Open Access

Nina Baumgarten, Laura Rumpf, Thorsten Keßler, Marcel Holger Schulz

• Highlights • Single nucleotide variants (SNVs) may affect transcription factor (TF) binding • Fast statistical approach to assess significance of differential TF binding for SNVs • Validate new approach on in vitro and in vivo TF binding assays • Applications on GWAS SNVs and large eQTL studies illustrate utility Summary Non-coding variants located within regulatory elements may alter gene expression by modifying transcription factor (TF) binding sites, thereby leading to functional consequences. Different TF models are being used to assess the effect of DNA sequence variants, such as single nucleotide variants (SNVs). Often existing methods are slow and do not assess statistical significance of results. We investigated the distribution of absolute maximal differential TF binding scores for general computational models that affect TF binding. We find that a modified Laplace distribution can adequately approximate the empirical distributions. A benchmark on in vitro and in vivo datasets showed that our approach improves upon an existing method in terms of performance and speed. Applications on eQTLs and on a genome-wide association study illustrate the usefulness of our statistics by highlighting cell type-specific regulators and target genes. An implementation of our approach is freely available on GitHub and as bioconda package.