The chromosome 6q22.33 region is associated with age at diagnosis of type 1 diabetes and disease risk in those diagnosed under 5 years of age

AIMS/HYPOTHESIS: The genetic risk of type 1 diabetes has been extensively studied. However, the genetic determinants of age at diagnosis (AAD) of type 1 diabetes remain relatively unexplained. Identification of AAD genes and pathways could provide insight into the earliest events in the disease process. METHODS: Using ImmunoChip data from 15,696 cases, we aimed to identify regions in the genome associated with AAD. RESULTS: Two regions were convincingly associated with AAD (p  0.001), the SNP most associated with AAD, rs72975913, was associated with susceptibility to type 1 diabetes in those individuals diagnosed at less than 5 years old (p = 2.3 × 10(-9)). CONCLUSION/INTERPRETATION: PTPRK and its neighbour THEMIS are required for early development of the thymus, which we can assume influences the initiation of autoimmunity. Non-HLA genes may only be detectable as risk factors for the disease in individuals diagnosed under the age 5 years because, after that period of immune development, their role in disease susceptibility has become redundant.CW is funded by the Wellcome Trust (WT107881) and the Medical Research Council (MC_UP_1302/5). LB was supported by the Alan Turing Institute under the EPSRC grant EP/N510129/1

Circulating C-peptide levels in living children and young people and pancreatic beta cell loss in pancreas donors across type 1 diabetes disease duration.

This is the author accepted manuscript. The final version is available from the American Diabetes Association via the DOI in this record Data Availability: Further information about the data is available from the corresponding author upon request.C-peptide declines in type 1 diabetes although many long-duration patients retain low, but detectable levels. Histological analyses confirm that beta cells can remain following type 1 diabetes onset. We explored the trends observed in C-peptide decline in UK Genetic Resource Investigating Diabetes (UK GRID) cohort (N=4,079), with beta cell loss in pancreas donors from the network for Pancreatic Organ donors with Diabetes (nPOD) biobank and the Exeter Archival Diabetes Biobank (EADB) (combined N=235), stratified by recently reported age at diagnosis endotypes (< 7, 7-12, ≥ 13 years) across increasing diabetes durations. The proportion of individuals with detectable C-peptide declined beyond the first year after diagnosis, but this was most marked in the youngest age group (< 1 year duration: age < 7 years: 18/20 (90%), 7-12 years: 107/110 (97%), ≥ 13 years: 58/61 (95%) versus. 1-5 years post diagnosis: < 7 years: 172/522 (33%), 7-12 years: 604/995 (61%), ≥ 13 years: 225/289 (78%)). A similar profile was observed in beta cell loss, with those diagnosed at younger ages experiencing more rapid loss of islets containing insulin-positive (insulin+) beta cells < 1 year post diagnosis: age < 7 years: 23/26 (88%), 7-12 years: 32/33 (97%), ≥ 13 years: 22/25 (88%) versus. 1-5 years post diagnosis: < 7 years: 1/12 (8.3%) ,7-12 years: 7/13 (54%), ≥ 13 years: 7/8 (88%)). These data should be considered in the planning and interpretation of intervention trials designed to promote beta cell retention and function.Diabetes UKDiabetes UKDiabetes UKThe Leona M. & Harry B. Helmsley Charitable TrustJuvenile Diabetes Research FoundationWellcome Trus

Persistent C-peptide secretion in Type 1 diabetes and its relationship to the genetic architecture of diabetes

