (T1D) is an autoimmune disease characterized by autoreactive T cell-mediated destruction of insulin-producing pancreatic beta-cells. Loss of beta-cells leads to insulin insufficiency and hyperglycemia, with patients eventually requiring lifelong insulin therapy to maintain normal glycemic control. Since T1D has been historically defined as a disease of immune system dysregulation, there has been little focus on the state and response of beta-cells and how they may also contribute to their own demise. Major hurdles to identifying a cure for T1D include a limited understanding of disease etiology and how functional and transcriptional beta-cell heterogeneity may be involved in disease progression. Recent studies indicate that the beta-cell response is not simply a passive aspect of T1D pathogenesis, but rather an interplay between the beta-cell and the immune system actively contributing to disease. Here, we comprehensively review the current literature describing beta-cell vulnerability, heterogeneity, and contributions to pathophysiology of T1D, how these responses are influenced by autoimmunity, and describe pathways that can potentially be exploited to delay T1D.

Class II Human Leukocyte Antigen Variants Associate With Risk of Pegaspargase Hypersensitivity

We conducted the first human leukocyte antigen (HLA) allele and genome-wide association study to identify loci associated with hypersensitivity reactions exclusively to the PEGylated preparation of asparaginase (pegaspargase) in racially diverse cohorts of pediatric leukemia patients: St Jude Children's Research Hospital's Total XVI (TXVI, n = 598) and Children's Oncology Group AALL0232 (n = 2,472) and AALL0434 (n = 1,189). Germline DNA was genotyped using arrays. Genetic variants not genotyped directly were imputed. HLA alleles were imputed using SNP2HLA or inferred using BWAkit. Analyses between genetic variants and hypersensitivity were performed in each cohort first using cohort-specific covariates and then combined using meta-analyses. Nongenetic risk factors included fewer intrathecal injections (P = 2.7 × 10-5 in TXVI) and male sex (P = 0.025 in AALL0232). HLA alleles DQB1*02:02, DRB1*07:01, and DQA1*02:01 had the strongest associations with pegaspargase hypersensitivity (P < 5.0 × 10-5 ) in patients with primarily European ancestry (EA), with the three alleles associating in a single haplotype. The top allele HLA-DQB1*02:02 was tagged by HLA-DQB1 rs1694129 in EAs (r2  = 0.96) and less so in non-EAs. All single nucleotide polymorphisms associated with pegaspargase hypersensitivity reaching genome-wide significance in EAs were in class II HLA loci, and were partially replicated in non-EAs, as is true for other HLA associations. The rs9958628 variant, in ARHGAP28 (previously linked to immune response in children) had the strongest genetic association (P = 8.9 × 10-9 ) in non-EAs. The HLA-DQB1*02:02-DRB1*07:01-DQA1*02:01 associated with hypersensitivity reactions to pegaspargase is the same haplotype associated with reactions to non-PEGylated asparaginase, even though the antigens differ between the two preparations.

Clinical features, biochemistry, and HLA-DRB1 status in youth-onset type 1 diabetes in Sudan

OBJECTIVE

To further understand clinical and biochemical features, and HLA-DRB1 genotypes, in new cases of diabetes in Sudanese children and adolescents.

RESEARCH DESIGN AND METHODS

Demographic characteristics, clinical information, and biochemical parameters (blood glucose, HbA1c, C-peptide, autoantibodies against glutamic acid decarboxylase 65 [GADA] and insulinoma-associated protein-2 [IA-2A], and HLA-DRB1) were assessed in 99 individuals <18 years, recently (<18 months) clinically diagnosed with T1D. HLA-DRB1 genotypes for 56 of these Arab individuals with T1D were compared to a mixed control group of 198 healthy Arab (75%) and African (25%) individuals without T1D.

RESULTS

Mean ± SD age at diagnosis was 10.1 ± 4.3 years (range 0.7-17.6 years) with mode at 9-12 years. A female preponderance was observed. Fifty-two individuals (55.3%) presented in diabetic ketoacidosis (DKA). Mean ± SD serum fasting C-peptide values were 0.22 ± 0.25 nmol/L (0.66±0.74 ng/ml). 31.3% were autoantibody negative, 53.4% were GADA positive, 27.2% were IA-2A positive, with 12.1% positive for both autoantibodies. Association analysis compared to 198 controls of similar ethnic origin revealed strong locus association with HLA-DRB1 (p < 2.4 × 10-14 ). Five HLA-DRB1 alleles exhibited significant T1D association: three alleles (DRB1*03:01, DRB1*04:02, and DRB1*04:05) were positively associated, while three (DRB1*10:01, DRB1*15:02, and DRB1*15:03) were protective. DRB1*03:01 had the strongest association (odds ratio = 5.04, p = 1.7 × 10-10 ).

CONCLUSIONS

Young Sudanese individuals with T1D generally have similar characteristics to reported European-origin T1D populations. However, they have higher rates of DKA and slightly lower autoantibody rates than reported European-origin populations, and a particularly strong association with HLA-DRB1*03:01.

Identification of a subgroup of black South Africans with type 1 diabetes who are older at diagnosis but have lower levels of glutamic acid decarboxylase and islet antigen 2 autoantibodies

AIMS

To compare the age at diagnosis and prevalence of islet autoantibody [glutamic acid decarboxylase (65 kDa) 65 and islet antigen 2] positivity in black and white participants with type 1 diabetes in South Africa, and to analyse the relationship between age at diagnosis and the presence of autoantibodies.

METHODS

Participants were recruited from diabetes outpatient departments and autoantibodies to glutamic acid decarboxylase (65 kDa) and islet antigen 2 were measured by enzyme-linked immunosorbent assay.

RESULTS

We recruited 472 (353 black and 119 white) participants with type 1 diabetes. Age at diagnosis of diabetes was later in black (19.7 ± 10.5) than in white participants (12.7 ± 10.8 years; P < 0.001) with a median (interquartile range) disease duration of 5.0 (2.0-10.0) and 8.5 (4.0-20.0) years (P < 0.001), respectively. An older age at diagnosis (≥ 21 years) was more frequent in black (152 of 340, 45%) than in white participants (24 of 116, 21%; P < 0.001). The prevalence of islet antigen 2 autoantibodies was 19% (66/352) in black and 41% in white participants (48/118; P < 0.001). There was no significant difference in glutamic acid decarboxylase (65 kDa) autoantibody positivity between black (212/353, 60%) and white participants (77/117, 66%; P = 0.269). In black, but not white, participants the prevalence of both glutamic acid decarboxylase (65 kDa) and islet antigen 2 autoantibody positivity was significantly lower in participants diagnosed at age ≥ 21 years (P < 0.001 for both comparisons).

CONCLUSIONS

The older age at diagnosis, lower prevalence of islet antigen 2 autoantibodies and a distinct subgroup of participants with type 1 diabetes with age at diagnosis of > 20 years in the black compared to white population suggest a difference in the immunological aetiology of type 1 diabetes in these two population groups.

Association of HLA-DR-DQ alleles, haplotypes, and diplotypes with type 1 diabetes in Saudis

AIMS

Type 1 diabetes (T1D) is an autoimmune disease that affects many children worldwide. Genetic factors and environmental triggers play crucial interacting roles in the aetiology. This study aimed to assess the contribution of HLA-DRB1-DQA1-DQB1 alleles, haplotypes, and genotypes to the risk of T1D among Saudis.

METHODS

A total of 222 children with T1D and 342 controls were genotyped for HLA-DRB1, -DQA1, and -DQB1 using reverse sequence-specific oligonucleotide (rSSO) Lab Type high definition (HD) kits. Alleles, haplotypes, and diplotypes were compared between cases and controls using the SAS statistical package.

