Presented During:
Monday, June 24, 2024: 5:45 PM - 7:00 PM
COEX
Room:
Grand Ballroom 101-102
Poster No:
861
Submission Type:
Abstract Submission
Authors:
Isaac Sebenius1, Varun Warrier2, Richard Bethlehem3, Eva Stauffer2, Richard Dear2, Sarah Morgan1, Edward Bullmore4
Institutions:
1Cambridge University, Cambridge, Cambridgeshire, 2University of Cambridge, Cambridge, Cambridgeshire, 3Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom, 4University of Cambridge, Cambridge, United Kingdom
First Author:
Co-Author(s):
Richard Bethlehem
Autism Research Centre, Department of Psychiatry, University of Cambridge
Cambridge, United Kingdom
Eva Stauffer
University of Cambridge
Cambridge, Cambridgeshire
Richard Dear
University of Cambridge
Cambridge, Cambridgeshire
Introduction:
Recent imaging-genetics research has demonstrated that heritable MRI-derived brain structural features show important genetic overlaps with brain function and psychopathology [1,2,3]. Yet, while the brain forms a genetically-coordinated network, existing work on the genetics of brain structure has focused on structural features at the global or regional level. As such, the genetics of network-based measures of brain structure remain largely unknown.
In this work, we conducted hundreds of genome-wide association studies (GWAS) to comprehensively characterize the genetics of structural similarity networks in the brain. Specifically, using N>30,000 subjects from the UK Biobank, we studied the genetics of Morphometric INverse Divergence (MIND), a robust and biologically-validated method to construct structural similarity networks from MRI [4]. We identified 109 independent genomic regions associated with MIND, many of which were not associated with the structural feature from which the networks were derived.
We observed positive genetic correlations between MIND network edges and the corresponding edges from functional connectivity (FC) networks, offering new evidence for a shared genetic basis for brain structure and function. Moreover, we identified putative causal relationships between MIND and functional connectivity that were specific to the association cortex.
Finally, we observed evidence for local genetic correlations between MIND network connectivity and schizophrenia, identifying specific genes such as CACNA1c that may disproportionately contribute to the shared genetic basis of brain connectivity and mental illness.
Methods:
MIND networks based on neurite density index (NDI) were constructed for 31,365 subjects of European ancestry in the UK Biobank using a coarse-grained version of the HCP parcellation with 276 network edges (Fig. 1a) [4]. Using the process described in [1], we conducted a GWAS on each of these MIND network edges and the corresponding edges from FC networks. Genetic correlations were calculated using LDSC and HDL [5,6]. Mendelian randomization was performed using MRAPSS [7]. Local genetic correlations were performed with SUPERGNOVA [8].
Results:
109 independent genomic regions were associated with at least one of the 276 MIND network edges at an experiment-wide significance threshold (P < 1.8e-10, Fig. 1b). Using pairwise LDSC and Louvain clustering, we identified three genetically-defined clusters of MIND network edges corresponding to distinct anatomy and functional processes: edges corresponding to 1) visual cortex, 2) paralimbic cortex, and 3) association and somatomotor cortex (Fig. 2a).
We observed a shared genetic signal between brain structure and function; 87% of network edges showed positive genetic correlations between MIND and functional connectivity (FC), and over one-third of these relationships were statistically significant after FDR-correction (Fig. 2b). As shown in Fig. 2c, these structure-function genetic correlations were strongest in clusters 2 and 3. Using Mendelian randomization, we observed a putative causal relationship (β = 0.18, P = 0.0002) between MIND and FC specific to connectivity in cluster 3 (Fig. 2d).
We observed 81 genomic regions with significant local genetic correlations between MIND and schizophrenia (Fig. 2e). Many genomic regions showed local genetic effects in discordant directions, suggesting that global genetic correlations may not capture the full extent of the genetic relationship between brain network connectivity and SCZ. The region that most strongly contributed to MIND connectivity and SCZ fell within chromosome 12p13.33 and contained a single gene, CACNA1c, which encodes a calcium channel subunit and has been robustly associated with SCZ [9].
![Supporting Image: ohbm1-cropped.jpg](https://files.aievolution.com/prd/hbm2401/abstracts/abs_3655/ohbm1-cropped.jpg)
![Supporting Image: ohbm2-cropped.jpg](https://files.aievolution.com/prd/hbm2401/abstracts/abs_3655/ohbm2-cropped.jpg)
Conclusions:
Through genome-wide analysis of MIND network phenotypes, we offer novel evidence for shared genetics underlying structural similarity networks, functional connectivity, and schizophrenia.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia)
Genetics:
Genetic Association Studies 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Keywords:
Other - GWAS; MIND networks; microstructure; Schizophrenia
1|2Indicates the priority used for review
Provide references using author date format
[1] Warrier, V. et al. Genetic insights into human cortical organization and development through genome-wide analyses of 2,347 neuroimaging phenotypes. Nat Genet. 55, 1483–1493 (2023).
[2] Stauffer, EM. et al. The genetic relationships between brain structure and schizophrenia. Nat Commun 14, 7820 (2023). https://doi.org/10.1038/s41467-023-43567-7
[3] 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
[4] Sebenius, I. et al. Robust estimation of cortical similarity networks from brain MRI. Nat Neurosci 26, 1461–1471 (2023). https://doi.org/10.1038/s41593-023-01376-7
[5] Bulik-Sullivan, B., Loh, PR., Finucane, H. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 47, 291–295 (2015). https://doi.org/10.1038/ng.3211
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[7] Hu X, et al. Mendelian randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics. PNAS 2022 Jul 12;119(28):e2106858119. doi: 10.1073/pnas.2106858119. Epub 2022 Jul 5.
[8] Zhang, Y., Lu, Q., Ye, Y. et al. SUPERGNOVA: local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits. Genome Biol 22, 262 (2021). https://doi.org/10.1186/s13059-021-02478-w
[9] Moon, A L, et al. CACNA1C: Association With Psychiatric Disorders, Behavior, and Neurogenesis, Schizophrenia Bulletin, Volume 44, Issue 5, September 2018, Pages 958–965, https://doi.org/10.1093/schbul/sby096
[10] Foley, C.N., Staley, J.R., Breen, P.G. et al. A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits. Nat Commun 12, 764 (2021). https://doi.org/10.1038/s41467-020-20885-8