TY - JOUR
T1 - Genome-wide identification of directed gene networks using large-scale population genomics data
AU - BIOS (Biobank-based Integrative Omics Study) Consortium
AU - Luijk, René
AU - Dekkers, Koen F.
AU - van Iterson, Maarten
AU - Arindrarto, Wibowo
AU - Claringbould, Annique
AU - Hop, Paul
AU - Beekman, Marian
AU - van der Breggen, Ruud
AU - Deelen, Joris
AU - Lakenberg, Nico
AU - Moed, Matthijs
AU - Suchiman, H. Eka D.
AU - Arindrarto, Wibowo
AU - van ’t Hof, Peter
AU - Bonder, Marc Jan J.
AU - Deelen, Patrick
AU - Tigchelaar, Ettje F.
AU - Zhernakova, Alexandra
AU - Zhernakova, Dasha V.
AU - van Dongen, Jenny
AU - Hottenga, Jouke J.
AU - Pool, René
AU - Isaacs, Aaron
AU - Hofman, Bert A.
AU - Jhamai, Mila
AU - van der Kallen, Carla J.H.
AU - Schalkwijk, Casper G.
AU - Stehouwer, Coen D.A.
AU - van den Berg, Leonard H.
AU - van Galen, Michiel
AU - Vermaat, Martijn
AU - van Rooij, Jeroen
AU - Uitterlinden, André G.
AU - Verbiest, Michael
AU - Verkerk, Marijn
AU - Kielbasa, P. Szymon M.
AU - Bot, Jan
AU - Nooren, Irene
AU - van Dijk, Freerk
AU - Swertz, Morris A.
AU - van Heemst, Diana
AU - Boomsma, Dorret I.
AU - van Duijn, Cornelia M.
AU - van Greevenbroek, Marleen M.J.
AU - Veldink, Jan H.
AU - Wijmenga, Cisca
AU - Franke, Lude
AU - ’t Hoen, Peter A.C.
AU - Jansen, Rick
AU - van Meurs, Joyce
N1 - Publisher Copyright:
© 2018, The Author(s).
PY - 2018/12/1
Y1 - 2018/12/1
N2 - Identification of causal drivers behind regulatory gene networks is crucial in understanding gene function. Here, we develop a method for the large-scale inference of gene–gene interactions in observational population genomics data that are both directed (using local genetic instruments as causal anchors, akin to Mendelian Randomization) and specific (by controlling for linkage disequilibrium and pleiotropy). Analysis of genotype and whole-blood RNA-sequencing data from 3072 individuals identified 49 genes as drivers of downstream transcriptional changes (Wald P < 7 × 10−10), among which transcription factors were overrepresented (Fisher’s P = 3.3 × 10−7). Our analysis suggests new gene functions and targets, including for SENP7 (zinc-finger genes involved in retroviral repression) and BCL2A1 (target genes possibly involved in auditory dysfunction). Our work highlights the utility of population genomics data in deriving directed gene expression networks. A resource of trans-effects for all 6600 genes with a genetic instrument can be explored individually using a web-based browser.
AB - Identification of causal drivers behind regulatory gene networks is crucial in understanding gene function. Here, we develop a method for the large-scale inference of gene–gene interactions in observational population genomics data that are both directed (using local genetic instruments as causal anchors, akin to Mendelian Randomization) and specific (by controlling for linkage disequilibrium and pleiotropy). Analysis of genotype and whole-blood RNA-sequencing data from 3072 individuals identified 49 genes as drivers of downstream transcriptional changes (Wald P < 7 × 10−10), among which transcription factors were overrepresented (Fisher’s P = 3.3 × 10−7). Our analysis suggests new gene functions and targets, including for SENP7 (zinc-finger genes involved in retroviral repression) and BCL2A1 (target genes possibly involved in auditory dysfunction). Our work highlights the utility of population genomics data in deriving directed gene expression networks. A resource of trans-effects for all 6600 genes with a genetic instrument can be explored individually using a web-based browser.
UR - http://www.scopus.com/inward/record.url?scp=85051275791&partnerID=8YFLogxK
U2 - 10.1038/s41467-018-05452-6
DO - 10.1038/s41467-018-05452-6
M3 - Article
C2 - 30082726
AN - SCOPUS:85051275791
SN - 2041-1723
VL - 9
JO - Nature Communications
JF - Nature Communications
IS - 1
M1 - 3097
ER -