The Type I Diabetes Genetics Consortium 'Rapid Response' family-based candidate gene study: strategy, genes selection, and main outcome

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.

TCF1 and LEF1 Control Treg Competitive Survival and Tfr Development to Prevent Autoimmune Diseases

CD4⁺ Foxp3⁺ T regulatory (Treg) cells are key players in preventing lethal autoimmunity. Tregs undertake differentiation processes and acquire diverse functional properties. However, how Treg’s differentiation and functional specification are regulated remains incompletely understood. Here, we report that gradient expression of TCF1 and LEF1 distinguishes Tregs into three distinct subpopulations, particularly highlighting a subset of activated Treg (aTreg) cells. Treg-specific ablation of TCF1 and LEF1 renders the mice susceptible to systemic autoimmunity. TCF1 and LEF1 are dispensable for Treg’s suppressive capacity but essential for maintaining a normal aTreg pool and promoting Treg’s competitive survival. As a consequence, the development of T follicular regulatory (Tfr) cells, which are a subset of aTreg, is abolished in TCF1/LEF1-conditional knockout mice, leading to unrestrained T follicular helper (Tfh) and germinal center B cell responses. Thus, TCF1 and LEF1 act redundantly to control the maintenance and functional specification of Treg subsets to prevent autoimmunity.

Transcriptional regulation of Treg differentiation and function remains incompletely understood. Yang et al. report that two TCF family transcription factors regulate the survival and functional specification of a subset of Treg cells to prevent autoimmunity.

Precision medicine in diabetes prevention, classification and management

Diabetes has become a major burden of healthcare expenditure. Diabetes management following a uniform treatment algorithm is often associated with progressive treatment failure and development of diabetic complications. Recent advances in our understanding of the genomic architecture of diabetes and its complications have provided the framework for development of precision medicine to personalize diabetes prevention and management. In the present review, we summarized recent advances in the understanding of the genetic basis of diabetes and its complications. From a clinician's perspective, we attempted to provide a balanced perspective on the utility of genomic medicine in the field of diabetes. Using genetic information to guide management of monogenic forms of diabetes represents the best-known examples of genomic medicine for diabetes. Although major strides have been made in genetic research for diabetes, its complications and pharmacogenetics, ongoing efforts are required to translate these findings into practice by incorporating genetic information into a risk prediction model for prioritization of treatment strategies, as well as using multi-omic analyses to discover novel drug targets with companion diagnostics. Further research is also required to ensure the appropriate use of this information to empower individuals and healthcare professionals to make personalized decisions for achieving the optimal outcome.

Antigen presentation in the autoimmune diabetes of the NOD mouse

This paper reviews the presentation of peptides by major histocompatibility complex (MHC) class II molecules in the autoimmune diabetes of the nonobese diabetic (NOD) mouse. Islets of Langerhans contain antigen-presenting cells that capture the proteins and peptides of the beta cells' secretory granules. Peptides bound to I-A(g7), the unique MHC class II molecule of NOD mice, are presented in islets and in pancreatic lymph nodes. The various beta cell-derived peptides interact with selected CD4 T cells to cause inflammation and beta cell demise. Many autoreactive T cells are found in NOD mice, but not all have a major role in the initiation of the autoimmune process. I emphasize here the evidence pointing to insulin autoreactivity as a seminal component in the diabetogenic process.

Receiver operating characteristic analysis of HLA, CTLA4, and insulin genotypes for type 1 diabetes

OBJECTIVE

This study assessed the ability to distinguish between type 1 diabetes-affected individuals and their unaffected relatives using HLA and single nucleotide polymorphism (SNP) genotypes.

RESEARCH DESIGN AND METHODS

Eight models, ranging from only the high-risk DR3/DR4 genotype to all significantly associated HLA genotypes and two SNPs mapping to the cytotoxic T-cell-associated antigen-4 gene (CTLA4) and insulin (INS) genes, were fitted to high-resolution class I and class II HLA genotyping data for patients from the Type 1 Diabetes Genetics Consortium collection. Pairs of affected individuals and their unaffected siblings were divided into a "discovery" (n = 1,015 pairs) and a "validation" set (n = 318 pairs). The discriminating performance of various combinations of genetic information was estimated using receiver operating characteristic (ROC) curve analysis.

RESULTS

The use of only the presence or absence of the high-risk DR3/DR4 genotype achieved very modest discriminating ability, yielding an area under the curve (AUC) of 0.62 in the discovery set and 0.59 in the validation set. The full model-which included HLA information from the class II loci DPB1, DRB1, and DQB1; selected alleles from HLA class I loci A and B; and SNPs from the CTLA4 and INS genes-increased the AUC to 0.74 in the discovery set and to 0.71 in the validation set. A cost-effective alternative is proposed, using genotype information equivalent to typing four SNPs (DR3, DR4-DQB1*03:02, CTLA-4, and INS), which achieved an AUC of 0.72 in the discovery set and 0.69 in the validation set.

CONCLUSIONS

Genotyping data sufficient to tag DR3, DR4-DQB1*03:02, CTLA4, and INS were shown to distinguish between subjects with type 1 diabetes and their unaffected siblings adequately to achieve clinically utility to identify children in multiplex families to be considered for early intervention.

Tests for genetic interactions in type 1 diabetes: linkage and stratification analyses of 4,422 affected sib-pairs

OBJECTIVE

Interactions between genetic and environmental factors lead to immune dysregulation causing type 1 diabetes and other autoimmune disorders. Recently, many common genetic variants have been associated with type 1 diabetes risk, but each has modest individual effects. Familial clustering of type 1 diabetes has not been explained fully and could arise from many factors, including undetected genetic variation and gene interactions.

