PLoS GeneticsGenomicsPeer-reviewed journal article
An autoimmune disease risk variant has a trans master regulatory effect mediated by IRF1 under immune stimulation
Margot K. BrandtiD, Sarah Kim-HellmuthiD, Marcello ZiosiiD, Alper GokdeniD, Aaron Wolman, Nora LamiD, Yocelyn RecinosiD, Zharko DaniloskiiD, John A. MorrisiD, Veit HornungiD, Johannes SchumacheriD, Tuuli LappalaineniD
Abstract
Functional mechanisms remain unknown for most genetic loci associated to complex human traits and diseases. In this study, we first mapped trans-eQTLs in a data set of primary monocytes stimulated with LPS, and discovered that a risk variant for autoimmune disease, rs17622517 in an intron of C5ORF56, affects the expression of the transcription factor IRF1 20 kb away. The cis-regulatory effect on IRF1 is active under early immune stimulus, with a large number of trans-eQTL effects across the genome under late LPS response. Using CRISPRi silencing, we showed that the SNP locus indeed functions as an IRF1 enhancer with widespread transcriptional effects. Genome editing by CRISPR further indicated that rs17622517 is indeed a causal variant in this locus, and recapitulated the LPS-specific trans-eQTL signal. Our results suggest that this common genetic variant affects inter-individual response to immune stimuli via regulation of IRF1. For this autoimmune GWAS locus, our work provides evidence of the causal variant, demonstrates a condition-specific enhancer effect, identifies IRF1 as the likely causal gene in cis, and indicates that overactivation of the downstream immune-related pathway may be the cellular mechanism increasing disease risk. This work not only provides rare experimental validation of a master-regulatory trans-eQTL, but also demonstrates the power of eQTL mapping to build mechanistic hypotheses amenable for experimental follow-up using the CRISPR toolkit.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: variant effect prediction
- research focus: database / resource
- approach: statistical model
- approach: experimental assay
- Citations
- 31
- FWCI
- 3.53
- Field percentile
- top 6%
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