RESULTS

DRB1*03:01-DQA1*05:01-DQB1*02:01 (32.4%; OR = 3.68; P_c < .0001), DRB1*04:05-DQA1*03:02-DQB1*03:02 (6.6%; OR = 6.76; P_c < .0001), DRB1*04:02-DQA1*03:01-DQB1*03:02 (6.0%; OR = 3.10; P_c = .0194), DRB1*04:01-DQA1*03:01-DQB1*03:02 (3.7%; OR = 4.22; P_c = .0335), and DRB1*04:05-DQA1*03:02-DQB1*02:02 (2.7%; OR = 6.31; P_c = .0326) haplotypes were significantly increased in cases compared to controls, whereas DRB1*07:01-DQA1*02:01-DQB1*02:02 (OR = 0.41; P_c = .0001), DRB1*13:01-DQA1*01:03-DQB1*06:03 (OR = 0.05; P_c < .0001), DRB1*15:01-DQA1*01:02-DQB1*06:02 (OR = 0.03; P_c < .0001), and DRB1*11:01-DQA1*05:05-DQB1*03:01 (OR = 0.07; P_c = .0291) were significantly decreased. Homozygous DRB1*03:01-DQA1*05:01-DQB1*02:01 genotypes and combinations of DRB1*03:01-DQA1*05:01-DQB1*02:01 with DRB1*04:05-DQA1*03:02-DQB1*03:02, DRB1*04:02-DQA1*03:01-DQB1*03:02, and DRB1*04:01-DQA1*03:01-DQB1*03:02 were significantly increased in cases than controls. Combinations of DRB1*03:01-DQA1*05:01-DQB1*02:01 with DRB1*07:01-DQA1*02:01-DQB1*02:02 and DRB1*13:02-DQA1*01:02-DQB1*06:04 showed low OR values but did not remain significantly decreased after Bonferroni correction.

CONCLUSIONS

HLA-DRB1-DQA1-DQB1 alleles, haplotypes, and diplotypes in Saudis with T1D are not markedly different from those observed in Western and Middle-Eastern populations but are quite different than those of East Asians.

Next steps in the identification of gene targets for type 1 diabetes

The purpose of this review is to provide a view of the future of genomics and other omics approaches in defining the genetic contribution to all stages of risk of type 1 diabetes and the functional impact and clinical implementations of the associated variants. From the recognition nearly 50 years ago that genetics (in the form of HLA) distinguishes risk of type 1 diabetes from type 2 diabetes, advances in technology and sample acquisition through collaboration have identified over 60 loci harbouring SNPs associated with type 1 diabetes risk. Coupled with HLA region genes, these variants account for the majority of the genetic risk (~50% of the total risk); however, relatively few variants are located in coding regions of genes exerting a predicted protein change. The vast majority of genetic risk in type 1 diabetes appears to be attributed to regions of the genome involved in gene regulation, but the target effectors of those genetic variants are not readily identifiable. Although past genetic studies clearly implicated immune-relevant cell types involved in risk, the target organ (the beta cell) was left untouched. Through emergent technologies, using combinations of genetics, gene expression, epigenetics, chromosome conformation and gene editing, novel landscapes of how SNPs regulate genes have emerged. Furthermore, both the immune system and the beta cell and their biological pathways have been implicated in a context-specific manner. The use of variants from immune and beta cell studies distinguish type 1 diabetes from type 2 diabetes and, when they are combined in a genetic risk score, open new avenues for prediction and treatment. Graphical abstract.

Dog leucocyte antigen (DLA) class II haplotypes and risk of canine diabetes mellitus in specific dog breeds

Background

Canine diabetes mellitus (DM) is a common endocrine disease in domestic dogs. A number of pathological mechanisms are thought to contribute to the aetiopathogenesis of relative or absolute insulin deficiency, including immune-mediated destruction of pancreatic beta cells. DM risk varies considerably between different dog breeds, suggesting that genetic factors are involved and contribute susceptibility or protection. Associations of particular dog leucocyte antigen (DLA) class II haplotypes with DM have been identified, but investigations to date have only considered all breeds pooled together. The aim of this study was to analyse an expanded data set so as to identify breed-specific diabetes-associated DLA haplotypes.

Methods

The 12 most highly represented breeds in the UK Canine Diabetes Register were selected for study. DLA-typing data from 646 diabetic dogs and 912 breed-matched non-diabetic controls were analysed to enable breed-specific analysis of the DLA. Dogs were genotyped for allelic variation at DLA-DRB1, -DQA1, -DQB1 loci using DNA sequence-based typing. Genotypes from all three loci were combined to reveal three-locus DLA class II haplotypes, which were evaluated for statistical associations with DM. This was performed for each breed individually and for all breeds pooled together.

Results

Five dog breeds were identified as having one or more DLA haplotype associated with DM susceptibility or protection. Four DM-associated haplotypes were identified in the Cocker Spaniel breed, of which one haplotype was shared with Border Terriers. In the three breeds known to be at highest risk of DM included in the study (Samoyed, Tibetan Terrier and Cairn Terrier), no DLA haplotypes were found to be associated with DM.

Conclusions

Novel DLA associations with DM in specific dog breeds provide further evidence that immune response genes contribute susceptibility to this disease in some cases. It is also apparent that DLA may not be contributing obvious or strong risk for DM in some breeds, including the seven breeds analysed for which no associations were identified.

Distribution of HLA-DQ risk genotypes for celiac disease in Ethiopian children

Most patients with celiac disease are positive for either HLA‐DQA1*05:01‐DQB1*02 (DQ2.5) or DQA1*03:01‐DQB1*03:02 (DQ8). Remaining few patients are usually DQA1*02:01‐DQB1*02 (DQ2.2) carriers. Screenings of populations with high frequencies of these HLA‐DQA1‐DQB1 haplotypes report a 1% to 3% celiac disease prevalence. The aim was to determine the prevalence of HLA‐DQ risk haplotypes for celiac disease in Ethiopian children. Dried blood spots collected from 1193 children from the Oromia regional state of Ethiopia were genotyped for HLA‐DQA1 and DQB1 genotyping using an asymmetric polymerase chain reaction (PCR) and a subsequent hybridization of allele‐specific probes. As references, 2000 previously HLA‐genotyped children randomly selected from the general population in Sweden were included. DQ2.2 was the most common haplotype and found in 15.3% of Ethiopian children, which was higher compared with 6.7% of Swedish references (P < .0001). Opposed to this finding, DQ2.5 and DQ8 occurred in 9.7% and 6.8% of Ethiopian children, which were less frequent compared with 12.8% and 13.1% of Swedish references, respectively (P < .0001). The DQ2.5‐trans genotype encoded by DQA1*05‐DQB1*03:01 in combination with DQ2.2 occurred in 3.6% of Ethiopian children, which was higher compared with 1.3% of Swedish references (P < .0001). However, when children with moderate high to very high‐risk HLA genotypes were grouped together, there was no difference between Ethiopian children and Swedish references (27.4% vs 29.0%) (P = .3504). The frequency of HLA risk haplotypes for celiac disease is very similar in Ethiopian and Swedish children. This finding of importance will be useful in future screening of children for celiac disease in Ethiopia.

