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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 x 10(-7)) with Bonferroni-corrected significance and another known region near PTPN22 (HR = 1.46, p = 2.17 x 10(-6)) and one novel region near PPIL2 (HR = 2.47, p = 9.64 x 10(-7)) with suggestive evidence (p < 10(-5)). Two known regions (PTPN22: p = 2.25 x 10(-6), INS; p = 1.32 x 10(-7)) and one novel region (PXK/PDHB: p = 8.99 x 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 x 10(-6) and TTC34/PROM16: 6.45 x 10(-6)) were associated if the fist appearing autoantibody was IAA and one region (RBFOXI: p = 8.02 x 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 x 10(-7)) and three novel regions (RNASET2, PLEKHA1, and PPIL2; 5.42 x 10(-6) > p > 2.31 x 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. (C) 2017 Elsevier Ltd. All rights reserved.

Islet autoantibodies detected at disease onset in patients with type 1 diabetes are signs of an autoimmune destruction of the insulin-producing β-cells. To further investigate the genetic determinants of autoantibody positivity, we performed dense immune-focused genotyping on the Immunochip array and tested for association with seven disease-specific autoantibodies in a large cross-sectional cohort of 6,160 type 1 diabetes–affected siblings. The genetic association with positivity for GAD autoantibodies (GADAs), IA2 antigen (IA-2A), zinc transporter 8, thyroid peroxidase, gastric parietal cells (PCAs), tissue transglutaminase, and 21-hydroxylase was tested using a linear mixed-model regression approach to simultaneously control for population structure and family relatedness. Four loci were associated with autoantibody positivity at genome-wide significance. Positivity for GADA was associated with 3q28/LPP, for IA-2A with 1q23/FCRL3 and 11q13/RELA, and for PCAs with 2q24/IFIH1. The 3q28 locus showed association after only 3 years duration and might therefore be a marker of persistent GADA positivity. The 1q23, 11q13, and 2q24 loci were associated with autoantibodies close to diabetes onset and constitute candidates for early screening. Major susceptibility loci for islet autoantibodies are separate from type 1 diabetes risk, which may have consequences for intervention strategies to reduce autoimmunity.

Aims/hypothesis Over 50 regions of the genome have been associated with type 1 diabetes risk, mainly using large case/control collections. In a recent genome-wide association (GWA) study, 18 novel susceptibility loci were identified and replicated, including replication evidence from 2,319 families. Here, we, the Type 1 Diabetes Genetics Consortium (T1DGC), aimed to exclude the possibility that any of the 18 loci were false-positives due to population stratification by significantly increasing the statistical power of our family study. Methods We genotyped the most disease-predicting single-nucleotide polymorphisms at the 18 susceptibility loci in 3,108 families and used existing genotype data for 2,319 families from the original study, providing 7,013 parent–child trios for analysis. We tested for association using the transmission disequilibrium test. Results Seventeen of the 18 susceptibility loci reached nominal levels of significance (p

Type 1 diabetes (T1D) and celiac disease (CeD) cluster in families and can occur in the same individual. Genetic loci have been associated with susceptibility to both diseases. Our aim was to explore the genetic differences between individuals developing both these diseases (double autoimmunity) versus those with only one. We hypothesized that double autoimmunity individuals carry more of the genetic risk markers that are shared between the two diseases independently. SNPs were genotyped in loci associated with T1D (n = 42) and CeD (n = 28) in 543 subjects who developed double autoimmunity, 2,472 subjects with T1D only, and 2,223 CeD-only subjects. For identification of loci that were specifically associated with individuals developing double autoimmunity, two association analyses were conducted: double autoimmunity versus T1D and double autoimmunity versus CeD. HLA risk haplotypes were compared between the two groups. The CTLA4 and IL2RA loci were more strongly associated with double autoimmunity than with either T1D or CeD alone. HLA analyses indicated that the T1D high-risk genotype, DQ2.5/DQ8, provided the highest risk for developing double autoimmunity (odds ratio 5.22, P = 2.25 × 10−29). We identified a strong HLA risk genotype (DQ2.5/DQ8) predisposing to double autoimmunity, suggesting a dominant role for HLA. Non-HLA loci, CTLA4 and IL2RA, may also confer risk to double autoimmunity. Thus, CeD patients who carry the DQ2.5/DQ8 genotype may benefit from periodic screening of autoantibodies related to T1D.

