TY - JOUR T1 - Leveraging the resolution of RNA-Seq markedly increases the number of causal eQTLs and candidate genes in human autoimmune disease: Mapping eQTLs in autoimmune disease using RNA-Seq JF - bioRxiv DO - 10.1101/128728 SP - 128728 AU - Christopher A. Odhams AU - Deborah S. Cunninghame Graham AU - Timothy J. Vyse Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/04/19/128728.abstract N2 - 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.Author Summary It is now well acknowledged that non-coding genetic variants contribute to susceptibility of autoimmune disease through alteration of gene expression levels (eQTLs). Identifying the variants that are causal to both disease risk and changes to expression levels has not been easy and we believe this is in part due to how expression is quantified using RNA-Sequencing (RNA-Seq). Whole-gene expression, where abundance is estimated by culminating expression of all transcripts or exons of the same gene, is conventionally used in eQTL analysis. This low resolution may conceal subtle isoform switches and expression variation in independent exons. Using isoform-, exon-, and junction-level quantification can not only point to the candidate genes involved, but also the specific transcripts implicated. We make use of existing RNA-Seq expression data profiled at gene-, isoform-, exon-, junction-, and intron-level, and perform eQTL analysis using association data from twenty autoimmune diseases. We find exon-, and junction-level thoroughly outperform gene-level analysis, and by leveraging all five quantification types, we find 45% of autoimmune loci share a single genetic effect with gene expression. We highlight that existing and new eQTL cohorts using RNA-Seq should profile expression at multiple resolutions to maximise the ability to detect causal eQTLs and candidate-genes. ER -