Protection or susceptibility to devastating childhood epilepsy: Nodding Syndrome associates with immunogenetic fingerprints in the HLA binding groove

Nodding syndrome (NS) is a devastating and enigmatic childhood epilepsy. NS is accompanied by multiple neurological impairments and neuroinflammation, and associated with the parasite Onchocerca volvulus (Ov) and other environmental factors. Moreover, NS seems to be an ‘Autoimmune Epilepsy’ since: 1. ~50% of NS patients have neurotoxic cross-reactive Ov/Leimodin-I autoimmune antibodies. 2. Our recently published findings: Most (~86%) of NS patients have glutamate-receptor AMPA-GluR3B peptide autoimmune antibodies that bind, induce Reactive Oxygen Species, and kill both neural cells and T cells. Furthermore, NS patient’s IgG induce seizures, brain multiple damage alike occurring in brains of NS patients, and elevation of T cells and activated microglia and astrocytes, in brains of normal mice. Human Leukocyte antigen (HLA) class I and II molecules are critical for initiating effective beneficial immunity against foreign microorganisms and contributing to proper brain function, but also predispose to detrimental autoimmunity against self-peptides. We analyzed seven HLA loci, either by next-generation-sequencing or Sequence-Specific-Oligonucleotide-Probe, in 48 NS patients and 51 healthy controls from South Sudan. We discovered that NS associates significantly with both protective HLA haplotype: HLA-B*42:01, C*17:01, DRB1*03:02, DQB1*04:02 and DQA1*04:01, and susceptible motif: Ala24, Glu63 and Phe67, in the HLA-B peptide-binding groove. These amino acids create a hydrophobic and sterically closed peptide-binding HLA pocket, favoring proline residue. Our findings suggest that immunogenetic fingerprints in HLA peptide-binding grooves tentatively associate with protection or susceptibility to NS. Accordingly, different HLA molecules may explain why under similar environmental factors, only some children, within the same families, tribes and districts, develop NS, while others do not.

Nodding syndrome (NS) is a devastating and mysterious neurological disorder affecting 5–15 years old children, primarily in Sudan, Uganda and Tanzania. NS strongly associates with an infection with the parasitic worm Oncocherca Volvulus (Ov), transmitted by the black fly, affecting many people worldwide. Moreover, NS is most probably an 'Autoimmune Epilepsy', especially in view of our recent findings that NS patient’s autoimmune GluR3B antibodies induce ROS and kill both neural cells and T cells. NS patient’s IgG also induce seizures, multiple brain damage and inflammation-inducing cells in the brain. HLA class I genes are expressed on the surface of all nucleated cells and present peptides to cytotoxic CD8⁺ T cells. HLA class II genes are expressed mainly on the surface of antigen presenting cells and present peptides to helper CD4+ T cells. Analysis of HLA of South-Sudanese NS patients and healthy controls revealed that that few amino acids in HLA peptide-binding grooves associate with either protection or susceptibility to NS. Theses amino acids could be critical in NS by affecting beneficial immunity and/or detrimental autoimmunity.

Genetic profile considerations for induction of allogeneic chimerism as a therapeutic approach for type 1 diabetes mellitus

The major therapeutic modality for type 1 diabetes mellitus (T1DM) remains sustaining euglycemia by exogenous administration of insulin. Based on a new understanding of bone marrow structural and functional dynamics, a conditioning-free bone marrow transplantation (BMT), with reduced adverse effects, opens the possibility for evaluating β cell regeneration and restoration of euglycemia by induction of allogeneic chimerism in patients T1DM, as shown in a mouse model. With this therapeutic modality, donor bone marrow (BM) selection based on T1DM-predisposing and preventive phenotypes will improve treatment outcomes by limiting the risk of exacerbating the autoimmune processes in the BM recipient.

HLA class II genotyping of admixed Brazilian patients with type 1 diabetes according to self-reported color/race in a nationwide study

The HLA region is responsible for almost 50% of the genetic risk of type 1 diabetes (T1D). However, haplotypes and their effects on risk or protection vary among different ethnic groups, mainly in an admixed population. We aimed to evaluate the HLA class II genetic profile of Brazilian individuals with T1D and its relationship with self-reported color/race. This was a nationwide multicenter study conducted in 10 Brazilian cities. We included 1,019 T1D individuals and 5,116 controls matched for the region of birth and self-reported color/race. Control participants belonged to the bone marrow transplant donor registry of Brazil (REDOME). HLA-class II alleles (DRB1, DQA1, and DQB1) were genotyped using the SSO and NGS methods. The most frequent risk and protection haplotypes were HLA~DRB1*03:01~DQA1*05:01 g~DQB1*02:01 (OR 5.8, p < 0.00001) and HLA~DRB1*07:01~DQA1*02:01~DQB1*02:02 (OR 0.54, p < 0.0001), respectively, regardless of self-reported color/race. Haplotypes HLA~DRB1*03:01~DQA1*05:01 g~DQB1*02:01 and HLA~DRB1*04:02~DQA1*03:01 g~DQB1*03:02 were more prevalent in the self-reported White group than in the Black group (p = 0.04 and p = 0.02, respectively). The frequency of haplotype HLA~DRB1*09:01~DQA1*03:01 g~DQB1*02:02 was higher in individuals self-reported as Black than White (p = <0.00001). No difference between the Brazilian geographical regions was found. Individuals with T1D presented differences in frequencies of haplotypes within self-reported color/race, but the more prevalent haplotypes, regardless of self-reported color/race, were the ones described previously in Europeans. We hypothesize that, in the T1D population of Brazil, although highly admixed, the disease risk alleles come mostly from Europeans as a result of centuries of colonization and migration.

Comprehensive meta-analysis reveals an association of the HLA-DRB1*1602 allele with autoimmune diseases mediated predominantly by autoantibodies

The human leukocytes antigen (HLA)-DRB1*16:02 allele has been suggested to be associated with many autoimmune diseases. However, a validation of the results of the different studies by a comprehensive analysis of the corresponding meta data is lacking. In this study, we performed a meta-analysis of the association between HLA-DRB1*16:02 allele with various autoimmune disorders. Our analysis shows that HLA-DRB1*16:02 allele was associated with systemic lupus erythematosus, anti-N-Methyl-d-Aspartate receptor (NMDAR) encephalitis, Graves' disease, myasthenia gravis, neuromyelitis optica and antibody-associated systemic vasculitis with microscopic polyangiitis (AASV-MPA). However, no such association was found for multiple sclerosis, autoimmune hepatitis type 1, rheumatoid arthritis, type 1 diabetes and Vogt-Koyanagi-Harada syndrome. Re-analysis of the studies after their categorization into autoantibody-dependent and T cell-dependent autoimmune diseases revealed that the HLA-DRB1*16:02 allele was strongly associated with disorder predominantly mediated by autoantibodies (OR = 1.93; 95% CI = 1.63-2.28, P = 1.95 × 10^-14) but not with those predominantly mediated by T cells (OR = 1.08; 95% CI = 0.87-1.34, P = .474). In addition, amino acid sequence alignment of common HLA-DRB1 subtypes demonstrated that HLA-DRB1*16:02 carries a unique motif of amino acid residues at position 67-74 which encodes the third hypervariable region. Taken together, the distinct pattern of disease association and the unique amino acid sequence of the third hypervariable region of the HLA-DRB1 provide some hints on how HLA-DRB1*16:02 is involved in the pathogenesis of autoimmune diseases.

Clinical features, biochemistry and HLA-DRB1 status in children and adolescents with diabetes in Dhaka, Bangladesh

AIMS

Little information is published on diabetes in young people in Bangladesh. We aimed to investigate the demographic, clinical, and biochemical features, and HLA-DRB1 alleles in new cases of diabetes affecting Bangladeshi children and adolescents <22 years of age.

