Del Rosario RC, Poschmann J, Rouam SL, Png E, Khor CC, Hibberd ML, Prabhakar S
Most disease associations detected by genome-wide association studies (GWAS) lie outside coding genes, but very few have been mapped to causal regulatory variants. Here, we present a method for detecting regulatory quantitative trait loci (QTLs) that does not require genotyping or whole-genome sequencing. The method combines deep, long-read chromatin immunoprecipitation-sequencing (ChIP-seq) with a statistical test that simultaneously scores peak height correlation and allelic imbalance: the genotype-independent signal correlation and imbalance (G-SCI) test. We performed histone acetylation ChIP-seq on 57 human lymphoblastoid cell lines and used the resulting reads to call 500,066 single-nucleotide polymorphisms de novo within regulatory elements. The G-SCI test annotated 8,764 of these as histone acetylation QTLs (haQTLs)-an order of magnitude larger than the set of candidates detected by expression QTL analysis. Lymphoblastoid haQTLs were highly predictive of autoimmune disease mechanisms. Thus, our method facilitates large-scale regulatory variant detection in any moderately sized cohort for which functional profiling data can be generated, thereby simplifying identification of causal variants within GWAS loci.