Fine-mapping cellular QTLs with RASQUAL and ATAC-seq

When cellular traits are measured using high-throughput DNA sequencing quantitative trait loci (QTLs) manifest as fragment count differences between individuals and allelic differences within individuals. We present RASQUAL (Robust Allele Specific QUAntitation and quality controL), a novel statistical approach for association mapping that models genetic effects and accounts for biases in sequencing data in a single, probabilistic framework. RASQUAL substantially improves fine-mapping accuracy and sensitivity of association detection over existing methods in RNA-seq, DNaseI-seq and ChIP-seq data. We illustrate how RASQUAL can be used to maximise association detection by generating the first map of chromatin accessibility QTLs (caQTLs) in a European population using ATAC-seq. Despite a modest sample size, we identified 2,707 independent caQTLs (FDR 10%) and demonstrate how combining RASQUAL and ATAC-seq can provide powerful information for fine-mapping gene regulatory variants and for linking distal regulatory elements with gene promoters. Our results highlight how combining between-individual and allele-specific genetic signals improves the functional interpretation of noncoding variation.