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.