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

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

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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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{58b17ebd7ef641528ff63b5ab4c29622,

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 = "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",

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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

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.

UR - http://www.scopus.com/inward/record.url?scp=85148619213&partnerID=8YFLogxK

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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