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Dissecting the regulatory architecture of gene expression QTLs

Daniel J Gaffney125*, Jean-Baptiste Veyrieras1, Jacob F Degner1, Roger Pique-Regi1, Athma A Pai1, Gregory E Crawford3, Matthew Stephens14, Yoav Gilad1 and Jonathan K Pritchard12

Author Affiliations

1 Department of Human Genetics, University of Chicago, 920 E58th Street, Chicago, IL 60637, USA

2 Howard Hughes Medical Institute, University of Chicago, 929 East 57th Street, Chicago, IL, 60637, USA

3 Duke Institute for Genome Sciences and Policy Duke University, 101 Science Drive, Durham, NC 27708, USA

4 Department of Statistics, University of Chicago, 920 E58th Street, Chicago, IL 60637, USA

5 Department of Bioinformatics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK

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Genome Biology 2012, 13:R7  doi:10.1186/gb-2012-13-1-r7

Published: 31 January 2012



Expression quantitative trait loci (eQTLs) are likely to play an important role in the genetics of complex traits; however, their functional basis remains poorly understood. Using the HapMap lymphoblastoid cell lines, we combine 1000 Genomes genotypes and an extensive catalogue of human functional elements to investigate the biological mechanisms that eQTLs perturb.


We use a Bayesian hierarchical model to estimate the enrichment of eQTLs in a wide variety of regulatory annotations. We find that approximately 40% of eQTLs occur in open chromatin, and that they are particularly enriched in transcription factor binding sites, suggesting that many directly impact protein-DNA interactions. Analysis of core promoter regions shows that eQTLs also frequently disrupt some known core promoter motifs but, surprisingly, are not enriched in other well-known motifs such as the TATA box. We also show that information from regulatory annotations alone, when weighted by the hierarchical model, can provide a meaningful ranking of the SNPs that are most likely to drive gene expression variation.


Our study demonstrates how regulatory annotation and the association signal derived from eQTL-mapping can be combined into a single framework. We used this approach to further our understanding of the biology that drives human gene expression variation, and of the putatively causal SNPs that underlie it.