The common variant genetic link between functional and structural phenotypes of the human brain
Presented During:
Wednesday, June 25, 2025: 5:45 PM - 7:00 PM
Brisbane Convention & Exhibition Centre
Room:
M2 (Mezzanine Level)
Poster No:
1390
Submission Type:
Abstract Submission
Authors:
Yuankai He1, Rafael Romero-Garcia2,1, Bin Wan3,4,5, Edward Bullmore1, Sofie Valk4,5, Richard Bethlehem6, Varun Warrier1
Institutions:
1Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom, 2University of Sevilla, Sevilla, Sevilla, 3University Hospitals of Genève, Genève, Thonex, 4Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Saxony, Germany, 5Institute of Neuroscience and Medicine (INM-7: Brain and Behavior), Research Center Jülich, Jülich, North Rhine-Westphalia, Germany, 6Department of Psychology, University of Cambridge, Cambridge, United Kingdom
First Author:
Co-Author(s):
Rafael Romero-Garcia
University of Sevilla|Department of Psychiatry, University of Cambridge
Sevilla, Sevilla|Cambridge, United Kingdom
University of Sevilla|Department of Psychiatry, University of Cambridge
Sevilla, Sevilla|Cambridge, United Kingdom
Bin Wan
University Hospitals of Genève|Max Planck Institute for Human Cognitive and Brain Sciences|Institute of Neuroscience and Medicine (INM-7: Brain and Behavior), Research Center Jülich
Genève, Thonex|Leipzig, Saxony, Germany|Jülich, North Rhine-Westphalia, Germany
University Hospitals of Genève|Max Planck Institute for Human Cognitive and Brain Sciences|Institute of Neuroscience and Medicine (INM-7: Brain and Behavior), Research Center Jülich
Genève, Thonex|Leipzig, Saxony, Germany|Jülich, North Rhine-Westphalia, Germany
Sofie Valk
Max Planck Institute for Human Cognitive and Brain Sciences|Institute of Neuroscience and Medicine (INM-7: Brain and Behavior), Research Center Jülich
Leipzig, Saxony, Germany|Jülich, North Rhine-Westphalia, Germany
Max Planck Institute for Human Cognitive and Brain Sciences|Institute of Neuroscience and Medicine (INM-7: Brain and Behavior), Research Center Jülich
Leipzig, Saxony, Germany|Jülich, North Rhine-Westphalia, Germany
Introduction:
Graph metrics (Rubinov & Sporns, 2010) derived from the resting-state network and asymmetry thereof have both produced reliable correlates of various neuropsychiatric disorders. However, these phenotypic correlates do not mirror those identified from structural MRI, raising the question if different biological processes underlie brain structure and function, and if they have different clinical correlates. Initial results have shown genetic correlations between the resting-state network and neuropsychiatric disorders (Bell et al., 2022; Roelfs et al., 2023; Zhao et al., 2022), but these results only covered sporadic phenotypes. Hence, we conducted a systematic genome-wide association study (GWAS) across six graph metrics at global, hemispheric and regional levels and their asymmetry (fig. 1), and identified their shared genetics with other imaging-derived phenotypes (IDPs) and 11 neuropsychiatric disorders (fig. 2).
Methods:
Pre-processed resting-state fMRI images were obtained from UK Biobank (n=54,030) and ABCD (n=3,325). Time series were extracted for each region of the HCP atlas (Glasser et al., 2016) and correlated to obtain the resting-state network. Seven graph metrics (fig. 1a) were derived at global and regional levels, and their asymmetry was measured by correlating corresponding regions of both hemispheres. GWAS was then conducted and meta-analysed across the two datasets (n=57,355). We then estimated the heritability and genetic correlation between the graph metrics, and tested the enrichment in different functional networks and histological cortical types.
To identify the shared genetics between the graph metrics and other IDPs, we obtained phenotype data and GWAS summary statistics for functional gradients (Wan et al., in prep), brain macro- and microstructure (Warrier et al., 2023). We estimated the genetic correlation between these IDPs and graph metrics at global and regional levels, and fine-mapped shared causal variants. The same analysis was repeated for 11 neuropsychiatric disorders to establish the clinical relevance of the graph metrics.
To identify the shared genetics between the graph metrics and other IDPs, we obtained phenotype data and GWAS summary statistics for functional gradients (Wan et al., in prep), brain macro- and microstructure (Warrier et al., 2023). We estimated the genetic correlation between these IDPs and graph metrics at global and regional levels, and fine-mapped shared causal variants. The same analysis was repeated for 11 neuropsychiatric disorders to establish the clinical relevance of the graph metrics.