RESEARCH DESIGN AND METHODS

To address this issue, the Type 1 Diabetes Genetics Consortium recruited 3,892 families, including 4,422 affected sib-pairs. After genotyping 6,090 markers, linkage analyses of these families were performed, using a novel method and taking into account factors such as genotype at known susceptibility loci.

RESULTS

Evidence for linkage was robust at the HLA and INS loci, with logarithm of odds (LOD) scores of 398.6 and 5.5, respectively. There was suggestive support for five other loci. Stratification by other risk factors (including HLA and age at diagnosis) identified one convincing region on chromosome 6q14 showing linkage in male subjects (corrected LOD = 4.49; replication P = 0.0002), a locus on chromosome 19q in HLA identical siblings (replication P = 0.006), and four other suggestive loci.

CONCLUSIONS

This is the largest linkage study reported for any disease. Our data indicate there are no major type 1 diabetes subtypes definable by linkage analyses; susceptibility is caused by actions of HLA and an apparently random selection from a large number of modest-effect loci; and apart from HLA and INS, there is no important susceptibility factor discoverable by linkage methods.

Personalized Medicine in Diabetes

BACKGROUND

Multiple genes that are associated with the risk of developing diabetes or the risk of diabetes complications have been identified by candidate gene analysis and genomewide scanning. These molecular markers, together with clinical data and findings from proteomics, metabolomics, pharmacogenetics, and other methods, lead to a consideration of the extent to which personalized approaches can be applied to the treatment of diabetes mellitus.

CONTENT

Known genes that cause monogenic subtypes of diabetes are reviewed, and several examples are discussed in which the genotype of an individual with diabetes can direct considerations of preferred choices for glycemic therapy. The extent of characterization of polygenic determinants of type 1 and type 2 diabetes is summarized, and the potential for using this information in personalized management of glycemia and complications in diabetes is discussed. The application and current limitations of proteomic and metabolomic methods in elucidating diabetes heterogeneity is reviewed.

SUMMARY

There is established heterogeneity in the determinants of diabetes and the risk of diabetes complications. Understanding the basis of this heterogeneity provides an opportunity for personalizing prevention and treatment strategies according to individual patient clinical and molecular characteristics. There is evidence-based support for benefits from a personalized approach to diabetes care in patients with certain monogenic forms of diabetes. It is anticipated that strategies for individualized treatment decisions in the more common forms of diabetes will emerge with expanding knowledge of polygenic factors and other molecular determinants of disease.

The pancreas in human type 1 diabetes

Type 1 diabetes (T1D) is considered a disorder whose pathogenesis is autoimmune in origin, a notion drawn in large part from studies of human pancreata performed as far back as the 1960s. While studies of the genetics, epidemiology, and peripheral immunity in T1D have been subject to widespread analysis over the ensuing decades, efforts to understand the disorder through analysis of human pancreata have been far more limited. We have reviewed the published literature pertaining to the pathology of the human pancreas throughout all stages in the natural history of T1D. This effort uncovered a series of findings that challenge many dogmas ascribed to T1D and revealed data suggesting the marked heterogeneity in terms of its pathology. An improved understanding and appreciation for pancreatic pathology in T1D could lead to improved disease classification, an understanding of why the disorder occurs, and better therapies for disease prevention and management.

Overview of the Rapid Response data

The Type I Diabetes Genetics Consortium (T1DGC) Rapid Response Workshop was established to evaluate published candidate gene associations in a large collection of affected sib-pair (ASP) families. We report on our quality control (QC) and preliminary family-based association analyses. A random sample of blind duplicates was analyzed for QC. Quality checks, including examination of plate-panel yield, marker yield, Hardy-Weinberg equilibrium, mismatch error rate, Mendelian error rate, and allele distribution across plates, were performed. Genotypes from 2324 families within nine cohorts were obtained from a panel of 21 candidate genes, including 384 single-nucleotide polymorphisms on two genotyping platforms performed at the Broad Institute Center for Genotyping and Analysis (Cambridge, MA, USA). The T1DGC Rapid Response project, following rigorous QC procedures, resulted in a 2297 family, 9688 genotyped individual database on a single-candidate gene panel. The available data include 9005 individuals with genotype data from both platforms and 683 individuals genotyped (276 in Illumina; 407 in Sequenom) on only one platform.

Overview of the Type I Diabetes Genetics Consortium

The Type I Diabetes Genetics Consortium (T1DGC) is an international, multicenter research program with two primary goals. The first goal is to identify genomic regions and candidate genes whose variants modify an individual's risk of type I diabetes (T1D) and help explain the clustering of the disease in families. The second goal is to make research data available to the research community and to establish resources that can be used by, and that are fully accessible to, the research community. To facilitate the access to these resources, the T1DGC has developed a Consortium Agreement (http://www.t1dgc.org) that specifies the rights and responsibilities of investigators who participate in Consortium activities. The T1DGC has assembled a resource of affected sib-pair families, parent-child trios, and case-control collections with banks of DNA, serum, plasma, and EBV-transformed cell lines. In addition, both candidate gene and genome-wide (linkage and association) studies have been performed and displayed in T1DBase (http://www.t1dbase.org) for all researchers to use in their own investigations. In this supplement, a subset of the T1DGC collection has been used to investigate earlier published candidate genes for T1D, to confirm the results from a genome-wide association scan for T1D, and to determine associations with candidate genes for other autoimmune diseases or with type II diabetes that may be involved with beta-cell function.