METHODS

The study was conducted at Bangladesh Institute of Research and Rehabilitation of Diabetes, Endocrine and Metabolic Disorders (BIRDEM) in Dhaka. One hundred subjects aged <22 years at diagnosis were enrolled. Demographic characteristics, clinical information, biochemical parameters (blood glucose, HbA1c, C-peptide, and autoantibodies against glutamic acid decarboxylase 65 (GADA) and islet antigen-2 (IA-2A) were measured. High-resolution DNA genotyping was performed for HLA-DRB1.

RESULTS

Eighty-four subjects were clinically diagnosed as type 1 diabetes (T1D), seven as type 2 diabetes (T2D), and nine as fibrocalculous pancreatic disease (FCPD). Of the 84 with T1D, 37 (44%) were males and 47 (56%) females, with median age at diagnosis 13 years (y) (range 1.6-21.7) and peak age at onset 12-15 years. 85% of subjects were assessed within one month of diagnosis and all within eleven months. For subjects diagnosed with T1D, mean C-peptide was 0.46 ± 0.22 nmol/L (1.40 ± 0.59 ng/mL), with 9 (10.7%) IA-2A positive, 22 (26%) GADA positive, and 5 (6%) positive for both autoantibodies. Analysis of HLA-DRB1 genotypes revealed locus-level T1D association (p = 6.0E-05); DRB1*04:01 appeared predisposing (p < 3.0E-06), and DRB1*14:01 appeared protective (p = 1.7E-02).

CONCLUSIONS

Atypical forms of T1D appear to be more common in young people in Bangladesh than in European populations. This will be helpful in guiding more specific assessment at onset and potentially, expanding treatment options.

Human Leukocyte Antigen (HLA) and Islet Autoantibodies Are Tools to Characterize Type 1 Diabetes in Arab Countries: Emphasis on Kuwait

The incidence rate of type 1 diabetes in Kuwait had been increasing exponentially and has doubled in children ≤ 14 years old within almost two decades. Therefore, there is a dire need for a careful systematic familial cohort study. Several immunogenetic factors affect the pathogenesis of the disease. The human leukocyte antigen (HLA) accounts for the major genetic susceptibility to the disease. The triggering agents initiate disease onset by type 1 destruction of pancreatic β-cells. Both HLA and anti-islet antibodies can be used to characterize, predict susceptibility to the disease, innovate, or delay the β-cell destruction. Evidence from prospective longitudinal studies suggested that the underlying disease process represents a continuum that begins before the symptoms are clinically evident. Autoimmunity of the functional pancreatic β-cells results in symptomatic type 1 diabetes and lifelong insulin dependence. The autoantibodies against glutamic acid decarboxylase (GADA), insulinoma antigen-2 (IA-2A), insulin (IAA), and zinc transporter-8 (ZnT-8A) comprise the most reliable biomarkers for type 1 diabetes in both children and adults. Although Kuwait is the second among the top 10 countries with a high incidence rate of type 1 diabetes, there have been no proper diagnostic and prediction tools as per the World Health Organization. The Kuwaiti Type 1 Diabetes Study (KADS) was initiated to understand the disease pathogenesis as well as the HLA and anti-islet autoantibody profile of type 1 diabetes in Kuwait. Understanding the disease sequela in a homogenous gene pool and highly consanguineous population of Kuwaitis could help solve the challenges and pathogenesis, as well as hasten the prevention, of type 1 diabetes.

The heterogeneous pathogenesis of type 1 diabetes mellitus

Type 1 diabetes mellitus (T1DM) results from the destruction of pancreatic β-cells that is mediated by the immune system. Multiple genetic and environmental factors found in variable combinations in individual patients are involved in the development of T1DM. Genetic risk is defined by the presence of particular allele combinations, which in the major susceptibility locus (the HLA region) affect T cell recognition and tolerance to foreign and autologous molecules. Multiple other loci also regulate and affect features of specific immune responses and modify the vulnerability of β-cells to inflammatory mediators. Compared with the genetic factors, environmental factors that affect the development of T1DM are less well characterized but contact with particular microorganisms is emerging as an important factor. Certain infections might affect immune regulation, and the role of commensal microorganisms, such as the gut microbiota, are important in the education of the developing immune system. Some evidence also suggests that nutritional factors are important. Multiple islet-specific autoantibodies are found in the circulation from a few weeks to up to 20 years before the onset of clinical disease and this prediabetic phase provides a potential opportunity to manipulate the islet-specific immune response to prevent or postpone β-cell loss. The latest developments in understanding the heterogeneity of T1DM and characterization of major disease subtypes might help in the development of preventive treatments.

Type 1 Diabetes Risk in African-Ancestry Participants and Utility of an Ancestry-Specific Genetic Risk Score

OBJECTIVE

Genetic risk scores (GRS) have been developed that differentiate individuals with type 1 diabetes from those with other forms of diabetes and are starting to be used for population screening; however, most studies were conducted in European-ancestry populations. This study identifies novel genetic variants associated with type 1 diabetes risk in African-ancestry participants and develops an African-specific GRS.

RESEARCH DESIGN AND METHODS

We generated single nucleotide polymorphism (SNP) data with the ImmunoChip on 1,021 African-ancestry participants with type 1 diabetes and 2,928 control participants. HLA class I and class II alleles were imputed using SNP2HLA. Logistic regression models were used to identify genome-wide significant (P < 5.0 × 10-8) SNPs associated with type 1 diabetes in the African-ancestry samples and validate SNPs associated with risk in known European-ancestry loci (P < 2.79 × 10-5).

RESULTS

African-specific (HLA-DQA1*03:01-HLA-DQB1*02:01) and known European-ancestry HLA haplotypes (HLA-DRB1*03:01-HLA-DQA1*05:01-HLA-DQB1*02:01, HLA-DRB1*04:01-HLA-DQA1*03:01-HLA-DQB1*03:02) were significantly associated with type 1 diabetes risk. Among European-ancestry defined non-HLA risk loci, six risk loci were significantly associated with type 1 diabetes in subjects of African ancestry. An African-specific GRS provided strong prediction of type 1 diabetes risk (area under the curve 0.871), performing significantly better than a European-based GRS and two polygenic risk scores in independent discovery and validation cohorts.

CONCLUSIONS

Genetic risk of type 1 diabetes includes ancestry-specific, disease-associated variants. The GRS developed here provides improved prediction of type 1 diabetes in African-ancestry subjects and a means to identify groups of individuals who would benefit from immune monitoring for early detection of islet autoimmunity.

The genetic architecture of type 1 diabetes mellitus

Type 1 diabetes mellitus (T1D) is a complex autoimmune disorder characterised by loss of the insulin-producing pancreatic beta cells in genetically predisposed individuals, ultimately resulting in insulin deficiency and hyperglycaemia. T1D is most common among children and young adults, and the incidence is on the rise across the world. The aetiology of T1D is hypothesized to involve genetic and environmental factors that result in the T-cell mediated destruction of pancreatic beta cells. There is a strong genetic risk to T1D; with genome-wide association studies (GWAS) identifying over 60 susceptibility regions within the human genome which are marked by single nucleotide polymorphisms (SNPs). Here, we review what is currently known about the genetics of T1D. We argue that advancing our understanding of the aetiology and pathogenesis of T1D will require the integration of genome biology (omics-data) with GWAS data, thereby making it possible to elucidate the putative gene regulatory networks modulated by disease-associated SNPs. This approach has a potential to revolutionize clinical management of T1D in an era of precision medicine.