Background: The objective of this cross-sectional study was to explore the relationship of detectable C-peptide secretion in type 1 diabetes to clinical features and to the genetic architecture of diabetes. Methods: C-peptide was measured in an untimed serum sample in the SDRNT1BIO cohort of 6076 Scottish people with clinically diagnosed type 1 diabetes or latent autoimmune diabetes of adulthood. Risk scores at loci previously associated with type 1 and type 2 diabetes were calculated from publicly available summary statistics. Results: Prevalence of detectable C-peptide varied from 19% in those with onset before age 15 and duration greater than 15 years to 92% in those with onset after age 35 and duration less than 5 years. Twenty-nine percent of variance in C-peptide levels was accounted for by associations with male gender, late age at onset and short duration. The SNP heritability of residual C-peptide secretion adjusted for gender, age at onset and duration was estimated as 26%. Genotypic risk score for type 1 diabetes was inversely associated with detectable C-peptide secretion: the most strongly associated loci were the HLA and INS gene regions. A risk score for type 1 diabetes based on the HLA DR3 and DQ8-DR4 serotypes was strongly associated with early age at onset and inversely associated with C-peptide persistence. For C-peptide but not age at onset, there were strong associations with risk scores for type 1 and type 2 diabetes that were based on SNPs in the HLA region but not accounted for by HLA serotype. Conclusions: Persistence of C-peptide secretion varies widely in people clinically diagnosed as type 1 diabetes. C-peptide persistence is influenced by variants in the HLA region that are different from those determining risk of early-onset type 1 diabetes. Known risk loci for diabetes account for only a small proportion of the genetic effects on C-peptide persistence

An integrated multi-omics approach identifies the landscape of interferon-α-mediated responses of human pancreatic beta cells

Interferon-α (IFNα), a type I interferon, is expressed in the islets of type 1 diabetic individuals, and its expression and signaling are regulated by T1D genetic risk variants and viral infections associated with T1D. We presently characterize human beta cell responses to IFNα by combining ATAC-seq, RNA-seq and proteomics assays. The initial response to IFNα is characterized by chromatin remodeling, followed by changes in transcriptional and translational regulation. IFNα induces changes in alternative splicing (AS) and first exon usage, increasing the diversity of transcripts expressed by the beta cells. This, combined with changes observed on protein modification/degradation, ER stress and MHC class I, may expand antigens presented by beta cells to the immune system. Beta cells also up-regulate the checkpoint proteins PDL1 and HLA-E that may exert a protective role against the autoimmune assault. Data mining of the present multi-omics analysis identifies two compound classes that antagonize IFNα effects on human beta cells.This article is freely available via Open Access. Click on the Publisher URL to access it via the publisher's site.P30 DK097512/DK/NIDDK NIH HHS/United States UC4 DK104166/DK/NIDDK NIH HHS/United States MR/P010695/1/MRC_/Medical Research Council/United Kingdompublished version, accepted version, submitted versio

Approaches and advances in the genetic causes of autoimmune disease and their implications

Genome-wide association studies are transformative in revealing the polygenetic basis of common diseases, with autoimmune diseases leading the charge. Although the field is just over 10 years old, advances in understanding the underlying mechanistic pathways of these conditions, which result from a dense multifactorial blend of genetic, developmental and environmental factors, have already been informative, including insights into therapeutic possibilities. Nevertheless, the challenge of identifying the actual causal genes and pathways and their biological effects on altering disease risk remains for many identified susceptibility regions. It is this fundamental knowledge that will underpin the revolution in patient stratification, the discovery of therapeutic targets and clinical trial design in the next 20 years. Here we outline recent advances in analytical and phenotyping approaches and the emergence of large cohorts with standardized gene-expression data and other phenotypic data that are fueling a bounty of discovery and improved understanding of human physiology

Genetic variants predisposing most strongly to type 1 diabetes diagnosed under age 7 years lie near candidate genes that function in the immune system and in pancreatic β-cells

OBJECTIVE: Immunohistological analyses of pancreata from patients with type 1 diabetes suggest distinct autoimmune islet β-cell pathology between those diagnosed at <7 years (<7 group) and those diagnosed at age ≥13 years (≥13 group), with both B- and T-lymphocyte islet inflammation common in children in the <7 group, whereas B cells are rare in the ≥13 group. Based on these observations, we sought to identify differences in genetic susceptibility between these prespecified age-at-diagnosis groups to inform on the etiology of the most aggressive form of type 1 diabetes that initiates in the first years of life. RESEARCH DESIGN AND METHODS: Using multinomial logistic regression models, we tested if known type 1 diabetes loci (17 within the HLA and 55 non-HLA loci had significantly stronger effect sizes in the <7 group compared with the ≥13 group, using genotype data from 27,071 individuals (18,485 control subjects and 3,121 case subjects diagnosed at <7 years, 3,757 at 7-13 years, and 1,708 at ≥13 years). RESULTS: Six HLA haplotypes/classical alleles and six non-HLA regions, one of which functions specifically in β-cells (GLIS3) and the other five likely affecting key T-cell (IL2RA, IL10, IKZF3, and THEMIS), thymus (THEMIS), and B-cell development/functions (IKZF3 and IL10) or in both immune and β-cells (CTSH), showed evidence for stronger effects in the <7 group. CONCLUSIONS: A subset of type 1 diabetes-associated variants are more prevalent in children diagnosed under the age of 7 years and are near candidate genes that act in both pancreatic β- and immune cells

