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A genetically informed brain atlas for enhancing brain imaging genomics

Author

Listed:
  • Jingxuan Bao

    (University of Pennsylvania Perelman School of Medicine
    University of Pennsylvania Perelman School of Medicine)

  • Junhao Wen

    (Columbia University
    Columbia University
    New York Genome Center (NYGC))

  • Changgee Chang

    (Indiana University School of Medicine)

  • Shizhuo Mu

    (University of Pennsylvania Perelman School of Medicine)

  • Jiong Chen

    (University of Pennsylvania Perelman School of Medicine
    University of Pennsylvania)

  • Manu Shivakumar

    (University of Pennsylvania Perelman School of Medicine
    University of Pennsylvania Perelman School of Medicine)

  • Yuhan Cui

    (University of Pennsylvania Perelman School of Medicine)

  • Guray Erus

    (University of Pennsylvania Perelman School of Medicine)

  • Zhijian Yang

    (University of Pennsylvania Perelman School of Medicine
    University of Pennsylvania)

  • Shu Yang

    (University of Pennsylvania Perelman School of Medicine)

  • Zixuan Wen

    (University of Pennsylvania Perelman School of Medicine
    University of Pennsylvania)

  • Yize Zhao

    (Yale University School of Public Health)

  • Dokyoon Kim

    (University of Pennsylvania Perelman School of Medicine)

  • Duy Duong-Tran

    (University of Pennsylvania Perelman School of Medicine
    United States Naval Academy)

  • Andrew J. Saykin

    (Indiana University)

  • Bingxin Zhao

    (University of Pennsylvania)

  • Christos Davatzikos

    (University of Pennsylvania Perelman School of Medicine)

  • Qi Long

    (University of Pennsylvania Perelman School of Medicine)

  • Li Shen

    (University of Pennsylvania Perelman School of Medicine)

Abstract

Brain imaging genomics has manifested considerable potential in illuminating the genetic determinants of human brain structure and function. This has propelled us to develop the GIANT (Genetically Informed brAiN aTlas) that accounts for genetic and neuroanatomical variations simultaneously. Integrating voxel-wise heritability and spatial proximity, GIANT clusters brain voxels into genetically informed regions, while retaining fundamental anatomical knowledge. Compared to conventional (non-genetics) brain atlases, GIANT exhibits smaller intra-region variations and larger inter-region variations in terms of voxel-wise heritability. As a result, GIANT yields increased regional SNP heritability, enhanced polygenicity, and its polygenic risk score explains more brain volumetric variation than traditional neuroanatomical brain atlases. We provide extensive validation to GIANT and demonstrate its neuroanatomical validity, confirming its generalizability across populations with diverse genetic ancestries and various brain conditions. Furthermore, we present a comprehensive genetic architecture of the GIANT regions, covering their functional annotation at the molecular levels, their associations with other complex traits/diseases, and the genetic and phenotypic correlations among GIANT-defined imaging endophenotypes. In summary, GIANT constitutes a brain atlas that captures the complexity of genetic and neuroanatomical heterogeneity, thereby enhancing the discovery power and applicability of imaging genomics investigations in biomedical science.

Suggested Citation

  • Jingxuan Bao & Junhao Wen & Changgee Chang & Shizhuo Mu & Jiong Chen & Manu Shivakumar & Yuhan Cui & Guray Erus & Zhijian Yang & Shu Yang & Zixuan Wen & Yize Zhao & Dokyoon Kim & Duy Duong-Tran & Andr, 2025. "A genetically informed brain atlas for enhancing brain imaging genomics," Nature Communications, Nature, vol. 16(1), pages 1-18, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-57636-6
    DOI: 10.1038/s41467-025-57636-6
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