Genome-wide identification of directed gene networks using large-scale population genomics data

BIOS (Biobank-based Integrative Omics Study) Consortium

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

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.

Original languageEnglish
Article number3097
Pages (from-to)1-10
Number of pages10
JournalNature Communications
Volume9
DOIs
Publication statusPublished - 6 Aug 2018

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Metagenomics
genome
Gene Regulatory Networks
genes
Genes
Genome
Web Browser
RNA Sequence Analysis
Zinc Fingers
Linkage Disequilibrium
Random Allocation
sequencing
gene expression
Genomics
Anchors
inference
Gene expression
linkages
Transcription Factors
blood

Cite this

BIOS (Biobank-based Integrative Omics Study) Consortium. / Genome-wide identification of directed gene networks using large-scale population genomics data. In: Nature Communications. 2018 ; Vol. 9. pp. 1-10.
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abstract = "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.",
author = "Ren{\'e} Luijk and Dekkers, {Koen F.} and {van Iterson}, Maarten and Wibowo Arindrarto and Annique Claringbould and Paul Hop and Marian Beekman and {van der Breggen}, Ruud and Joris Deelen and Nico Lakenberg and Matthijs Moed and Suchiman, {H. Eka D.} and Wibowo Arindrarto and {van ’t Hof}, Peter and Bonder, {Marc Jan J.} and Patrick Deelen and Tigchelaar, {Ettje F.} and Alexandra Zhernakova and Zhernakova, {Dasha V.} and {van Dongen}, Jenny and Hottenga, {Jouke J.} and Ren{\'e} Pool and Aaron Isaacs and Hofman, {Bert A.} and Mila Jhamai and {van der Kallen}, {Carla J.H.} and Schalkwijk, {Casper G.} and Stehouwer, {Coen D.A.} and {van den Berg}, {Leonard H.} and {van Galen}, Michiel and Martijn Vermaat and {van Rooij}, Jeroen and Uitterlinden, {Andr{\'e} G.} and Michael Verbiest and Marijn Verkerk and Kielbasa, {P. Szymon M.} and Jan Bot and Irene Nooren and {van Dijk}, Freerk and Swertz, {Morris A.} and {van Heemst}, Diana and Boomsma, {Dorret I.} and {van Duijn}, {Cornelia M.} and {van Greevenbroek}, {Marleen M.J.} and Veldink, {Jan H.} and Cisca Wijmenga and Lude Franke and {’t Hoen}, {Peter A.C.} and Rick Jansen and {van Meurs}, Joyce and {BIOS (Biobank-based Integrative Omics Study) Consortium}",
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Genome-wide identification of directed gene networks using large-scale population genomics data. / BIOS (Biobank-based Integrative Omics Study) Consortium.

In: Nature Communications, Vol. 9, 3097, 06.08.2018, p. 1-10.

Research output: Contribution to JournalArticleAcademicpeer-review

TY - JOUR

T1 - Genome-wide identification of directed gene networks using large-scale population genomics data

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

AU - BIOS (Biobank-based Integrative Omics Study) Consortium

PY - 2018/8/6

Y1 - 2018/8/6

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.

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U2 - 10.1038/s41467-018-05452-6

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JF - Nature Communications

SN - 2041-1723

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