Shared genetic architecture between mental health and the brain functional connectome in the UK Biobank

Daniel Roelfs; Oleksandr Frei; Dennis van der Meer; Elleke Tissink; Alexey Shadrin; Dag Alnaes; Ole A. Andreassen; Lars T. Westlye; Tobias Kaufmann

doi:10.1186/s12888-023-04905-7

Shared genetic architecture between mental health and the brain functional connectome in the UK Biobank

Daniel Roelfs^*, Oleksandr Frei, Dennis van der Meer, Elleke Tissink, Alexey Shadrin, Dag Alnaes, Ole A. Andreassen, Lars T. Westlye, Tobias Kaufmann^*

^*Corresponding author for this work

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

Abstract

Psychiatric disorders are complex clinical conditions with large heterogeneity and overlap in symptoms, genetic liability and brain imaging abnormalities. Building on a dimensional conceptualization of mental health, previous studies have reported genetic overlap between psychiatric disorders and population-level mental health, and between psychiatric disorders and brain functional connectivity. Here, in 30,701 participants aged 45–82 from the UK Biobank we map the genetic associations between self-reported mental health and resting-state fMRI-based measures of brain network function. Multivariate Omnibus Statistical Test revealed 10 genetic loci associated with population-level mental symptoms. Next, conjunctional FDR identified 23 shared genetic variants between these symptom profiles and fMRI-based brain network measures. Functional annotation implicated genes involved in brain structure and function, in particular related to synaptic processes such as axonal growth (e.g. NGFR and RHOA). These findings provide further genetic evidence of an association between brain function and mental health traits in the population.

Original language	English
Article number	461
Pages (from-to)	1-7
Number of pages	7
Journal	BMC Psychiatry
Volume	23
DOIs	https://doi.org/10.1186/s12888-023-04905-7
Publication status	Published - 23 Jun 2023

Bibliographical note

Funding Information:
The authors were funded by the Research Council of Norway (#276082 LifespanHealth, #223273 NORMENT, #283798 ERA-NET Neuron SYNSCHIZ, #249795), the South-East Norway Regional Health Authority (2019101, 2019107, and 2020086), and the European Research Council under the European Union’s Horizon2020 Research and Innovation program (ERC Starting Grant #802998), as well as the Horizon2020 Research and Innovation Action Grant CoMorMent (#847776). E.T. has been supported by the Foundation “De Drie Lichten” and The Simons Foundation Fund in The Netherlands. This research has been conducted using the UK Biobank Resource (access code 27412, https://www.ukbiobank.ac.uk/ ).

Funding Information:
This work was performed on the TSD (Tjenester for Sensitive Data) facilities, owned by the University of Oslo, operated and developed by the TSD service group at the University of Oslo, IT-Department (USIT). Computations were also performed on resources provided by UNINETT Sigma2 - the National Infrastructure for High Performance Computing and Data Storage in Norway.

Publisher Copyright:
© 2023, The Author(s).

Funding

The authors were funded by the Research Council of Norway (#276082 LifespanHealth, #223273 NORMENT, #283798 ERA-NET Neuron SYNSCHIZ, #249795), the South-East Norway Regional Health Authority (2019101, 2019107, and 2020086), and the European Research Council under the European Union’s Horizon2020 Research and Innovation program (ERC Starting Grant #802998), as well as the Horizon2020 Research and Innovation Action Grant CoMorMent (#847776). E.T. has been supported by the Foundation “De Drie Lichten” and The Simons Foundation Fund in The Netherlands. This research has been conducted using the UK Biobank Resource (access code 27412, https://www.ukbiobank.ac.uk/ ). This work was performed on the TSD (Tjenester for Sensitive Data) facilities, owned by the University of Oslo, operated and developed by the TSD service group at the University of Oslo, IT-Department (USIT). Computations were also performed on resources provided by UNINETT Sigma2 - the National Infrastructure for High Performance Computing and Data Storage in Norway.

Funders	Funder number
European Union’s Horizon2020 research and innovation program
Horizon2020 Research and Innovation Action Grant CoMorMent	847776
IT-department
Simons Foundation Fund
UNINETT
USIT
European Research Council	802998
European Research Council
Universitetet i Oslo
Norges forskningsråd	223273, 249795, 283798, 276082
Norges forskningsråd
Helse Sør-Øst RHF	2019101, 2020086, 2019107
Helse Sør-Øst RHF

Keywords

Brain connectivity
Computational psychiatry
Genetics
Mental health
Multivariate GWAS

