Type 1 diabetes (T1D) is an organ-specific autoimmune disease, whereby immune cell-mediated killing leads to loss of the insulin-producing β cells in the pancreas. Genome-wide association studies (GWAS) have identified over 200 genetic variants associated with risk for T1D. The majority of the GWAS risk variants reside in the non-coding regions of the genome, suggesting that gene regulatory changes substantially contribute to T1D. However, identification of causal regulatory variants associated with T1D risk and their affected genes is challenging due to incomplete knowledge of non-coding regulatory elements and the cellular states and processes in which they function. Here, we performed a comprehensive integrated post-GWAS analysis of T1D to identify functional regulatory variants in enhancers and their cognate target genes. Starting with 1,817 candidate T1D SNPs defined from the GWAS catalog and LDlink databases, we conducted functional annotation analysis using genomic data from various public databases. These include 1) Roadmap Epigenomics, ENCODE, and RegulomeDB for epigenome data; 2) GTEx for tissue-specific gene expression and expression quantitative trait loci data; and 3) lncRNASNP2 for long non-coding RNA data. Our results indicated a prevalent enhancer-based immune dysregulation in T1D pathogenesis. We identified 26 high-probability causal enhancer SNPs associated with T1D, and 64 predicted target genes. The majority of the target genes play major roles in antigen presentation and immune response and are regulated through complex transcriptional regulatory circuits, including those in HLA (6p21) and non-HLA (16p11.2) loci. These candidate causal enhancer SNPs are supported by strong evidence and warrant functional follow-up studies.

Purpose To build an age prediction model, we measured CD4+ and CD8+ cells, and humoral components in canine peripheral blood. Materials and Methods Large Belgian Malinois (BGM) and German Shepherd Dog (GSD) breeds (n=27), aged from 1 to 12 years, were used for this study. Peripheral bloods were obtained by venepuncture, then plasma and peripheral blood mononuclear cells (PBMCs) were separated immediately. Six myokines, including interleukin (IL)-6, IL-8, IL-15, leukemia inhibitory factor (LIF), growth differentiation factor 8 (GDF8), and GDF11 were measured from plasma and CD4+/CD8+ T-lymphocytes ratio were measured from PBMC. These parameters were then tested with age prediction models to find the best fit model. Results We found that the T-lymphocyte ratio (CD4+/CD8+) was significantly correlated with age (r=0.46, p=0.016). Among the six myokines, only GDF8 showed a significant correlation with age (r=0.52, p=0.005). Interestingly, these two markers showed better correlations in male dogs than females, and BGM breed than GSD. Using these two age biomarkers, we could obtain the best fit in a quadratic linear mixed model (r=0.77, p=3×10 -6 ). Conclusions Age prediction is a challenging task because of complication with biological age. Our quadratic linear mixed model using CD4+/CD8+ ratio and GDF8 level showed a meaningful age prediction.