Application of a Genetic Risk Score to Racially Diverse Type 1 Diabetes Populations Demonstrates the Need for Diversity in Risk-Modeling

Prior studies identified HLA class-II and 57 additional loci as contributors to genetic susceptibility for type 1 diabetes (T1D). We hypothesized that race and/or ethnicity would be contextually important for evaluating genetic risk markers previously identified from Caucasian/European cohorts. We determined the capacity for a combined genetic risk score (GRS) to discriminate disease-risk subgroups in a racially and ethnically diverse cohort from the southeastern U.S. including 637 T1D patients, 46 at-risk relatives having two or more T1D-related autoantibodies (≥2AAb+), 790 first-degree relatives (≤1AAb+), 68 second-degree relatives (≤1 AAb+), and 405 controls. GRS was higher among Caucasian T1D and at-risk subjects versus ≤ 1AAb+ relatives or controls (P < 0.001). GRS receiver operating characteristic AUC (AUROC) for T1D versus controls was 0.86 (P < 0.001, specificity = 73.9%, sensitivity = 83.3%) among all Caucasian subjects and 0.90 for Hispanic Caucasians (P < 0.001, specificity = 86.5%, sensitivity = 84.4%). Age-at-diagnosis negatively correlated with GRS (P < 0.001) and associated with HLA-DR3/DR4 diplotype. Conversely, GRS was less robust (AUROC = 0.75) and did not correlate with age-of-diagnosis for African Americans. Our findings confirm GRS should be further used in Caucasian populations to assign T1D risk for clinical trials designed for biomarker identification and development of personalized treatment strategies. We also highlight the need to develop a GRS model that accommodates racial diversity.

Genetics and its potential to improve type 1 diabetes care

PURPOSE OF REVIEW

The genetic basis of type 1 diabetes (T1D) is being characterized through DNA sequence variation and cell type specificity. This review discusses the current understanding of the genes and variants implicated in risk of T1D and how genetic information can be used in prediction, intervention and components of clinical care.

RECENT FINDINGS

Fine mapping and functional studies has provided resolution of the heritable basis of T1D risk, incorporating novel insights on the dominant role of human leukocyte antigen (HLA) genes as well as the lesser impact of non-HLA genes. Evaluation of T1D-associated single nucleotide polymorphisms (SNPs), there is enrichment of genetic effects restricted to specific immune cell types (CD4 and CD8 T cells, CD19 B cells and CD34 stem cells), suggesting pathways to improved prediction. In addition, T1D-associated SNPs have been used to generate genetic risk scores (GRS) as a tool to distinguish T1D from type 2 diabetes (T2D) and to provide prediagnostic data to target those for autoimmunity screening (e.g. islet autoantibodies) as a prelude for continuous monitoring and entry into intervention trials.

SUMMARY

Genetic susceptibility accounts for nearly one-half of the risk for T1D. Although the T1D-associated SNPs in white populations account for nearly 90% of the genetic risk, with high sensitivity and specificity, the low prevalence of T1D makes the T1D GRS of limited utility. However, identifying those with highest genetic risk may permit early and targeted immune monitoring to diagnose T1D months prior to clinical onset.

The influence of population stratification on genetic markers associated with type 1 diabetes

Ethnic admixtures may interfere with the definition of type 1 diabetes (T1D) risk determinants. The role of HLA, PTPN22, INS-VNTR, and CTLA4 in T1D predisposition was analyzed in Brazilian T1D patients (n = 915), with 81.7% self-reporting as white and 789 controls (65.6% white). The results were corrected for population stratification by genotyping 93 ancestry informative markers (AIMs) (BeadXpress platform). Ancestry composition and structural association were characterized using Structure 2.3 and STRAT. Ethnic diversity resulted in T1D determinants that were partially discordant from those reported in Caucasians and Africans. The greatest contributor to T1D was the HLA-DR3/DR4 genotype (OR = 16.5) in 23.9% of the patients, followed by -DR3/DR3 (OR = 8.9) in 8.7%, -DR4/DR4 (OR = 4.7) in 6.0% and -DR3/DR9 (OR = 4.9) in 2.6%. Correction by ancestry also confirmed that the DRB1*09-DQB1*0202 haplotype conferred susceptibility, whereas the DRB1*07-DQB1*0202 and DRB1*11-DQB1*0602 haplotypes were protective, which is similar to reports in African-American patients. By contrast, the DRB1*07-DQB1*0201 haplotype was protective in our population and in Europeans, despite conferring susceptibility to Africans. The DRB1*10-DQB1*0501 haplotype was only protective in the Brazilian population. Predisposition to T1D conferred by PTPN22 and INS-VNTR and protection against T1D conferred by the DRB1*16 allele were confirmed. Correcting for population structure is important to clarify the particular genetic variants that confer susceptibility/protection for T1D in populations with ethnic admixtures.

Predominance of DR3 in Somali children with type 1 diabetes in the twin cities, Minnesota

BACKGROUND

Minnesota is home to the largest Somali population in USA, and pediatric diabetes teams are seeing increasing numbers of Somali children with diabetes.

OBJECTIVE

To assess the immune basis of diabetes in Somali children in the Twin Cities, Minnesota.

METHODS

A total of 31 Somali children ≤19 yr were treated for type 1 diabetes (T1D) at the University of Minnesota Masonic Children's Hospital and Children's Hospitals and Clinics of Minnesota underwent analysis of human leukocyte antigen (HLA) alleles (n = 30) and diabetes autoantibodies [glutamic acid decarboxylase (GAD65), islet antigen 2 (IA-2), zinc transporter 8 (ZnT8); n = 31]. HLA alleles were analyzed in 49 Somalis without diabetes (controls). Anti-transglutaminase autoantibodies (TGA) for celiac disease were also measured.

RESULTS

In Somali children with T1D aged 13.5 ± 5 yr (35% female, disease duration 6.5 ± 3.6 yr), the most common HLA allele was DRB1*03:01 (93%, compared with 45% of Somali controls), followed by DRB1*13:02 (27%). There was a relatively low frequency of DR4 (13%). Controls showed a similar pattern. All 31 participants were positive for at least one diabetes autoantibody. Insulin antibodies were positive in 84% (all were on insulin). Excluding insulin antibodies, 23 (74%) subjects tested positive for at least one other diabetes autoantibody; 32% had 1 autoantibody, 32% had 2 autoantibodies, and 10% had 3 autoantibodies. GAD65 autoantibodies were found in 56% of subjects, IA-2 in 29%, and ZnT8 in 26%. Four (13%) were TGA positive.

CONCLUSION

The autoantibody and HLA profiles of Somali children with diabetes are consistent with autoimmune diabetes. Their HLA profile is unique with an exceptionally high prevalence of DRB1*03:01 allele and relative paucity of DR4 alleles compared with African Americans with T1D.

Type I Interferon Is a Catastrophic Feature of the Diabetic Islet Microenvironment

A detailed understanding of the molecular pathways and cellular interactions that result in islet beta cell (β cell) destruction is essential for the development and implementation of effective therapies for prevention or reversal of type 1 diabetes (T1D). However, events that define the pathogenesis of human T1D have remained elusive. This gap in our knowledge results from the complex interaction between genetics, the immune system, and environmental factors that precipitate T1D in humans. A link between genetics, the immune system, and environmental factors are type 1 interferons (T1-IFNs). These cytokines are well known for inducing antiviral factors that limit infection by regulating innate and adaptive immune responses. Further, several T1D genetic risk loci are within genes that link innate and adaptive immune cell responses to T1-IFN. An additional clue that links T1-IFN to T1D is that these cytokines are a known constituent of the autoinflammatory milieu within the pancreas of patients with T1D. The presence of IFNα/β is correlated with characteristic MHC class I (MHC-I) hyperexpression found in the islets of patients with T1D, suggesting that T1-IFNs modulate the cross-talk between autoreactive cytotoxic CD8⁺ T lymphocytes and insulin-producing pancreatic β cells. Here, we review the evidence supporting the diabetogenic potential of T1-IFN in the islet microenvironment.