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10.1186/s12888-023-04905-7

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Cite this

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title = "Shared genetic architecture between mental health and the brain functional connectome in the UK Biobank",

abstract = "Psychiatric disorders are complex clinical conditions with large heterogeneity and overlap in symptoms, genetic liability and brain imaging abnormalities. Building on a dimensional conceptualization of mental health, previous studies have reported genetic overlap between psychiatric disorders and population-level mental health, and between psychiatric disorders and brain functional connectivity. Here, in 30,701 participants aged 45–82 from the UK Biobank we map the genetic associations between self-reported mental health and resting-state fMRI-based measures of brain network function. Multivariate Omnibus Statistical Test revealed 10 genetic loci associated with population-level mental symptoms. Next, conjunctional FDR identified 23 shared genetic variants between these symptom profiles and fMRI-based brain network measures. Functional annotation implicated genes involved in brain structure and function, in particular related to synaptic processes such as axonal growth (e.g. NGFR and RHOA). These findings provide further genetic evidence of an association between brain function and mental health traits in the population.",

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note = "Funding Information: The authors were funded by the Research Council of Norway (#276082 LifespanHealth, #223273 NORMENT, #283798 ERA-NET Neuron SYNSCHIZ, #249795), the South-East Norway Regional Health Authority (2019101, 2019107, and 2020086), and the European Research Council under the European Union{\textquoteright}s Horizon2020 Research and Innovation program (ERC Starting Grant #802998), as well as the Horizon2020 Research and Innovation Action Grant CoMorMent (#847776). E.T. has been supported by the Foundation “De Drie Lichten” and The Simons Foundation Fund in The Netherlands. This research has been conducted using the UK Biobank Resource (access code 27412, https://www.ukbiobank.ac.uk/ ). Funding Information: This work was performed on the TSD (Tjenester for Sensitive Data) facilities, owned by the University of Oslo, operated and developed by the TSD service group at the University of Oslo, IT-Department (USIT). Computations were also performed on resources provided by UNINETT Sigma2 - the National Infrastructure for High Performance Computing and Data Storage in Norway. Publisher Copyright: {\textcopyright} 2023, The Author(s).",

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AU - van der Meer, Dennis

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AU - Shadrin, Alexey

AU - Alnaes, Dag

AU - Andreassen, Ole A.

AU - Westlye, Lars T.

AU - Kaufmann, Tobias

N1 - Funding Information: The authors were funded by the Research Council of Norway (#276082 LifespanHealth, #223273 NORMENT, #283798 ERA-NET Neuron SYNSCHIZ, #249795), the South-East Norway Regional Health Authority (2019101, 2019107, and 2020086), and the European Research Council under the European Union’s Horizon2020 Research and Innovation program (ERC Starting Grant #802998), as well as the Horizon2020 Research and Innovation Action Grant CoMorMent (#847776). E.T. has been supported by the Foundation “De Drie Lichten” and The Simons Foundation Fund in The Netherlands. This research has been conducted using the UK Biobank Resource (access code 27412, https://www.ukbiobank.ac.uk/ ). Funding Information: This work was performed on the TSD (Tjenester for Sensitive Data) facilities, owned by the University of Oslo, operated and developed by the TSD service group at the University of Oslo, IT-Department (USIT). Computations were also performed on resources provided by UNINETT Sigma2 - the National Infrastructure for High Performance Computing and Data Storage in Norway. Publisher Copyright: © 2023, The Author(s).

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N2 - Psychiatric disorders are complex clinical conditions with large heterogeneity and overlap in symptoms, genetic liability and brain imaging abnormalities. Building on a dimensional conceptualization of mental health, previous studies have reported genetic overlap between psychiatric disorders and population-level mental health, and between psychiatric disorders and brain functional connectivity. Here, in 30,701 participants aged 45–82 from the UK Biobank we map the genetic associations between self-reported mental health and resting-state fMRI-based measures of brain network function. Multivariate Omnibus Statistical Test revealed 10 genetic loci associated with population-level mental symptoms. Next, conjunctional FDR identified 23 shared genetic variants between these symptom profiles and fMRI-based brain network measures. Functional annotation implicated genes involved in brain structure and function, in particular related to synaptic processes such as axonal growth (e.g. NGFR and RHOA). These findings provide further genetic evidence of an association between brain function and mental health traits in the population.

AB - Psychiatric disorders are complex clinical conditions with large heterogeneity and overlap in symptoms, genetic liability and brain imaging abnormalities. Building on a dimensional conceptualization of mental health, previous studies have reported genetic overlap between psychiatric disorders and population-level mental health, and between psychiatric disorders and brain functional connectivity. Here, in 30,701 participants aged 45–82 from the UK Biobank we map the genetic associations between self-reported mental health and resting-state fMRI-based measures of brain network function. Multivariate Omnibus Statistical Test revealed 10 genetic loci associated with population-level mental symptoms. Next, conjunctional FDR identified 23 shared genetic variants between these symptom profiles and fMRI-based brain network measures. Functional annotation implicated genes involved in brain structure and function, in particular related to synaptic processes such as axonal growth (e.g. NGFR and RHOA). These findings provide further genetic evidence of an association between brain function and mental health traits in the population.

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Shared genetic architecture between mental health and the brain functional connectome in the UK Biobank

Abstract

Bibliographical note

Funding

Keywords

Access to Document

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