Characterization of non-coding variants associated with transcription factor binding through ATAC-seq-defined footprint QTLs in liver

Abstract

Non-coding variants discovered by genome-wide association studies (GWAS) are enriched in regulatory elements harboring transcription factor (TF) binding motifs, strongly suggesting a connection between disease association and the disruption of cis-regulatory sequences. Occupancy of a TF inside a region of open chromatin can be detected in ATAC-seq where bound TFs block the transposase Tn5, leaving a pattern of relatively depleted Tn5 insertions known as a "footprint". Here, we sought to identify variants associated with TF-binding, or "footprint quantitative trait loci" (fpQTLs) in ATAC-seq data generated from 170 human liver samples. We used computational tools to scan the ATAC-seq reads to quantify TF binding likelihood as "footprint scores" at variants derived from whole genome sequencing generated in the same samples. We tested for association between genotype and footprint score and observed 693 fpQTLs associated with footprint-inferred TF binding (FDR < 5%). Given that Tn5 insertion sites are measured with base-pair resolution, we show that fpQTLs can aid GWAS and QTL fine-mapping by precisely pinpointing TF activity within broad trait-associated loci where the underlying causal variant is unknown. Liver fpQTLs were strongly enriched across ChIP-seq peaks, liver expression QTLs (eQTLs), and liver-related GWAS loci, and their inferred effect on TF binding was concordant with their effect on underlying sequence motifs in 80% of cases. We conclude that fpQTLs can reveal causal GWAS variants, define the role of TF binding site disruption in disease and provide functional insights into non-coding variants, ultimately informing novel treatments for common diseases. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/614730v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@129928borg.highwire.dtl.DTLVardef@53fff8org.highwire.dtl.DTLVardef@c9f814org.highwire.dtl.DTLVardef@1bdf1a8_HPS_FORMAT_FIGEXP M_FIG C_FIG We leverage footprinting methods to infer transcription factor binding likelihood genome-wide across 170 liver ATAC-seq samples and implicate 693 SNPs with a genetic influence on binding. Unlike other comparable approaches, this analytical method is not limited in resolution by the constraints of linkage disequilibrium, and can prioritize likely causal variants at GWAS loci for subsequent experimental validation.