The mapping of expression quantitative trait loci (eQTLs) has emerged as an important tool for linking genetic variation to changes in gene regulation. However, it remains difficult to identify the causal variants underlying eQTLs, and little is known about the regulatory mechanisms by which they act. We used DNase I sequencing to measure chromatin accessibility in 70 Yoruba lymphoblastoid cell lines, for which genome-wide genotypes and estimates of gene expression levels are also available. We obtained a total of 2.7 billion uniquely mapped DNase I-sequencing (DNase-seq) reads, which allowed us to infer transcription factor binding exploiting the specific DNase I cleavage footprint left on 827,000 sites corresponding to more than 100 factors. Across individuals, we identified 8,902 locations at which the DNase-seq read depth correlated significantly with genotype at a nearby locus (FDR = 10%). We call such genetic variants ‘DNase I sensitivity quantitative trait loci’ (dsQTLs). We found that dsQTLs are strongly enriched within inferred transcription factor binding sites and are frequently associated with allele-specific changes in transcription factor binding. A substantial number of dsQTLs are also associated with variation in the expression levels of nearby genes. Our observations indicate that dsQTLs are highly abundant in the human genome and are likely to be important contributors to phenotypic variation."
Roger Pique-Regi received his telecommunications engineering degree from the Universitat Politecnica de Catalunya, BarcelonaTech, in 2002 and his Ph.D. degree in electrical engineering from the University of Southern California in 2009. His Ph.D. work was supported by the La Caixa Fellowship program. Since 2009, he has been a postdoctoral fellow in the Department of Human Genetics at the University of Chicago, where his research is supported by the Chicago Fellows program. He is a Member of the IEEE, the American Association for the Advancement of Science, and the International Society for Computational Biology. His research interests are in the area of genome signal processing, statistical machine learning, human genetics, and gene regulation.