Genome ResearchGeneticsPeer-reviewed journal article
Functional non-coding SNPs in human endothelial cells fine-map vascular trait associations
Anu ToropaineniD, Lindsey K. StolzeiD, Tiit ÖrdiD, Michael Bernard WhaleniD, Paula Martí Torrell, Verena M. LinkiD, Minna Unelma KaikkoneniD, Casey E. RomanoskiiD
Abstract
Functional consequences of genetic variation in the non-coding human genome are difficult to ascertain despite demonstrated associations to common, complex disease traits. To elucidate properties of functional non-coding SNPs with effects in human endothelial cells (EC), we utilized molecular Quantitative Trait Locus (molQTL) analysis for transcription factor binding, chromatin accessibility, and H3K27 acetylation to nominate a set of likely functional non-coding SNPs. Together with information from genome-wide association studies for vascular disease traits, we tested the ability of 34,344 variants to perturb enhancer function in ECs using the highly multiplexed STARR-seq assay. Of these, 5,592 variants validated, whose enriched attributes included: 1) mutations to TF binding motifs for ETS or AP1 that are regulators of EC state, 2) location in accessible and H3K27ac-marked EC chromatin, and 3) molQTLs associations whereby alleles associate with differences in chromatin accessibility and TF binding across genetically diverse ECs. Next, using pro-inflammatory IL1B as an activator of cell state, we observed robust evidence (>50%) of context-specific SNP effects, underscoring the prevalence of non-coding gene-by-environment (GxE) effects. Lastly, using these cumulative data, we fine-mapped vascular disease loci and highlight evidence suggesting mechanisms by which non-coding SNPs at two loci affect risk for Pulse Pressure/Large Artery Stroke, and Abdominal Aortic Aneurysm through respective effects on transcriptional regulation of POU4F1 and LDAH. Together, we highlight the attributes and context dependence of functional non-coding SNPs, and provide new mechanisms underlying vascular disease risk.
- organism: human
- assay: DNA methylation
- assay: ChIP-seq
- research focus: fine-mapping
- approach: statistical model
- Citations
- 28
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