The common genetic loci that independently influence the risk of type 1 diabetes have largely been determined. Their interactions with age-at-diagnosis of type 1 diabetes, sex, or the major susceptibility locus, HLA class II, remain mostly unexplored. A large collection of more than 14,866 type 1 diabetes samples (6,750 British diabetic individuals and 8,116 affected family samples of European descent) were genotyped at 38 confirmed type 1 diabetes-associated non-HLA regions and used to test for interaction of association with age-at-diagnosis, sex, and HLA class II genotypes using regression models. The alleles that confer susceptibility to type 1 diabetes at interleukin-2 (IL-2), IL2/4q27 (rs2069763) and renalase, FAD-dependent amine oxidase (RNLS)/10q23.31 (rs10509540), were associated with a lower age-at-diagnosis (P = 4.6 × 10−6 and 2.5 × 10−5, respectively). For both loci, individuals carrying the susceptible homozygous genotype were, on average, 7.2 months younger at diagnosis than those carrying the protective homozygous genotypes. In addition to protein tyrosine phosphatase nonreceptor type 22 (PTPN22), evidence of statistical interaction between HLA class II genotypes and rs3087243 at cytotoxic T-lymphocyte antigen 4 (CTLA4)/2q33.2 was obtained (P = 7.90 × 10−5). No evidence of differential risk by sex was obtained at any loci (P ≥ 0.01). Statistical interaction effects can be detected in type 1 diabetes although they provide a relatively small contribution to our understanding of the familial clustering of the disease.

Using variants from the 1000 Genomes Project pilot European CEU dataset and data from additional resequencing studies, we densely genotyped 183 non-HLA risk loci previously associated with immune-mediated diseases in 12,041 individuals with celiac disease (cases) and 12,228 controls. We identified 13 new celiac disease risk loci reaching genome-wide significance, bringing the number of known loci (including the HLA locus) to 40. We found multiple independent association signals at over one-third of these loci, a finding that is attributable to a combination of common, low-frequency and rare genetic variants. Compared to previously available data such as those from HapMap3, our dense genotyping in a large sample collection provided a higher resolution of the pattern of linkage disequilibrium and suggested localization of many signals to finer scale regions. In particular, 29 of the 54 fine-mapped signals seemed to be localized to single genes and, in some instances, to gene regulatory elements. Altogether, we define the complex genetic architecture of the risk regions of and refine the risk signals for celiac disease, providing the next step toward uncovering the causal mechanisms of the disease. © 2011 Nature America, Inc. All rights reserved.

Scavenger receptor BI (SR-BI) is the major receptor for high-density lipoprotein (HDL) cholesterol (HDL-C). In humans, high amounts of HDL-C in plasma are associated with a lower risk of coronary heart disease (CHD). Mice that have depleted Scarb1 (SR-BI knockout mice) have markedly elevated HDL-C levels but, paradoxically, increased atherosclerosis. The impact of SR-BI on HDL metabolism and CHD risk in humans remains unclear. Through targeted sequencing of coding regions of lipid-modifying genes in 328 individuals with extremely high plasma HDL-C levels, we identified a homozygote for a lossof-function variant, in which leucine replaces proline 376 (P376L), in SCARB1, the gene encoding SR-BI. The P376L variant impairs posttranslational processing of SR-BI and abrogates selective HDL cholesterol uptake in transfected cells, in hepatocyte-like cells derived from induced pluripotent stem cells from the homozygous subject, and in mice. Large population-based studies revealed that subjects who are heterozygous carriers of the P376L variant have significantly increased levels of plasma HDL-C. P376L carriers have a profound HDL-related phenotype and an increased risk of CHD (odds ratio = 1.79, which is statistically significant). Zanoni P, Khetarpal SA, Larach DB, Hancock-Cerutti WF, Millar JS, Cuchel M, DerOhannessian S, Kontush A, Surendran P, Saleheen D, Trompet S, Jukema JW, De Craen A, Deloukas P, Sattar N, Ford I, Packard C, Majumder Aa, Alam DS, Di Angelantonio E, Science (New York, N.Y.), 2016, vol. 351, no. 6278, pp. 1166-1171, 2016 Refereed/Peer-reviewed