Genome-wide association studies have identified hundreds of risk loci for autoimmune disease, yet only a minority (~25%) share genetic effects with changes to gene expression (eQTLs) in immune cells. 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 can be concealed. We performed integrative cis-eQTL analysis using association statistics from twenty autoimmune diseases (560 independent loci) and RNA-Seq data from 373 individuals of the Geuvadis cohort profiled at gene-, isoform-, exon-, junction-, and intron-level resolution in lymphoblastoid cell lines. After stringently testing for a shared causal variant using both the Joint Likelihood Mapping and Regulatory Trait Concordance frameworks, we found that gene-level quantification significantly underestimated the number of causal cis-eQTLs. Only 5.0-5.3% of loci were found to share a causal cis-eQTL at gene-level compared to 12.9-18.4% at exon-level and 9.6-10.5% at junction-level. More than a fifth of autoimmune loci shared an underlying causal variant in a single cell type by combining all five quantification types; a marked increase over current estimates of steady-state causal cis-eQTLs. As an example, we dissected in detail the genetic associations of systemic lupus erythematosus and functionally annotated the candidate genes. Many of the known and novel genes were concealed at gene-level (e.g. BANK1, UBE2L3, IKZF2, TYK2, LYST). By leveraging RNA-Seq, we were able to isolate the specific transcripts, exons, junctions, and introns modulated by the cis-eQTL - which supports the targeted design of follow-up functional studies involving alternative splicing. Causal cis-eQTLs detected at different quantification types were also found to localise to discrete epigenetic annotations. We provide our findings from all twenty autoimmune diseases as a web resource.

Profiling RNA-Seq at multiple resolutions markedly increases the number of causal eQTLs in autoimmune disease