Quantitative assessment of association between noncoding variants and transcription factor binding

Abstract

Association fine-mapping of molecular traits is an essential method for understanding the impact of genetic variation. Sequencing-based assays, including RNA-seq, DNase-seq and ChIP-seq, have been widely used to measure different cellular traits and enabled genome-wide mapping of quantitative trait loci (QTLs). The disruption of cis-regulatory sequence, often occurring through variation within transcription factor binding motifs, has been strongly associated with gene dysregulation and human disease. We recently developed a computational method, TRACE, for transcription factor binding footprint prediction. TRACE integrates chromatin accessibility and transcription factor binding motifs to produce quantitative scores that describe the binding affinity of a TF for a specific TFBS locus. Here we have extended this method to incorporate variant data for 57 Yoruban individuals. Using genome-wide chromatin-accessibility data and human TF binding motifs, we have generated precise, genome-wide predictions of individual-specific transcription factor binding footprints. Subsequent association mapping between these footprints and nearby regulatory variants yielded numerous footprint-variant pairs with significant evidence for correlation, which we call footprint-QTLs (fpQTLs). fpQTLs appear to affect TF binding in a distance-dependent manner and share significant overlap with known dsQTLs and eQTLs. fpQTLs provide a rich resource for the study of regulatory variants, both within and outside known TFBSs, leading to improved functional interpretation of noncoding variation.