Genome-wide association studies have enabled the discovery of hundreds to thousands of significant variants associated to autoimmune diseases such as Type 1 Diabetes (T1D), most of which lie in non-coding regions of the genome. Unfortunately, the underlying mechanisms by which these genetic variants contribute to T1D remains unknown. Given that T1D is an autoimmune disease with a breakdown of immune tolerance, and regulatory T cells are a critical mediator of immune tolerance, we focused on a model of defective function of regulatory T cells (Tregs). We designed a variant filtering workflow utilising overlapping profiles our own and public Treg-specific epigenomic data (ATAC-seq, HiC-seq and FOXP3-binding profiles), hypothesising that variants found within (a) open chromatin regions (ATAC), (b) with a valid HiC interaction, (c) overlapping known regulatory regions, and (d) at Tregs-specific FOXP3 bound regions (ChIP), are driving the regulation of disease-associated gene loci. We identified 36 SNPs from a curated set of 1,228 T1D-associated SNPs (Onengut-Gumuscu et al. 2015 Nature Genetics), confirming previous known associations to 14 candidate T1D gene regions. Approximately 85% of valid interaction in close proximity to filtered variants overlapped with T cell-specific super-enhancers, suggesting the presence of regulatory hubs that control proximal gene expression. Extending the filtering approach to all common (>10% allele frequency) variants from the Genome Aggregation Database (gnomAD), we identified a total of 7,900 filtered variants forming a list of potential sites for future autoimmune disease research. Using cell-specific filtering schemes, we demonstrate that it is possible to further prioritise GWAS variants that contribute to the immune regulatory function of complex diseases, and illustrate the power of using specific multi-omics datasets to investigate disease mechanisms.