Leveraging CRISPR/Cas Genome Editing Technology to Identify and Characterize Causal GWAS Variants for Blood Lipids

Abstract

Genome-wide association studies (GWAS) have identified a number of novel genetic loci linked to serum cholesterol and triglyceride levels. The causal DNA variants at these loci and the mechanism by which they influence phenotype and disease risk remain largely unexplored. Expression quantitative trait locus (eQTL) analyses of patient liver and adipose biopsies indicate that many lipid-associated variants influence gene expression in a cis-regulatory manner. However, linkage disequilibrium (LD) among neighboring single nucleotide polymorphisms (SNPs) at a GWAS-implicated locus makes it challenging to pinpoint the actual variant underlying an association signal. Here we performed high-throughput identification of putative disease-causal loci through a functional reporter-based screen, the massively parallel reporter assay (MPRA). We then validated prioritized variants using a combination of genome edited stem cells, clustered regularly interspaced short palindromic repeats (CRISPR) interference, and in vivo genome edited humanized mouse models to establish rs2277862-CPNE1, rs10889356-ANGPTL3, and rs12740374-SORT1 as causal SNP gene sets. These results highlight a novel experimental framework to discover causal genes and variants contributing to complex human traits.