@article{aff8978c10454edfab76eafb7c31c855,
title = "Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases",
abstract = "Identification of therapeutic targets from genome-wide association studies (GWAS) requires insights into downstream functional consequences. We harmonized 8,613 RNA-sequencing samples from 14 brain datasets to create the MetaBrain resource and performed cis- and trans-expression quantitative trait locus (eQTL) meta-analyses in multiple brain region- and ancestry-specific datasets (n ≤ 2,759). Many of the 16,169 cortex cis-eQTLs were tissue-dependent when compared with blood cis-eQTLs. We inferred brain cell types for 3,549 cis-eQTLs by interaction analysis. We prioritized 186 cis-eQTLs for 31 brain-related traits using Mendelian randomization and co-localization including 40 cis-eQTLs with an inferred cell type, such as a neuron-specific cis-eQTL (CYP24A1) for multiple sclerosis. We further describe 737 trans-eQTLs for 526 unique variants and 108 unique genes. We used brain-specific gene-co-regulation networks to link GWAS loci and prioritize additional genes for five central nervous system diseases. This study represents a valuable resource for post-GWAS research on central nervous system diseases.",
author = "{de Klein}, Niek and Tsai, {Ellen A.} and Martijn Vochteloo and Denis Baird and Yunfeng Huang and Chen, {Chia Yen} and {van Dam}, Sipko and Roy Oelen and Patrick Deelen and Bakker, {Olivier B.} and {El Garwany}, Omar and Zhengyu Ouyang and Marshall, {Eric E.} and Zavodszky, {Maria I.} and {van Rheenen}, Wouter and Bakker, {Mark K.} and Jan Veldink and Gaunt, {Tom R.} and Heiko Runz and Lude Franke and Westra, {Harm Jan}",
note = "Funding Information: We thank the donors of the brain tissues underlying the RNA-seq data used for this study and their families for their willingness to donate samples for research. We also thank all researchers involved with the included cohorts for making their data available for use. We have acknowledged each of the included cohorts and datasets in the Supplementary Note. We thank the Center for Information Technology of the University of Groningen for their support and for providing access to the Peregrine high-performance computing cluster, as well as the UMCG Genomics Coordination Center, the UG Center for Information Technology and their sponsors BBMRI-NL and TarGet for storage and computing infrastructure. D.B. and T.R.G. are supported by funding from the UK Medical Research Council (MRC Integrative Epidemiology Unit at the University of Bristol, grant no. MC_UU_00011/4) and a sponsored research collaboration with Biogen. L.F. is supported by a grant from the Dutch Research Council (grant no. ZonMW-VICI 09150182010019), an ERC Starting Grant (grant agreement 637640 (ImmRisk)), an Oncode Senior Investigator grant and a sponsored research collaboration with Biogen. This project has received funding from the European Research Council (ERC) under the European Union{\textquoteright}s Horizon 2020 research and innovation program (grant agreement no. 772376–EScORIAL to J.V.). The authors thank the Biogen CellMap team (Z. Ouyang, N. Bourgeois, E. Lyashenko, P. Cundiff, K. Li, X. Zhang, F. Casey, S. Engle, R. Kleiman, B. Zhang and M. Zavodszky) for the advice they provided towards deriving the cell type-specific expression profiles. Funding Information: We thank the donors of the brain tissues underlying the RNA-seq data used for this study and their families for their willingness to donate samples for research. We also thank all researchers involved with the included cohorts for making their data available for use. We have acknowledged each of the included cohorts and datasets in the . We thank the Center for Information Technology of the University of Groningen for their support and for providing access to the Peregrine high-performance computing cluster, as well as the UMCG Genomics Coordination Center, the UG Center for Information Technology and their sponsors BBMRI-NL and TarGet for storage and computing infrastructure. D.B. and T.R.G. are supported by funding from the UK Medical Research Council (MRC Integrative Epidemiology Unit at the University of Bristol, grant no. MC_UU_00011/4) and a sponsored research collaboration with Biogen. L.F. is supported by a grant from the Dutch Research Council (grant no. ZonMW-VICI 09150182010019), an ERC Starting Grant (grant agreement 637640 (ImmRisk)), an Oncode Senior Investigator grant and a sponsored research collaboration with Biogen. This project has received funding from the European Research Council (ERC) under the European Union{\textquoteright}s Horizon 2020 research and innovation program (grant agreement no. 772376–EScORIAL to J.V.). The authors thank the Biogen CellMap team (Z. Ouyang, N. Bourgeois, E. Lyashenko, P. Cundiff, K. Li, X. Zhang, F. Casey, S. Engle, R. Kleiman, B. Zhang and M. Zavodszky) for the advice they provided towards deriving the cell type-specific expression profiles. Publisher Copyright: {\textcopyright} 2023, The Author(s).",
year = "2023",
month = mar,
doi = "10.1038/s41588-023-01300-6",
language = "English",
volume = "55",
pages = "377--388",
journal = "Nature genetics",
issn = "1061-4036",
publisher = "Nature Publishing Group",
number = "3",
}