Direct and tissue-specific assessment of the damaging potential of regulatory SNPs

In the era of GWAS and personalized medicine systematically deciphering the impact of non-coding sequence variation on the regulatory genome is a critical bottleneck. Here we present Sasquatch a novel computational approach to estimate and visualize the effects of noncoding-variants on transcription factor (TF) binding using DNase I footprint data. Developed to provide an unbiased approach to prioritise non-coding variants for functional analysis, Sasquatch performs exhaustive, k-mer based analysis of average footprints to determine any k-mers potential for TF binding and quantifies how this is changed by sequence variants. Importantly the approach only requires one DNase-seq dataset per cell-type, from any genotype and is resilient across differing experimental procedures and sequence depths. We have made Sasquatch available as a versatile, high-throughput, webtool incorporating pre-processed human data from the Encode project and we demonstrate its effectiveness using validated causal GWAS SNPs and known polymorphic TF binding sites in erythroid tissues. 2 biological replicates using 2 growth protocols and 2 background controls of DNaseq-seq and 1 replicate and 1 background of ATAC-seq.