TY - JOUR T1 - Hypothesis-free identification of modulators of genetic risk factors JF - bioRxiv DO - 10.1101/033217 SP - 033217 AU - Daria V. Zhernakova AU - Patrick Deelen AU - Martijn Vermaat AU - Maarten van Iterson AU - Michiel van Galen AU - Wibowo Arindrarto AU - Peter van ‘t Hof AU - Hailiang Mei AU - Freerk van Dijk AU - Harm-Jan Westra AU - Marc Jan Bonder AU - Jeroen van Rooij AU - Marijn Verkerk AU - P. Mila Jhamai AU - Matthijs Moed AU - Szymon M. Kielbasa AU - Jan Bot AU - Irene Nooren AU - René Pool AU - Jenny van Dongen AU - Jouke J. Hottenga AU - Coen D.A. Stehouwer AU - Carla J.H. van der Kallen AU - Casper G. Schalkwijk AU - Alexandra Zhernakova AU - Yang Li AU - Ettje F. Tigchelaar AU - Marian Beekman AU - Joris Deelen AU - Diana van Heemst AU - Leonard H. van den Berg AU - Albert Hofman AU - André G. Uitterlinden AU - Marleen M.J. van Greevenbroek AU - Jan H. Veldink AU - Dorret I. Boomsma AU - Cornelia M. van Duijn AU - Cisca Wijmenga AU - P. Eline Slagboom AU - Morris A. Swertz AU - Aaron Isaacs AU - Joyce B.J. van Meurs AU - Rick Jansen AU - Bastiaan T. Heijmans AU - Peter A.C. ‘t Hoen AU - Lude Franke Y1 - 2015/01/01 UR - http://biorxiv.org/content/early/2015/11/30/033217.abstract N2 - Genetic risk factors often localize in non-coding regions of the genome with unknown effects on disease etiology. Expression quantitative trait loci (eQTLs) help to explain the regulatory mechanisms underlying the association of genetic risk factors with disease. More mechanistic insights can be derived from knowledge of the context, such as cell type or the activity of signaling pathways, influencing the nature and strength of eQTLs. Here, we generated peripheral blood RNA-seq data from 2,116 unrelated Dutch individuals and systematically identified these context-dependent eQTLs using a hypothesis-free strategy that does not require prior knowledge on the identity of the modifiers. Out of the 23,060 significant cis-regulated genes (false discovery rate < 0.05), 2,743 genes (12%) show context-dependent eQTL effects. The majority of those were influenced by cell type composition, revealing eQTLs that are particularly strong in cell types such as CD4+ T-cells, erythrocytes, and even lowly abundant eosinophils. A set of 145 cis-eQTLs were influenced by the activity of the type I interferon signaling pathway and we identified several cis-eQTLs that are modulated by specific transcription factors that bind to the eQTL SNPs. This demonstrates that large-scale eQTL studies in unchallenged individuals can complement perturbation experiments to gain better insight in regulatory networks and their stimuli. ER -