High-throughput Screening and CRISPR-Cas9 Modeling of Causal Lipid-associated Expression Quantitative Trait Locus Variants

Abstract

Genome-wide association studies have identified a number of novel genetic loci linked to serum cholesterol and triglyceride levels. The causal DNA variants at these loci and the mechanisms by which they influence phenotype and disease risk remain largely unexplored. Expression quantitative trait locus analyses of patient liver and fat biopsies indicate that many lipid-associated variants influence gene expression in a cis-regulatory manner. However, linkage disequilibrium among neighboring SNPs at a genome-wide association study-implicated locus makes it challenging to pinpoint the actual variant underlying an association signal. We used a methodological framework for causal variant discovery that involves high-throughput identification of putative disease-causal loci through a functional reporter-based screen, the massively parallel reporter assay, followed by validation of prioritized variants in genome-edited human pluripotent stem cell models generated with CRISPR-Cas9. We complemented the stem cell models with CRISPR interference experiments in vitro and in knock-in mice in vivo. We provide validation for two high-priority SNPs, rs2277862 and rs10889356, being causal for lipid-associated expression quantitative trait loci. We also highlight the challenges inherent in modeling common genetic variation with these experimental approaches.\n\nAuthor SummaryGenome-wide association studies have identified numerous loci linked to a variety of clinical phenotypes. It remains a challenge to identify and validate the causal DNA variants in these loci. We describe the use of a high-throughput technique called the massively parallel reporter assay to analyze thousands of candidate causal DNA variants for their potential effects on gene expression. We use a combination of genome editing in human pluripotent stem cells, \"CRISPR interference\" experiments in other cultured human cell lines, and genetically modified mice to analyze the two highest-priority candidate DNA variants to emerge from the massively parallel reporter assay, and we confirm the relevance of the variants to nearby gene expression. These findings highlight a methodological framework with which to identify and functionally validate causal DNA variants.