Abstract 15063: Liver Gene Regulatory Mechanisms of Variants at Lipid and Cardio Metabolic Trait Loci

Genome-wide association studies (GWAS) have identified hundreds of risk loci for cardiometabolic and lipid-related traits. GWAS loci are enriched in tissue-specific transcriptional regulatory elements that affect target gene expression driving physiological changes. Most GWAS variants are located in non-coding regions, which makes identifying functional variants and target genes challenging. Previously, we identified GWAS signals colocalized with molecular quantitative trait loci for liver gene expression (eQTL) and liver accessible chromatin (caQTL) to predict regulatory variants for lipid and cardiometabolic traits. These caQTLs are rs13395911, rs11644920, rs34003091 and rs9556404 associated with liver enzymes, LDL-cholesterol, and triglycerides. To evaluate the predicted mechanisms, we tested these variants with surrounding regulatory elements for allelic effects on transcriptional reporter activity and for effects on target gene expression in HepG2 cell line. Along with the two previously reported intronic caQTL variants, rs13395911 and rs11644920 , we observed significant (P<0.05) allelic differences in transcriptional activity for rs9556404 and rs34003091, located at the promoters of GPR180 and ZNF329 , respectively. Based on eQTL and HiC data, the predicted genes for intronic enhancers rs13395911 and rs11644920 are EFHD1 and LITAF , respectively. To confirm the target genes for these two variants, we deployed inducible CRISPRi system by stably expressing dCas9 fused to a KRAB-repressor domain in HepG2 and targeting guide RNAs (gRNAs) to the DNA elements that span caQTL variants. For each element, we introduced ≥6 gRNAs into cells via lentiviruses and measured gene expression using qPCR. Compared to pools of non-targeted control gRNAs, preliminary analyses using pools of enhancer-targeting gRNAs surrounding rs13395911 and rs11644920 led to 30.3% lower EFHD1 and 38.6% lower LITAF expression at 7 days post-transduction, respectively. Thus, we identified and validated mechanisms by which DNA variants alter transcriptional activity and identified the target genes for lipid-related GWAS loci. These results suggest that this framework can be scaled up to map thousands of enhancers to genes in their native genomic state.