A genetically informed brain atlas for enhancing brain imaging genomics

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.

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Publication Details

Journal
Nature Communications
Published
2025-04-14
DOI
https://doi.org/10.1038/s41467-025-57636-6
Citations
3
Primary Topic
Gene expression and cancer classification
Type
article
Field-Weighted Citation Impact
2.42

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article

A genetically informed brain atlas for enhancing brain imaging genomics

Yuhan Cui, Shu Yang, Zixuan Wen, Changgee Chang et al.
3 citations
Nature Communications
Gene expression and cancer classification
2.42
article

A genetically informed brain atlas for enhancing brain imaging genomics

Yuhan Cui, Shu Yang, Zixuan Wen, Changgee Chang, Güray Erus, Jingxuan Bao, Manu Shivakumar, Junhao Wen, Zhijian Yang, Jiong Chen, Shizhuo Mu
article en
3 citations

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.

Nature CommunicationsVol. 16(1)
University of Southern California (US), Alzheimer's Association (US), Biogen (United States) (US), Mount Sinai Hospital (US), General Electric (United States) (US), University of Wisconsin–Madison (US), University of Connecticut (US), Fordham University (US), University of Pittsburgh (US), Duke University (US), University of California, San Francisco (US), United States Naval Academy (US), University of Washington (US), University of California, Irvine (US), Washington University in St. Louis (US), University of Michigan (US), Harvard University Press (US), Yale University (US), University of California San Diego (US), Massachusetts General Hospital (US), Denali Therapeutics (United States) (US), PC Krause & Associates (United States) (US), Mayo Clinic in Arizona (US), Applied Mathematics (United States) (US), Prevent Alzheimer’s Disease 2020 (US), National Institute on Aging (US), Center for Innovation (US), New York Genome Center (US), Eisai (United States) (US), National Institute of Mental Health (US), Northern California Institute for Research and Education (US), Indiana University School of Medicine, Rush University (US), Indiana University – Purdue University Indianapolis (US), Columbia University (US), University of Pennsylvania (US), University of California, Davis (US), University of California, Berkeley (US)
U.S. Department of Defense, Alzheimer's Association, Bristol-Myers Squibb, Eli Lilly and Company, Pfizer, Biogen, University of Southern California, Meso Scale Diagnostics, Alzheimer's Disease Neuroimaging Initiative, BioClinica, Novartis Pharmaceuticals Corporation, Northern California Institute for Research and Education, Eisai, Servier, H. Lundbeck A/S, IXICO, National Institutes of Health, Genentech, Canadian Institutes of Health Research
Openalex Percentile: Top 12%
Gene expression and cancer classification
2.42
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