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Common variants contribute to intrinsic human brain functional networks

Nature Genetics volume 54, pages 508–517 (2022)Cite this article

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Abstract

The human brain forms functional networks of correlated activity, which have been linked with both cognitive and clinical outcomes. However, the genetic variants affecting brain function are largely unknown. Here, we used resting-state functional magnetic resonance images from 47,276 individuals to discover and validate common genetic variants influencing intrinsic brain activity. We identified 45 new genetic regions associated with brain functional signatures (P < 2.8 × 10−11), including associations to the central executive, default mode, and salience networks involved in the triple-network model of psychopathology. A number of brain activity-associated loci colocalized with brain disorders (e.g., the APOE ε4 locus with Alzheimer’s disease). Variation in brain function was genetically correlated with brain disorders, such as major depressive disorder and schizophrenia. Together, our study provides a step forward in understanding the genetic architecture of brain functional networks and their genetic links to brain-related complex traits and disorders.

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Fig. 1: SNP heritability analysis of rsfMRI traits.
Fig. 2: Selected genetic locus associated with both rsfMRI trait of brain activity and brain-related complex traits.
Fig. 3: Selected pairwise genetic correlations between functional connectivity traits and regional brain volumes.
Fig. 4: Selected pairwise genetic correlations between functional connectivity traits and fractional anisotropy of white matter tracts.
Fig. 5: Selected pairwise genetic correlations between functional connectivity traits and brain disorders and intelligence.

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Data availability

Our GWAS summary statistics are publicly available at Zenodo105 (https://doi.org/10.5281/zenodo.5775047). The individual-level imaging and genetics data used in the present study are available from four publicly accessible data resources: UKB (https://www.ukbiobank.ac.uk/), ABCD (https://abcdstudy.org/), HCP (https://www.humanconnectome.org/) and PNC (https://www.med.upenn.edu/bbl/philadelphianeurodevelopmentalcohort.html). The Molecular Signatures Database dataset can be downloaded from https://www.gsea-msigdb.org/gsea/msigdb/. The Hi-C datasets of brain tissue and cell types can be requested or accessed following the instructions in the original publications. Our results can also be easily browsed through our knowledge portal at https://bigkp.org/.

Code availability

We made use of publicly available software and tools listed in URLs. The codes to generate the rsfMRI features are publicly available on Zenodo (https://doi.org/10.5281/zenodo.5784010). Other codes used in our analyses are available upon reasonable request.

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Acknowledgements

This research was partially supported by National Institutes of Health (NIH) grants MH086633 (H.Z.) and MH116527 (T. Li). We thank the individuals represented in the UKB, ABCD, HCP and PNC studies for their participation and the research teams for their work in collecting, processing and disseminating these datasets for analysis. We gratefully acknowledge all the studies and databases that made GWAS summary data available. This research has been conducted using the UKB resource (application number 22783), subject to a data transfer agreement. Some data used in the preparation of this article were obtained from the ABCD study (https://abcdstudy.org), held in the National Institute of Mental Health Data Archive. This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9–10 years and follow them over 10 years into early adulthood. The ABCD study is supported by the NIH and additional federal partners under award numbers U01DA041022, U01DA041028, U01DA041048, U01DA041089, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, U24DA041147, U01DA041093 and U01DA041025. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/scientists/workgroups/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in analysis or writing of this report. This article reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators. Support for the collection of the PNC datasets was provided by grant RC2MH089983 awarded to Raquel Gur and RC2MH089924 awarded to H. Hakonarson. All PNC subjects were recruited through the Center for Applied Genomics at The Children’s Hospital in Philadelphia. HCP data were provided by the HCP, WU-Minn Consortium (principal investigators D. Van Essen and K. Ugurbil; 1U54MH091657) funded by the 16 NIH institutes and centers that support the NIH Blueprint for Neuroscience Research and the McDonnell Center for Systems Neuroscience at Washington University. The UKB study has obtained ethics approval from the North West Multi-Centre Research Ethics Committee (approval number 11/NW/0382) and obtained written informed consent from all participants before the study. All experimental procedures in the HCP study were approved by the institutional review boards at Washington University (approval number 201204036). All procedures in the ABCD study were approved by the institutional review boards at ABCD collection sites (approval numbers 201708123 and 160091). The institutional review boards of the University of Pennsylvania and the Children’s Hospital of Philadelphia approved all study procedures in the PNC study.

