Current status and the future for the genetics of type I diabetes

Identification of non-HLA genes associated with development of islet autoimmunity and type 1 diabetes in the prospective TEDDY cohort

Traditional linkage analysis and genome-wide association studies have identified HLA and a number of non-HLA genes as genetic factors for islet autoimmunity (IA) and type 1 diabetes (T1D). However, the relative risk associated with previously identified non-HLA genes is usually very small as measured in cases/controls from mixed populations. Genetic associations for IA and T1D may be more accurately assessed in prospective cohorts. In this study, 5806 subjects from the TEDDY (The Environmental Determinants of Diabetes in the Young) study, an international prospective cohort study, were genotyped for 176,586 SNPs on the ImmunoChip. Cox proportional hazards analyses were performed to discover the SNPs associated with the risk for IA, T1D, or both. Three regions were associated with the risk of developing any persistent confirmed islet autoantibody: one known region near SH2B3 (HR = 1.35, p = 3.58 × 10-7) with Bonferroni-corrected significance and another known region near PTPN22 (HR = 1.46, p = 2.17 × 10-6) and one novel region near PPIL2 (HR = 2.47, p = 9.64 × 10-7) with suggestive evidence (p < 10-5). Two known regions (PTPN22: p = 2.25 × 10-6, INS; p = 1.32 × 10-7) and one novel region (PXK/PDHB: p = 8.99 × 10-6) were associated with the risk for multiple islet autoantibodies. First appearing islet autoantibodies differ with respect to association. Two regions (INS: p = 5.67 × 10-6 and TTC34/PRDM16: 6.45 × 10-6) were associated if the fist appearing autoantibody was IAA and one region (RBFOX1: p = 8.02 × 10-6) was associated if the first appearing autoantibody was GADA. The analysis of T1D identified one region already known to be associated with T1D (INS: p = 3.13 × 10-7) and three novel regions (RNASET2, PLEKHA1, and PPIL2; 5.42 × 10-6 > p > 2.31 × 10-6). These results suggest that a number of low frequency variants influence the risk of developing IA and/or T1D and these variants can be identified by large prospective cohort studies using a survival analysis approach.

Prediction and prevention of type 1 diabetes: update on success of prediction and struggles at prevention

Type 1 diabetes mellitus (T1DM) is the archetypal example of a T cell-mediated autoimmune disease characterized by selective destruction of pancreatic β cells. The pathogenic equation for T1DM presents a complex interrelation of genetic and environmental factors, most of which have yet to be identified. On the basis of observed familial aggregation of T1DM, it is certain that there is a decided heritable genetic susceptibility for developing T1DM. The well-known association of T1DM with certain human histocompatibility leukocyte antigen (HLA) alleles of the major histocompatibility complex (MHC) was a major step toward understanding the role of inheritance in T1DM. Type 1 diabetes is a polygenic disease with a small number of genes having large effects (e.g., HLA) and a large number of genes having small effects. Risk of T1DM progression is conferred by specific HLA DR/DQ alleles [e.g., DRB1*03-DQB1*0201 (DR3/DQ2) or DRB1*04-DQB1*0302 (DR4/DQ8)]. In addition, the HLA allele DQB1*0602 is associated with dominant protection from T1DM in multiple populations. A concordance rate lower than 100% between monozygotic twins indicates a potential involvement of environmental factors on disease development. The detection of at least two islet autoantibodies in the blood is virtually pre-diagnostic for T1DM. The majority of children who carry these biomarkers, regardless of whether they have an a priori family history of the disease, will develop insulin-requiring diabetes. Facilitating pre-diagnosis is the timing of seroconversion which is most pronounced in the first 2 yr of life. Unfortunately the significant progress in improving prediction of T1DM has not yet been paralleled by safe and efficacious intervention strategies aimed at preventing the disease. Herein we summarize the chequered history of prediction and prevention of T1DM, describing successes and failures alike, and thereafter examine future trends in the exciting, partially explored field of T1DM prevention.

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.

Epigenetic regulation of the immune system in health and disease

Epigenetics comprises various mechanisms that mold chromatin structures and regulate gene expression with stability, thus defining cell identity and function and adapting cells to environmental changes. Alteration of these mechanisms contributes to the inception of various pathological conditions. Given the complexity of the immune system, one would predict that a higher-order, supragenetic regulation is indispensable for generation of its constituents and control of its functions. Here, we summarize various aspects of immune system physiology and pathology in which epigenetic pathways have been implicated. Increasing knowledge in this field, together with the development of specific tools with which to manipulate epigenetic pathways, might form a basis for new strategies of immune function modulation, both to optimize immune therapies for infections or cancer and to control immune alterations in aging or autoimmunity.