Hypothesis-free identification of modulators of genetic risk factors

• Published in: bioRxiv
• Main Authors: Zhernakova, Daria, Deelen, Patrick, Vermaat, Martijn, Maarten Van Iterson, Michiel Van Galen, Wibowo Arindrarto, Van T Hof, Peter, Mei, Hailiang, Freerk Van Dijk, Harm-Jan Westra, Bonder, Marc Jan, Jeroen Van Rooij, Verkerk, Marijn, P Mila Jhamai, Moed, Matthijs, Kielbasa, Szymon M, Bot, Jan, Nooren, Irene, Ren Pool, Jenny Van Dongen, Hottenga, Jouke J, Coen Da Stehouwer, Carla Jh Van Der Kallen, Schalkwijk, Casper G, Zhernakova, Alexandra, Yang, Li, Tigchelaar, Ettje F, Beekman, Marian, Deelen, Joris, Diana Van Heemst, Leonard H Van Den Berg, Hofman, Albert, Andr G Uitterlinden, Marleen Mj Van Greevenbroek, Veldink, Jan H, Boomsma, Dorret I, Van Duijn, Cornelia M, Wijmenga, Cisca, P Eline Slagboom, Swertz, Morris A, Isaacs, Aaron, Van Meurs, Joyce Bj, Jansen, Rick, Heijmans, Bastiaan T, Hoen, Peter Ac T, Franke, Lude
• Format: Paper
• Language: English
• Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 30.11.2015
• Subjects: CD4 antigen; Erythrocytes; Etiology; Genomes; Health risk assessment; Hypotheses; Interferon; Leukocytes (eosinophilic); Lymphocytes T; Peripheral blood; Quantitative trait loci; Ribonucleic acid; Risk factors; RNA; Signal transduction; Single-nucleotide polymorphism; Transcription factors
• DOI: 10.1101/033217

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.