Genome-wide association studies have identified hundreds of risk loci for autoimmune disease, yet only a minority (~25%) share a single genetic effect with changes to gene expression (eQTLs) in primary immune cell types. RNA-Seq based quantification at whole-gene resolution, where abundance is estimated by culminating expression of all transcripts or exons of the same gene, is likely to account for this observed lack of colocalisation as subtle isoform switches and expression variation in independent exons are concealed. We perform integrative cis-eQTL analysis using association data from twenty autoimmune diseases (846 SNPs; 584 independent loci), with RNA-Seq expression from the GEUVADIS cohort profiled at gene-, isoform-, exon-, junction-, and intron-level resolution. After testing for a shared causal variant, we found exon-, and junction-level analyses produced the greatest frequency of candidate-causal cis-eQTLs; many of which were concealed at whole-gene resolution. In fact, only 9% of autoimmune loci shared a disease-relevant eQTL effect at gene-level. Expression profiling at all resolutions however was necessary to capture the full array of eQTL associations, and by doing so, we found 45% of loci were candidate-causal cis-eQTLs. Our findings are provided as a web resource for the functional annotation of autoimmune disease association studies (www.insidegen.com). As an example, we dissect the genetic associations of Ankylosing Spondylitis as only a handful of loci have documented causative relationships with gene expression. We classified fourteen of the thirty-one associated SNPs as candidate-causal cis-eQTLs. Many of the newly implicated genes had direct relevance to inflammation through regulation of TNF signalling (for example NFATC2IP, PDE4A, and RUSC1), and were supported by integration of functional genomic data from epigenetic and chromatin interaction studies. We have provided a deeper mechanistic understanding of the genetic regulation of gene expression in autoimmune disease by profiling the transcriptome at multiple resolutions.