The chromosome 6q22.33 region is associated with age at diagnosis of type 1 diabetes and disease risk in those diagnosed under 5 years of age.

Aims/hypothesis The genetic risk of type 1 diabetes has been extensively studied. However, the genetic determinants of age at diagnosis (AAD) of type 1 diabetes remain relatively unexplained. Identification of AAD genes and pathways could provide insight into the earliest events in the disease process. Methods Using ImmunoChip data from 15,696 cases, we aimed to identify regions in the genome associated with AAD. Results Two regions were convincingly associated with AAD (p &lt; 5 × 10^−8): the MHC on 6p21, and 6q22.33. Fine-mapping of 6q22.33 identified two AAD-associated haplotypes in the region nearest to the genes encoding protein tyrosine phosphatase receptor kappa (PTPRK) and thymocyte-expressed molecule involved in selection (THEMIS).We examined the susceptibility to type 1 diabetes at these SNPs by performing a metaanalysis including 19,510 control participants. Although these SNPs were not associated with type 1 diabetes overall (p &gt; 0.001), the SNP most associated with AAD, rs72975913, was associated with susceptibility to type 1 diabetes in those individuals diagnosed at less than 5 years old (p = 2.3 × 10^−9). Conclusion/interpretation PTPRK and its neighbour THEMIS are required for early development of the thymus, which we can assume influences the initiation of autoimmunity. Non-HLA genes may only be detectable as risk factors for the disease in individuals diagnosed under the age 5 years because, after that period of immune development, their role in disease susceptibility has become redundant.</p

Analysis of overlapping genetic association in type 1 and type 2 diabetes

Aims/hypothesis Given the potential shared aetiology between type 1 and type 2 diabetes, we aimed to identify any genetic regions associated with both diseases. For associations where there is a shared signal and the allele that increases risk to one disease also increases risk to the other, inference about shared aetiology could be made, with the potential to develop therapeutic strategies to treat or prevent both diseases simultaneously. Alternatively, if a genetic signal co-localises with divergent effect directions, it could provide valuable biological insight into how the association affects the two diseases differently. Methods Using publicly available type 2 diabetes summary statistics from a genome-wide association study (GWAS) meta-analysis of European ancestry individuals (74,124 cases and 824,006 controls) and type 1 diabetes GWAS summary statistics from a meta-analysis of studies on individuals from the UK and Sardinia (7467 cases and 10,218 controls), we identified all regions of 0.5 Mb that contained variants associated with both diseases (false discovery rate <0.01). In each region, we performed forward stepwise logistic regression to identify independent association signals, then examined co-localisation of each type 1 diabetes signal with each type 2 diabetes signal using coloc. Any association with a co-localisation posterior probability of ≥0.9 was considered a genuine shared association with both diseases. Results Of the 81 association signals from 42 genetic regions that showed association with both type 1 and type 2 diabetes, four association signals co-localised between both diseases (posterior probability ≥0.9): (1) chromosome 16q23.1, near CTRB1/BCAR1, which has been previously identified; (2) chromosome 11p15.5, near the INS gene; (3) chromosome 4p16.3, near TMEM129 and (4) chromosome 1p31.3, near PGM1. In each of these regions, the effect of genetic variants on type 1 diabetes was in the opposite direction to the effect on type 2 diabetes. Use of additional datasets also supported the previously identified co-localisation on chromosome 9p24.2, near the GLIS3 gene, in this case with a concordant direction of effect. Conclusions/interpretation Four of five association signals that co-localise between type 1 diabetes and type 2 diabetes are in opposite directions, suggesting a complex genetic relationship between the two diseases