Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases

Niek de Klein; Ellen A. Tsai; Martijn Vochteloo; Denis Baird; Yunfeng Huang; Chia Yen Chen; Sipko van Dam; Roy Oelen; Patrick Deelen; Olivier B. Bakker; Omar El Garwany; Zhengyu Ouyang; Eric E. Marshall; Maria I. Zavodszky; Wouter van Rheenen; Mark K. Bakker; Jan Veldink; Tom R. Gaunt; Heiko Runz; Lude Franke; Harm Jan Westra

doi:10.1038/s41588-023-01300-6

Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases

Niek de Klein
, Ellen A. Tsai
, Martijn Vochteloo
, Denis Baird
, Yunfeng Huang
, Chia Yen Chen
, Sipko van Dam
, Roy Oelen
, Patrick Deelen
, Olivier B. Bakker
, Omar El Garwany
, Zhengyu Ouyang
, Eric E. Marshall
, Maria I. Zavodszky
, Wouter van Rheenen
, Mark K. Bakker
, Jan Veldink
, Tom R. Gaunt
, Heiko Runz^*
, Lude Franke^*

Harm Jan Westra^*

^*Corresponding author for this work

Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI)

Research output: Contribution to journal › Article › Academic › peer-review

116 Citations (Scopus)

241 Downloads (Pure)

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.

Original language	English
Pages (from-to)	377-388
Number of pages	12
Journal	Nature genetics
Volume	55
Issue number	3
DOIs	https://doi.org/10.1038/s41588-023-01300-6
Publication status	Published - Mar-2023

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Additional file 2 of PICALO: principal interaction component analysis for the identification of discrete technical, cell-type, and environmental factors that mediate eQTLs
Vochteloo, M. (Creator), Deelen, P. (Creator), Vink, B. (Creator), Tsai, E. A. (Creator), Runz, H. (Creator), Andreu-Sánchez, S. (Creator), Fu, J. Y. (Creator), Zhernakova, A. O. (Creator), Westra, H. J. (Creator) & Franke, L. (Creator), figshare, 14-Aug-2024
DOI: 10.6084/m9.figshare.26670786, https://springernature.figshare.com/articles/dataset/Additional_file_2_of_PICALO_principal_interaction_component_analysis_for_the_identification_of_discrete_technical_cell-type_and_environmental_factors_that_mediate_eQTLs/26670786
Dataset
Additional file 6 of PICALO: principal interaction component analysis for the identification of discrete technical, cell-type, and environmental factors that mediate eQTLs
Andreu-Sánchez, S. (Creator), Fu, J. Y. (Creator), Runz, H. (Creator), Zhernakova, A. O. (Creator) & Tsai, E. A. (Creator), figshare, 14-Aug-2024
DOI: 10.6084/m9.figshare.26670798, https://springernature.figshare.com/articles/dataset/Additional_file_6_of_PICALO_principal_interaction_component_analysis_for_the_identification_of_discrete_technical_cell-type_and_environmental_factors_that_mediate_eQTLs/26670798
Dataset
Additional file 9 of PICALO: principal interaction component analysis for the identification of discrete technical, cell-type, and environmental factors that mediate eQTLs
Vochteloo, M. (Creator), Deelen, P. (Creator), Vink, B. (Creator), Tsai, E. A. (Creator), Runz, H. (Creator), Andreu-Sánchez, S. (Creator), Fu, J. Y. (Creator), Zhernakova, A. O. (Creator), Westra, H. J. (Creator) & Franke, L. (Creator), figshare, 14-Aug-2024
DOI: 10.6084/m9.figshare.26670807, https://springernature.figshare.com/articles/dataset/Additional_file_9_of_PICALO_principal_interaction_component_analysis_for_the_identification_of_discrete_technical_cell-type_and_environmental_factors_that_mediate_eQTLs/26670807
Dataset

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de Klein, N., Tsai, E. A., Vochteloo, M., Baird, D., Huang, Y., Chen, C. Y., van Dam, S., Oelen, R., Deelen, P., Bakker, O. B., El Garwany, O., Ouyang, Z., Marshall, E. E., Zavodszky, M. I., van Rheenen, W., Bakker, M. K., Veldink, J., Gaunt, T. R., Runz, H., ... Westra, H. J. (2023). Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases. Nature genetics, 55(3), 377-388. https://doi.org/10.1038/s41588-023-01300-6

@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",

}

de Klein, N, Tsai, EA, Vochteloo, M, Baird, D, Huang, Y, Chen, CY, van Dam, S , Oelen, R , Deelen, P , Bakker, OB, El Garwany, O, Ouyang, Z, Marshall, EE, Zavodszky, MI, van Rheenen, W, Bakker, MK, Veldink, J, Gaunt, TR, Runz, H, Franke, L & Westra, HJ 2023, 'Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases', Nature genetics, vol. 55, no. 3, pp. 377-388. https://doi.org/10.1038/s41588-023-01300-6

TY - JOUR

T1 - Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases

AU - de Klein, Niek

AU - Tsai, Ellen A.

AU - Vochteloo, Martijn

AU - Baird, Denis

AU - Huang, Yunfeng

AU - Chen, Chia Yen

AU - van Dam, Sipko

AU - Oelen, Roy

AU - Deelen, Patrick

AU - Bakker, Olivier B.

AU - El Garwany, Omar

AU - Ouyang, Zhengyu

AU - Marshall, Eric E.

AU - Zavodszky, Maria I.

AU - van Rheenen, Wouter

AU - Bakker, Mark K.

AU - Veldink, Jan

AU - Gaunt, Tom R.

AU - Runz, Heiko

AU - Franke, Lude

AU - Westra, Harm Jan

N1 - 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’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’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: © 2023, The Author(s).

PY - 2023/3

Y1 - 2023/3

N2 - 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.

AB - 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.

U2 - 10.1038/s41588-023-01300-6

DO - 10.1038/s41588-023-01300-6

M3 - Article

C2 - 36823318

AN - SCOPUS:85148619213

SN - 1061-4036

VL - 55

SP - 377

EP - 388

JO - Nature genetics

JF - Nature genetics

IS - 3

ER -

Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases

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Additional file 2 of PICALO: principal interaction component analysis for the identification of discrete technical, cell-type, and environmental factors that mediate eQTLs

Additional file 6 of PICALO: principal interaction component analysis for the identification of discrete technical, cell-type, and environmental factors that mediate eQTLs

Additional file 9 of PICALO: principal interaction component analysis for the identification of discrete technical, cell-type, and environmental factors that mediate eQTLs

Cite this