PQG Seminar

Whole genome sequencing studies have identified hundreds of millions of rare variants, the majority of which are in non-coding regions and of unknown function. Because the regulatory landscape of many candidate Cis-Regulatory Elements (cCREs) varies across cell types, it is of substantial interest to incorporate single-cell sequencing data into RVATs of cCREs to capture the functional variability that exists across cell types in the non-coding genome and boost statistical power in the process. We propose cellSTAAR to address two opportunities to improve existing gene-centric RVAT methods as applied to genetic variants in cCREs. First, cellSTAAR integrates single-cell ATAC-seq data to capture variability in chromatin accessibility across cell types via the construction of cell-type-specific variant sets and cell-type-specific functional annotations. Second, cellSTAAR links cCREs to their target genes using an omnibus framework that aggregates results from a variety of linking approaches to reflect the uncertainty in element-gene linking. We applied cellSTAAR on Freeze 8 (N = 60,000) of the NHLBI Trans-Omics for Precision Medicine (TOPMed) consortium whole genome sequencing data to three quantitative lipids traits: LDL, HDL, and TG. In at least one cell type, genome-wide significant promoter and enhancer associations were found in several known lipids loci, including APOE, APOA1, and CETP. Unlike existing methods, cellSTAAR reveals variability in the significance at these loci across a variety of cell types from diverse tissues and uncertainty in the target gene for significant enhancers. Using a weakened genome-wide significance threshold, the most discoveries using cellSTAAR are found in cell types that are the most relevant to lipids such hepatocytes. We replicate our results using UK Biobank whole genome sequcing data (N = 190,000).