Linking genotypes and gene expression to cortical functional gradients
Poster No:
677
Submission Type:
Abstract Submission
Authors:
Bin Wan1, Yuankai He2, Varun Warrier2, Alexandra John3, Matthias Kirschner1, Simon Eickhoff4, Richard Bethlehem2, Sofie Valk3
Institutions:
1University Hospitals of Genève, Genève, Switzerland, 2University of Cambridge, Cambridge, Cambridge, 3Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Saxony, 4Research Centre Jülich, Jülich, NRW
First Author:
Co-Author(s):
Introduction:
Functional gradients differentiate sensory and higher-order association regions in the human cortex (1) and show subtle variations across individuals. However, to what extent individual variation reflects genetic variation still needs to be completed. Here, we conducted genome-wide association studies (GWAS) to uncover the genetic architecture of functional gradients. Following, we evaluated how the expression of GWAS-informed genes can shape the spatial patterns of functional gradients of the cortex.
Methods:
We leveraged resting-state functional MRI and genotype data of 30,716 subjects from the UK Biobank. Individual functional connectome gradients (G1, G2, and G3) were computed using a nonlinear dimensionality reduction technique, diffusion map embedding (1, 2). We used Procrustes rotations to align individual gradients to the Human Project Connectome S1200 group-level gradients template, increasing inter-individual comparability. We used Pearson r and Euclidean distance to assess the (dis)similarity scores between each individual and the template. Following, GWASs were conducted on the six phenotypes: both similarity scores for each of the 3 gradients. FUMA (3) was used to map the annotated genes. Transcriptomic data of > 15,000 genes in the Allen Human Brain Atlas (AHBA, N = 6), integrated into the ENIGMA toolbox (4), were used to model the spatial pattern of functional gradients in combination with the GWAS outcome.
Results:
We used the >1% minor allele frequency and P < 5 x 10-8 convention to identify common variants. We identified 3 loci containing 187 single-nucleotide polymorphisms (SNPs) for G1 (Pearson r, Figure 1a), two loci containing 155 SNPs for G2, and two loci containing 42 SNPs for G3. Regarding Euclidean distance, we identified 5 loci (3 shared with Pearson r) containing 408 SNPs for G1 (Figure 1b), two loci containing 161 SNPs for G2, and three loci containing 51 SNPs for G3. We could map 6 genes for the Pearson r including FAM200A, NPC3L, PLCE1, GNA12, FHL5, and AMZ1. Regarding Euclidean distance, in addition to the shared genes, we mapped 10 genes including UFL1, FHL5, ANO1, FUT9, TBC1D12, HELLS, CYP2C9, CYP2C8, SLC35G1, and RP11-805J14.3. Ten of them were also available in AHBA (Figure 2a and b). We observed that GWAS-informed gene sets could achieve >95% specificity (i.e., compared with using the same number of random genes) for three gradients (Figure 2c-e). We further used an elastic net regression to select common genes (ADAD2, AMH, CARTPT, COL8A2, IL11, MEI1, CRB2, ZSCAN30) to predict gradients and found the specificities of > 98% for three gradients (Figure 2f and g). GWAS-informed genes are involved in metabolic pathways and AHBA-driven genes are involved in cell-cell signaling pathways.
Conclusions:
Our work offers high-quality biological evidence for the genetic factors, involved in metabolic pathways, shaping functional gradients in the human cortex. At the transcriptomic level, both GWAS-information and AHBA-driven genes capture the gradient axes but further experimental studies (metabolic and/or signaling) are needed to uncover the detailed biological process of brain function.
Genetics:
Genetic Association Studies 1
Transcriptomics 2
Keywords:
Cortex
FUNCTIONAL MRI
1|2Indicates the priority used for review
Abstract Information
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2. R. R. Coifman, S. Lafon, A. B. Lee, M. Maggioni, B. Nadler, F. Warner, S. W. Zucker, Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps. Proc. Natl. Acad. Sci. 102, 7426–7431 (2005).
3. K. Watanabe, E. Taskesen, A. van Bochoven, D. Posthuma, Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).
4. S. Larivière, C. Paquola, B. Park, J. Royer, Y. Wang, O. Benkarim, R. Vos de Wael, S. L. Valk, S. I. Thomopoulos, M. Kirschner, L. B. Lewis, A. C. Evans, S. M. Sisodiya, C. R. McDonald, P. M. Thompson, B. C. Bernhardt, The ENIGMA Toolbox: multiscale neural contextualization of multisite neuroimaging datasets. Nat. Methods 18, 698–700 (2021).