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Genetic architecture of the white matter connectome of the human brain - PubMed

  • ️Sun Jan 01 2023

Genetic architecture of the white matter connectome of the human brain

Zhiqiang Sha et al. Sci Adv. 2023.

Abstract

White matter tracts form the structural basis of large-scale brain networks. We applied brain-wide tractography to diffusion images from 30,810 adults (U.K. Biobank) and found significant heritability for 90 node-level and 851 edge-level network connectivity measures. Multivariate genome-wide association analyses identified 325 genetic loci, of which 80% had not been previously associated with brain metrics. Enrichment analyses implicated neurodevelopmental processes including neurogenesis, neural differentiation, neural migration, neural projection guidance, and axon development, as well as prenatal brain expression especially in stem cells, astrocytes, microglia, and neurons. The multivariate association profiles implicated 31 loci in connectivity between core regions of the left-hemisphere language network. Polygenic scores for psychiatric, neurological, and behavioral traits also showed significant multivariate associations with structural connectivity, each implicating distinct sets of brain regions with trait-relevant functional profiles. This large-scale mapping study revealed common genetic contributions to variation in the structural connectome of the human brain.

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Figures

Fig. 1.
Fig. 1.. Schematic of white matter network construction within an individual brain.

Network nodes were defined by mapping the Automated Anatomical Labeling atlas from common MNI space to individual space, with 45 regions per hemisphere (including cortical and subcortical structures). The edge between each pair of regions was defined as the number of streamlines constructed by tractography based on the corresponding diffusion tensor image, adjusted for the combined volume of the two connected regions. The process yielded a zero-diagonal symmetrical 90 × 90 undirected connectivity matrix for each of 30,810 participants (the top triangles were then used for subsequent analyses).

Fig. 2.
Fig. 2.. SNP-based heritability and mvGWAS analyses of node-level connectivity and edge-level connectivity in 30,810 participants.

(A) All 90 node-level (i.e., regional) connectivities showed significant SNP-based heritability after Bonferroni correction, ranging from 7.8 to 29.5%. (B) Eight hundred fifty-one of 947 edge-level connectivities showed significant SNP-based heritability after Bonferroni correction, ranging from 4.6 to 29.5%. Right: Brain maps. Left: Nodes grouped by frontal, prefrontal, parietal, temporal, and occipital cortical lobes and subcortical structures. Heritabilities can be visualized interactively in a dynamic Web-based interface (see “Data and materials availability” statement). (C) Miami plot for mvGWAS of 90 node-level connectivities (top) and 851 edge-level connectivities (bottom). The black lines indicate the genome-wide significance threshold P < 2.5 × 10−8 (Materials and Methods).

Fig. 3.
Fig. 3.. Genes associated with variation in the adult white matter connectome are enriched for specific neurodevelopmental roles.

(A) Sixty-one functionally defined gene sets showed significant enrichment of association with node-level connectivity. (B) Seventy-two functionally defined gene sets showed significant enrichment of association with edge-level connectivity. (C and D) On the basis of BrainSpan data from 11 life-span stages or 29 age groups, genes associated with variation in (C) adult node-level connectivity and (D) adult edge-level connectivity show up-regulation in the human brain prenatally. (E and F) On the basis of single-cell gene expression data from the prenatal brain, genes associated with variation in (E) adult node-level connectivity show up-regulation in astrocytes when considering all prenatal age groups combined and in stem cells and microglia at 10 gestational weeks (GW), astrocytes at 19 GW, and GABAergic neurons and astrocytes at 26 GW when breaking down by developmental stages, and similarly, genes associated with variation in (F) adult edge-level connectivity show up-regulation in astrocytes when considering all prenatal age groups combined and in stem cells and microglia at 10 GW, neurons at 16 GW, and GABAergic neurons and astrocytes at 26 GW when breaking down by developmental stages. (C to F) Black lines indicate the significance threshold P < 0.05 after Bonferroni correction within each analysis. PCW, postconceptional weeks.

Fig. 4.
Fig. 4.. Genetics of left-hemisphere language network connectivity.

(A) Four regions with core functions in the left-hemisphere language network, encompassing the classically defined Broca’s (frontal lobe) and Wernicke’s (temporal lobe) areas. Also shown are the six edges connecting these four regions when considered as network nodes. (B) Visualization of the six edges in an example individual, with red representing connections between the pars opercularis and pars triangularis, green representing connections between the middle temporal and superior temporal cortex, gold representing connections between the pars opercularis and middle temporal cortex, blue representing connections between the pars opercularis and superior temporal cortex, purple representing connections between the pars triangularis and middle temporal cortex, and yellow representing connections between pars triangularis and superior temporal cortex. (C) The closest genes to independent lead SNPs from the brain-wide mvGWAS of edge-level connectivity, which showed significant association with at least one of the six left-hemisphere language network edges (Bonferroni correction at 0.05; table S26).

Fig. 5.
Fig. 5.. Polygenic dispositions to various brain-related disorders or behavioral traits show multivariate associations with regional (node-level) white matter connectivities in 30,810 participants.

Loadings are shown from canonical correlation analyses that indicate the extent and direction to which each node-level connectivity is associated with polygenic scores for (A) schizophrenia, (B) bipolar disorder, (C) autism, (D) attention-deficit/hyperactivity disorder, (E) left-handedness, (F) Alzheimer’s disease, (G) amyotrophic lateral sclerosis, and (H) epilepsy. A positive loading (red) indicates a higher–node-level connectivity associated with increased polygenic disposition for a given disorder/behavioral trait, while a negative loading (blue) represents a lower–node-level connectivity associated with increased polygenic disposition for a given disorder/behavioral trait. Word clouds represent functions associated with the map of regions (nodes) showing the strongest loadings (|r| > 0.2) for each polygenic score. Functions were assigned using large-scale meta-analyzed functional neuroimaging data (Materials and Methods). The font sizes in the word clouds represent correlation magnitudes between the meta-analyzed functional maps for those terms and the coactivation map for the set of regions associated with each polygenic score. See table S30 for the correlation coefficients. wm, working memory; dmn, default-mode network.

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