Tissue distribution and clonal diversity of the T and B cell repertoire in type 1 diabetes

The adaptive immune repertoire plays a critical role in type 1 diabetes (T1D) pathogenesis. However, efforts to characterize B cell and T cell receptor (TCR) profiles in T1D subjects have been largely limited to peripheral blood sampling and restricted to known antigens. To address this, we collected pancreatic draining lymph nodes (pLN), "irrelevant" nonpancreatic draining lymph nodes, peripheral blood mononuclear cells (PBMC), and splenocytes from T1D subjects (n = 18) and control donors (n = 9) as well as pancreatic islets from 1 T1D patient; from these tissues, we collected purified CD4⁺ conventional T cells (Tconv), CD4⁺ Treg, CD8⁺ T cells, and B cells. By conducting high-throughput immunosequencing of the TCR β chain (TRB) and B cell receptor (BCR) immunoglobulin heavy chain (IGH) on these samples, we sought to analyze the molecular signature of the lymphocyte populations within these tissues and of T1D. Ultimately, we observed a highly tissue-restricted CD4⁺ repertoire, while up to 24% of CD8⁺ clones were shared among tissues. We surveyed our data set for previously described proinsulin- and glutamic acid decarboxylase 65-reactive (GAD65-reactive) receptors, and interestingly, we observed a TRB with homology to a known GAD65-reactive TCR (clone GAD4.13) present in 7 T1D donors (38.9%), representing >25% of all productive TRB within Tconv isolated from the pLN of 1 T1D subject. These data demonstrate diverse receptor signatures at the nucleotide level and enriched autoreactive clones at the amino acid level, supporting the utility of coupling immunosequencing data with knowledge of characterized autoreactive receptors.

High prevalence of antithyroid peroxidase and antiparietal cell antibodies among patients with type 1 diabetes mellitus attending a tertiary diabetes centre in South Africa

OBJECTIVE

Data on the prevalence of autoimmune thyroid disease (AITD) and gastric autoimmunity in type 1 diabetes mellitus (T1DM) in Africa are limited. The aim of this study was to assess the prevalence of antithyroid peroxidase (TPO-A) and antiparietal cell antibody (PCA) in patients with T1DM at a tertiary diabetes clinic in Durban, South Africa.

RESEARCH DESIGN AND METHODS

This was a cross-sectional observational study among subjects attending the adult T1DM clinic at Inkosi Albert Luthuli Hospital. Information about history and clinical examination was collected. Blood tests included glutamic acid decarboxylase antibody (GADA), TPO-A, PCA, vitamin B₁₂, folate, ferritin, thyroid stimulating hormone (TSH), free thyroxine, lipids and HbA1c.

RESULTS

A total of 202 (M:F, 90:112) patients were recruited. The ethnic composition was African (black) (56.4%; n=114), Indian (31.7%; n=64), white (4.5%; n=9) and coloured (mixed race) (7.4%; n=15). Mean age and mean duration of diabetes were 26.4±11.4 and 10.7±9.1 years, respectively. Mean body mass index was 21.6±6.3 kg/m². GADA was positive in 63.37% (n=128). The prevalence of TPO-A was 18.9% (n=39) and PCA 8.9% (n=17). The prevalence of overt hypothyroidism, subclinical hypothyroidism and Graves' disease was 10.9%, 2.5% and 1.5%, respectively; vitamin B₁₂ deficiency was noted in 3.5% (n=7) and iron deficiency in 19.3% (n=39).

CONCLUSIONS

Among patients with T1DM in this study, there was a high prevalence of coexistent AITD and gastric autoimmunity. Screening for hypothyroidism and thyroid autoimmunity should be undertaken in all patients at initial presentation. However, to assess the feasibility and optimal timing of subsequent testing in the African setting with limited resources, more collaborative research with longitudinal studies is required.

The relationships between HLA class II alleles and antigens with gestational diabetes mellitus: A meta-analysis

Gestational diabetes mellitus (GDM) is defined as glucose intolerance with onset or first recognition during pregnancy. It is associated with an increased risk of pregnancy complications. Susceptibility to GDM is partly determined by genetics and linked with type 1 diabetes-associated high risk HLA class II genes. However, the evidence for this relationship is still highly controversial. In this study, we assessed the relationship between HLA class II variants and GDM. We performed meta-analysis on all of literatures available in PubMed, Embase, Web of Science and China National Knowledge Infrastructure databases. The odds ratio and 95% confidence interval of each variant were estimated. All statistical analyses were conducted using the Comprehensive Meta Analysis 2.2.064 software. At the allelic analysis, DQB1*02, DQB1*0203, DQB1*0402, DQB1*0602, DRB1*03, DRB1*0301 and DRB1*1302 reached a nominal level of significance, and only DQB1*02, DQB1*0602 and DRB1*1302 were statistically significant after Bonferroni correction. At the serological analysis, none of DQ2, DQ6, DR13 and DR17 was statistically significant following Bonferroni correction although they reached a nominal level of significance. In sum, our meta-analysis demonstrated that there were the associations between HLA class II variants and GDM but more studies are required to elucidate how these variants contribute to GDM susceptibility.

HLA-DQ genotypes - but not immune markers - differ by ethnicity in patients with childhood onset type 1 diabetes residing in Belgium

AIM

The aim of this study was to compare genetic (HLA-DQ) and immune markers in a large population of type 1 diabetic (T1D) children and adolescents residing in the same environment, but of different ethnic origin: European Caucasians (EC), Moghrabin Caucasians (MC), Black Africans (BA) and of Mixed Origin (MO).

METHODS

Retrospective study, including 452 patients with T1D aged 0.1-17.5 yr at diagnosis recruited at the Diabetology Clinic of the University Children's Hospital Queen Fabiola from May 1995 to March 2013. HLA-DQ genotyping, diabetes-associated autoantibodies, organ-specific autoantibodies, and other markers of autoimmunity were studied.

RESULTS

The proportion of the different ethnic groups was: 55% EC, 35% MC, 6% BA, and 4% MO. Between these four groups, there were no significant differences concerning age, hemoglobin A1c (HbA1c), presence of diabetic ketoacidosis, random C-peptide level at diagnosis and 2 yr later. The two most frequent haplotypes were DQA1*0501-DQB1*0201 and DQA1*0301-DQB1*0302 with a significant higher prevalence in MC and EC (p = 0.002 and 0.03, respectively). The high-risk heterozygous genotype DQA1*0301-DQB1*0302/DQA1*0501-DQB1*0201 was more frequent in EC than in MC, whereas the homozygous genotype DQA1*0501-DQB1*0201/DQA1*0501-DQB1*0201 was more prevalent in MC (p = 0.019). These susceptible genotypes were more frequent in youngest patients (p = 0.003). Diabetes-associated autoantibodies, organ-specific autoantibodies, and other immune markers did not statistically differ between ethnic groups.

CONCLUSIONS

These observations in a large population of T1D children and adolescents of different ethnic groups residing in Belgium show significant differences in HLA-DQ status, but not in diabetes-associated autoantibodies, organ-specific autoantibodies, or other immune markers.