Author information

Author notes

  1. These authors contributed equally: Bingxin Zhao, Tengfei Li.

Authors and Affiliations

  1. Department of Statistics, Purdue University, West Lafayette, IN, USA

    Bingxin Zhao

  2. Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Tengfei Li

  3. Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Tengfei Li, Weili Lin & Hongtu Zhu

  4. Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK

    Stephen M. Smith & Weili Lin

  5. Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Di Xiong, Xifeng Wang, Yue Yang, Tianyou Luo, Ziliang Zhu, Yue Shan, Quan Sun, Yun Li & Hongtu Zhu

  6. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Nana Matoba, Yuchen Yang, Yun Li & Jason L. Stein

  7. UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Nana Matoba & Jason L. Stein

  8. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Mads E. Hauberg, Jaroslav Bendl, John F. Fullard & Panagiotis Roussos

  9. Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Mads E. Hauberg, Jaroslav Bendl, John F. Fullard & Panagiotis Roussos

  10. iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark

    Mads E. Hauberg & Panagiotis Roussos

  11. Centre for Integrative Sequencing (iSEQ), Aarhus University, Aarhus, Denmark

    Mads E. Hauberg

  12. Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Jaroslav Bendl, John F. Fullard & Panagiotis Roussos

  13. Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA

    Panagiotis Roussos

  14. Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Yun Li & Hongtu Zhu

Authors
  1. Bingxin Zhao
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  2. Tengfei Li
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  3. Stephen M. Smith
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  4. Di Xiong
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  5. Xifeng Wang
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  6. Yue Yang
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  7. Tianyou Luo
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  8. Ziliang Zhu
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  9. Yue Shan
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  10. Nana Matoba
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  11. Quan Sun
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  12. Yuchen Yang
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  13. Mads E. Hauberg
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  14. Jaroslav Bendl
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  15. John F. Fullard
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  16. Panagiotis Roussos
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  17. Weili Lin
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  18. Yun Li
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  19. Jason L. Stein
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  20. Hongtu Zhu
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Contributions

B.Z., H.Z., J.L.S., S.M.S., and Y.L. designed the study. B.Z., T. Li, D.X., X.W., Yue Yang, T. Luo, N.M., Q.S. and Yuchen Yang analyzed the data. T. Li, Z.Z. and Y.S. downloaded the datasets, processed rsfMRI data and undertook quantity controls. W.L. assisted in interpreting findings. P.R., M.E.H., J.B. and J.F.F. analyzed brain cell chromatin accessibility data. B.Z. wrote the manuscript, with feedback from all authors.

Corresponding author

Correspondence to Hongtu Zhu.

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The authors declare no competing interests.

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Nature Genetics thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Genetic locus at 19q13.21 associated with both rsfMRI trait of brain activity and brain-related complex traits and disorders.

At 19q13.32, we observed colocalization (LD r2 ≥ 0.6) between the amplitude of the precuneus region in the default mode and central executive networks and Alzheimer’s disease (shared index SNP rs429358). Location of the displayed rsfMRI trait (amplitude of the precuneus) is illustrated in the right panel.

Extended Data Fig. 2 Genetic locus at 2p16.1 associated with both rsfMRI trait of brain activity and brain-related complex traits and disorders.

At 2p16.1, we observed colocalization (LD r2 ≥ 0.6) between the functional connectivity among the default mode, central executive, and salience networks (index SNP rs2678890) and schizophrenia (index SNP rs1518395). Location of the displayed rsfMRI trait (functional connectivity between precuneus & cuneus and superior frontal & middle frontal regions) is illustrated in the right panel.

Supplementary information

Supplementary Information

Supplementary Note, Supplementary Figures 1-29, and captions for Supplementary Tables 1-26.

Reporting Summary

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Supplementary Table 1

Supplementary Tables 1-26

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Zhao, B., Li, T., Smith, S.M. et al. Common variants contribute to intrinsic human brain functional networks. Nat Genet 54, 508–517 (2022). https://doi.org/10.1038/s41588-022-01039-6

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  • DOI: https://doi.org/10.1038/s41588-022-01039-6

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