Results:
We identified one genome-wide significant locus for global phenotypes (fig. 1c) and seven study-wide significant (p<3.11e-11) loci at regional level. All global phenotypes and asymmetry had modest but significant heritability (ranging 0.044-0.06), and show two genetically correlated clusters: one representing global graph metrics (rg>0.81) and another representing asymmetry phenotypes (rg>0.74) supporting different aetiological processes underlying graph metrics and their asymmetry (fig. 1d). Regional graph metrics were mostly genetically correlated with global phenotypes except for limbic regions (fig. 1e).
Global and asymmetry phenotypes showed different correlation patterns with other IDPs (fig. 2a). Whilst global phenotypes were mainly genetically correlated with macro-structure (rg=0.07-0.28) and selectively with the second functional gradient (rg=0.06-0.38), asymmetry was correlated with microstructure (rg=0.25-0.53) and the top three functional gradients (rg=0.24-0.63). This is supported by local-level enrichment of microstructure in idiotypic regions of the left hemisphere (fig. 2b). Similarly, whilst global phenotypes were genetically correlated with autism (rg=0.16-0.26, fig. 2c), particularly in frontoparietal regions (fig. 2d), asymmetry was weakly correlated with a range of mood and anxiety disorders (rg=-0.06 ~ -0.47, fig. 2c).
Global and asymmetry phenotypes showed different correlation patterns with other IDPs (fig. 2a). Whilst global phenotypes were mainly genetically correlated with macro-structure (rg=0.07-0.28) and selectively with the second functional gradient (rg=0.06-0.38), asymmetry was correlated with microstructure (rg=0.25-0.53) and the top three functional gradients (rg=0.24-0.63). This is supported by local-level enrichment of microstructure in idiotypic regions of the left hemisphere (fig. 2b). Similarly, whilst global phenotypes were genetically correlated with autism (rg=0.16-0.26, fig. 2c), particularly in frontoparietal regions (fig. 2d), asymmetry was weakly correlated with a range of mood and anxiety disorders (rg=-0.06 ~ -0.47, fig. 2c).
Conclusions:
Overall, different graph metrics describing network integration and segregation have highly overlapping genetic underpinnings, whereas asymmetry of the functional network topology represents a distinct biological process. The strong correlation between global graph metrics and macrostructure, and between functional asymmetry, microstructure and gradient hierarchy suggests the existence of principal genetic axes organising brain structure and function, which might in turn have different clinical correlates.
Genetics:
Genetic Association Studies 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 1
Keywords:
Autism
FUNCTIONAL MRI
Multivariate
White Matter
Other - Genome-wide association study; functional gradients; connectome; graph theory
1|2Indicates the priority used for review
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Glasser, M. F., Coalson, T. S., Robinson, E. C., Hacker, C. D., Harwell, J., Yacoub, E., Ugurbil, K., Andersson, J., Beckmann, C. F., Jenkinson, M., Smith, S. M., & Van Essen, D. C. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536(7615), 171–178. https://doi.org/10.1038/nature18933
Roelfs, D., Frei, O., Van Der Meer, D., Tissink, E., Shadrin, A., Alnaes, D., Andreassen, O. A., Westlye, L. T., & Kaufmann, T. (2023). Shared genetic architecture between mental health and the brain functional connectome in the UK Biobank. BMC Psychiatry, 23(1), 461. https://doi.org/10.1186/s12888-023-04905-7
Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52(3), 1059–1069. https://doi.org/10.1016/j.neuroimage.2009.10.003
Wan, B., He, Y., Warrier, V., Kirschner, M., Eickhoff, S. B., Bethlehem, R. A. I., Valk, S. L. (in prep). Genetics, transcriptomics, and neuropsychiatric underpinings of cortical functional gradients
Warrier, V., Stauffer, E.-M., Huang, Q. Q., Wigdor, E. M., Slob, E. A. W., Seidlitz, J., Ronan, L., Valk, S. L., Mallard, T. T., Grotzinger, A. D., Romero-Garcia, R., Baron-Cohen, S., Geschwind, D. H., Lancaster, M. A., Murray, G. K., Gandal, M. J., Alexander-Bloch, A., Won, H., Martin, H. C., … Bethlehem, R. A. I. (2023). Genetic insights into human cortical organization and development through genome-wide analyses of 2,347 neuroimaging phenotypes. Nature Genetics, 55(9), 1483–1493. https://doi.org/10.1038/s41588-023-01475-y
Zhao, B., Li, T., Smith, S. M., Xiong, D., Wang, X., Yang, Y., Luo, T., Zhu, Z., Shan, Y., Matoba, N., Sun, Q., Yang, Y., Hauberg, M. E., Bendl, J., Fullard, J. F., Roussos, P., Lin, W., Li, Y., Stein, J. L., & Zhu, H. (2022). Common variants contribute to intrinsic human brain functional networks. Nature Genetics, 54(4), 508–517. https://doi.org/10.1038/s41588-022-01039-6