Concordance of next generation sequence-based and sequence specific oligonucleotide probe-based HLA-DRB1 genotyping

Next generation sequencing (NGS) of clonally amplified DNA, using Roche 454 technology, was used to genotype HLA-DRB1, DRB3, DRB4, and DRB5 loci (exon 2 only) from a set of 993 samples from newborns with maternally-reported African American ancestry. DRB1 exon 2 was genotyped previously on the same sample set using sequence-specific oligonucleotide probe (SSOP) technology. Comparison of the genotype calls from both methods indicated concordance of 92.3%. Some discordance was expected due to the higher resolution of NGS data, compared to SSOP data. This resulted from selection of the incorrect allele from the ambiguity string produced by SSOP genotyping. Of 76 discordant genotypes, only three were due to resolution of ambiguity with the NGS method. The low percent of changes due to the increased resolution of the NGS method instills confidence in the overall value of previous data genotyped with moderate resolution methods, i.e., the vast majority of alleles present in a population are those that are detectable at moderate resolution. The remaining 73 discordant genotypes resulted from preventable errors in sample handling, data interpretation, and data entry. These results underscore the potential for error that can result from factors such as low quality genomic DNA, manual data entry, and interpretation of marginal genotyping results. Optimization of genomic DNA quality, automation of genotyping steps wherever possible, and use of the highest resolution technology available can lead to dramatic improvements in HLA genotype data quality. NGS-based methodology generated data of superior quality and accuracy compared to the SSOP system.

Associations of human leukocyte antigens with autoimmune diseases: challenges in identifying the mechanism

The mechanism of genetic associations between human leukocyte antigen (HLA) and susceptibility to autoimmune disorders has remained elusive for most of the diseases, including rheumatoid arthritis (RA) and type 1 diabetes (T1D), for which both the genetic associations and pathogenic mechanisms have been extensively analyzed. In this review, we summarize what are currently known about the mechanisms of HLA associations with RA and T1D, and elucidate the potential mechanistic basis of the HLA-autoimmunity associations. In RA, the established association between the shared epitope (SE) and RA risk has been explained, at least in part, by the involvement of SE in the presentation of citrullinated peptides, as confirmed by the structural analysis of DR4-citrullinated peptide complex. Self-peptide(s) that might explain the predispositions of variants at 11β and 13β in DRB1 to RA risk have not currently been identified. Regarding the mechanism of T1D, pancreatic self-peptides that are presented weakly on the susceptible HLA allele products are recognized by self-reactive T cells. Other studies have revealed that DQ proteins encoded by the T1D susceptible DQ haplotypes are intrinsically unstable. These findings indicate that the T1D susceptible DQ haplotypes might confer risk for T1D by facilitating the formation of unstable HLA-self-peptide complex. The studies of RA and T1D reveal the two distinct mechanistic basis that might operate in the HLA-autoimmunity associations. Combination of these mechanisms, together with other functional variations among the DR and DQ alleles, may generate the complex patterns of DR-DQ haplotype associations with autoimmunity.

Immunogenetics of type 1 diabetes: A comprehensive review

Type 1 diabetes (T1D) results from the autoimmune destruction of insulin-producing beta cells in the pancreas. Prevention of T1D will require the ability to detect and modulate the autoimmune process before the clinical onset of disease. Genetic screening is a logical first step in identification of future patients to test prevention strategies. Susceptibility to T1D includes a strong genetic component, with the strongest risk attributable to genes that encode the classical Human Leukocyte Antigens (HLA). Other genetic loci, both immune and non-immune genes, contribute to T1D risk; however, the results of decades of small and large genetic linkage and association studies show clearly that the HLA genes confer the most disease risk and protection and can be used as part of a prediction strategy for T1D. Current predictive genetic models, based on HLA and other susceptibility loci, are effective in identifying the highest-risk individuals in populations of European descent. These models generally include screening for the HLA haplotypes "DR3" and "DR4." However, genetic variation among racial and ethnic groups reduces the predictive value of current models that are based on low resolution HLA genotyping. Not all DR3 and DR4 haplotypes are high T1D risk; some versions, rare in Europeans but high frequency in other populations, are even T1D protective. More information is needed to create predictive models for non-European populations. Comparative studies among different populations are needed to complete the knowledge base for the genetics of T1D risk to enable the eventual development of screening and intervention strategies applicable to all individuals, tailored to their individual genetic background. This review summarizes the current understanding of the genetic basis of T1D susceptibility, focusing on genes of the immune system, with particular emphasis on the HLA genes.

Risk genes and autoantibodies in Egyptian children with type 1 diabetes - low frequency of autoantibodies in carriers of the HLA-DRB1*04:05-DQA1*03-DQB1*02 risk haplotype

BACKGROUND

The study aimed to define the frequencies of type 1 diabetes-associated gene polymorphisms and their associations with various diabetes-associated autoantibodies in Egyptian children.

METHODS

One hundred and one children with type 1 diabetes and 160 healthy controls from the same region were studied for HLA-DQB1, HLA-DQA1, and HLA-DRB1 (DR4 subtypes) alleles; for INS and protein tyrosine phosphatase, non-receptor type 22 gene polymorphisms (rs689 and rs2476601); and for diabetes-associated autoantibodies.

RESULTS

Most children with diabetes (77.2%) were positive for the HLA-(DR3)-DQA1*05-DQB1*02 (DR3-DQ2) haplotype compared with 26.2% of the controls (OR = 9.5; p < 0.001). HLA-DRB1*04:02-DQA1*03-DQB1*03:02 (DR4-DQ8) (26.7%, OR = 3.3; p < 0.001), DRB1*04:05-DQA1*03-DQB1*02 (DR4-DQ2) (23.8%, OR 5.2; p < 0.001), and DRB1*04:05-DQA1*03-DQB1*03:02 (DR4-DQ8) (8.9%, OR = 7.7; p = 0.007) were also significantly increased. HLA-(DR15)-DQB1*06:01, (DR13)-DQB1*06:03, and DRB1*04:03-DQA1*03-DQB1*03:02 were the most protective haplotypes with OR values from 0.04 to 0.06. Patients positive for DR3-DQ2 but negative for DR4 haplotypes had a high frequency of glutamic acid decarboxylase antibodies (78%; p < 0.001 versus other genotypes), but only 26.6% of those with DR3-DQ2/DR4-DQ2 tested positive for glutamic acid decarboxylase antibodies (p = 0.006 versus other genotypes). Subjects with the DR4-DQ8 haplotype without DR3-DQ2 or DR4-DQ2 were more often positive for islet antigen-2 and zinc transporter 8 antibodies (55.5%, p = 0.007 and 55.5%, p = 0.01 respectively). The AA genotype of the INS gene was more common in patients than in controls (75.2 versus 59.5%, OR = 2.07; p = 0.018).

CONCLUSIONS

Besides a strong HLA-DR3-DQ2 association, a relatively high frequency of the DR4-DQ2 haplotype characterized the diabetic population. The low frequency of autoantibodies in children with HLA-DR4-DQ2 may indicate specific pathogenetic pathways associated with this haplotype.

Update on diabetes classification

This article highlights the difficulties in creating a definitive classification of diabetes mellitus in the absence of a complete understanding of the pathogenesis of the major forms. This brief review shows the evolving nature of the classification of diabetes mellitus. No classification scheme is ideal, and all have some overlap and inconsistencies. The only diabetes in which it is possible to accurately diagnose by DNA sequencing, monogenic diabetes, remains undiagnosed in more than 90% of the individuals who have diabetes caused by one of the known gene mutations. The point of classification, or taxonomy, of disease, should be to give insight into both pathogenesis and treatment. It remains a source of frustration that all schemes of diabetes mellitus continue to fall short of this goal.

Cell-surface MHC density profiling reveals instability of autoimmunity-associated HLA

Polymorphisms within HLA gene loci are strongly associated with susceptibility to autoimmune disorders; however, it is not clear how genetic variations in these loci confer a disease risk. Here, we devised a cell-surface MHC expression assay to detect allelic differences in the intrinsic stability of HLA-DQ proteins. We found extreme variation in cell-surface MHC density among HLA-DQ alleles, indicating a dynamic allelic hierarchy in the intrinsic stability of HLA-DQ proteins. Using the case-control data for type 1 diabetes (T1D) for the Swedish and Japanese populations, we determined that T1D risk-associated HLA-DQ haplotypes, which also increase risk for autoimmune endocrinopathies and other autoimmune disorders, encode unstable proteins, whereas the T1D-protective haplotypes encode the most stable HLA-DQ proteins. Among the amino acid variants of HLA-DQ, alterations in 47α, the residue that is located on the outside of the peptide-binding groove and acts as a key stability regulator, showed strong association with T1D. Evolutionary analysis suggested that 47α variants have been the target of positive diversifying selection. Our study demonstrates a steep allelic hierarchy in the intrinsic stability of HLA-DQ that is associated with T1D risk and protection, suggesting that HLA instability mediates the development of autoimmune disorders.

Meta-analysis of genome-wide association studies in African Americans provides insights into the genetic architecture of type 2 diabetes

Type 2 diabetes (T2D) is more prevalent in African Americans than in Europeans. However, little is known about the genetic risk in African Americans despite the recent identification of more than 70 T2D loci primarily by genome-wide association studies (GWAS) in individuals of European ancestry. In order to investigate the genetic architecture of T2D in African Americans, the MEta-analysis of type 2 DIabetes in African Americans (MEDIA) Consortium examined 17 GWAS on T2D comprising 8,284 cases and 15,543 controls in African Americans in stage 1 analysis. Single nucleotide polymorphisms (SNPs) association analysis was conducted in each study under the additive model after adjustment for age, sex, study site, and principal components. Meta-analysis of approximately 2.6 million genotyped and imputed SNPs in all studies was conducted using an inverse variance-weighted fixed effect model. Replications were performed to follow up 21 loci in up to 6,061 cases and 5,483 controls in African Americans, and 8,130 cases and 38,987 controls of European ancestry. We identified three known loci (TCF7L2, HMGA2 and KCNQ1) and two novel loci (HLA-B and INS-IGF2) at genome-wide significance (4.15 × 10(-94) Collapse

Key Words Collapse

MESH Headings

• Black or African American/genetics
• Diabetes Mellitus, Type 2/genetics
• Diabetes Mellitus, Type 2/pathology
• Genome-Wide Association Study
• HLA-B27 Antigen/genetics
• HMGA2 Protein/genetics
• Humans
• KCNQ1 Potassium Channel/genetics
• Mutant Chimeric Proteins/genetics
• Polymorphism, Single Nucleotide
• Transcription Factor 7-Like 2 Protein/genetics

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Affiliation(s)

Maggie C. Y. Ng Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Daniel Shriner Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
Brian H. Chen Program on Genomics and Nutrition, School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America Center for Metabolic Disease Prevention, School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America
Jiang Li Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Wei-Min Chen Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, United States of America
Xiuqing Guo Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
Jiankang Liu Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
Suzette J. Bielinski Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
Lisa R. Yanek The GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
Michael A. Nalls Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, United States of America
Mary E. Comeau Center for Public Health Genomics, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Laura J. Rasmussen-Torvik Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
Richard A. Jensen Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America Department of Medicine, University of Washington, Seattle, Washington, United States of America
Daniel S. Evans San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, California, United States of America
Yan V. Sun Department of Epidemiology and Biomedical Informatics, Emory University, Atlanta, Georgia, United States of America
Ping An Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
Sanjay R. Patel Division of Sleep Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
Yingchang Lu The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America The Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
Jirong Long Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
Loren L. Armstrong Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
Lynne Wagenknecht Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Lingyao Yang Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Beverly M. Snively Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Nicholette D. Palmer Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Poorva Mudgal Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Carl D. Langefeld Center for Public Health Genomics, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Keith L. Keene Department of Biology, Center for Health Disparities, East Carolina University, Greenville, North Carolina, United States of America
Barry I. Freedman Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Josyf C. Mychaleckyj Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, United States of America
Uma Nayak Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, United States of America
Leslie J. Raffel Medical Genetics Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
Mark O. Goodarzi Medical Genetics Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
Y-D Ida Chen Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
Herman A. Taylor Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America Jackson State University, Tougaloo College, Jackson, Mississippi, United States of America
Adolfo Correa Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
Mario Sims Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
David Couper Collaborative Studies Coordinating Center, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
James S. Pankow Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota, United States of America
Eric Boerwinkle Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
Adebowale Adeyemo Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
Ayo Doumatey Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
Guanjie Chen Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
Rasika A. Mathias The GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America Division of Allergy and Clinical Immunology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
Dhananjay Vaidya The GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
Andrew B. Singleton Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, United States of America
Alan B. Zonderman Laboratory of Personality and Cognition, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America
Robert P. Igo Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America
John R. Sedor Department of Medicine, Case Western Reserve University, MetroHealth System campus, Cleveland, Ohio, United States of America Department of Physiology and Biophysics, Case Western Reserve University, Cleveland, Ohio, United States of America
the FIND Consortium
Edmond K. Kabagambe Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
David S. Siscovick Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America Department of Medicine, University of Washington, Seattle, Washington, United States of America Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
Barbara McKnight Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
Kenneth Rice Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
Yongmei Liu Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Wen-Chi Hsueh Department of Medicine, University of California, San Francisco, California, United States of America
Wei Zhao Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
Lawrence F. Bielak Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
Aldi Kraja Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
Michael A. Province Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
Erwin P. Bottinger The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
Omri Gottesman The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
Qiuyin Cai Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
Wei Zheng Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
William J. Blot Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee; International Epidemiology Institute, Rockville, Maryland, United States of America
William L. Lowe Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
Jennifer A. Pacheco Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
Dana C. Crawford Center for Human Genetics Research and Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America
the eMERGE Consortium
the DIAGRAM Consortium
Elin Grundberg Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
the MuTHER Consortium
Stephen S. Rich Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
M. Geoffrey Hayes Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
Xiao-Ou Shu Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
Ruth J. F. Loos The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America The Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
Ingrid B. Borecki Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
Patricia A. Peyser Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
Steven R. Cummings San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, California, United States of America
Bruce M. Psaty Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America Department of Medicine, University of Washington, Seattle, Washington, United States of America Department of Epidemiology, University of Washington, Seattle, Washington, United States of America Department of Health Services, University of Washington, Seattle, Washington, United States of America
Myriam Fornage Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
Sudha K. Iyengar Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America
Michele K. Evans Health Disparities Unit, National Institute on Aging, National Institutes of Health, Baltimore Maryland, United States of America
Diane M. Becker The GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
W. H. Linda Kao Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
James G. Wilson Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
Jerome I. Rotter Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
Michèle M. Sale Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America Department of Medicine, University of Virginia, Charlottesville, Virginia, United States of America Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America
Simin Liu Program on Genomics and Nutrition, School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America Department of Epidemiology, University of California Los Angeles, Los Angeles, California, United States of America Departments of Epidemiology and Medicine, Brown University, Providence, Rhode Island, United States of America
Charles N. Rotimi Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
Donald W. Bowden Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
for the MEta-analysis of type 2 DIabetes in African Americans (MEDIA) Consortium

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