Robust multi-layered evidence is provided for the causal roles of white matter microstructural abnormalities, metabolic dysregulation, and regional gene expression in glioma development and indicates that significant regional genes are heavily involved in DNA metabolism and cell cycle pathways.
Abstract
Background This study aimed to systematically characterize genetically links and Mediation mechanism between glioma susceptibility and brain microstructure, cross-compartment metabolic profiles, and region-specific gene expression, followed by functional validation. Methods Using GWAS data from 12,488 glioma cases and 18,169 controls, we performed two-sample MR (TSMR) and summary data-based MR (SMR) analyses. We evaluated 587 brain imaging-derived phenotypes (IDPs) from diffusion and structural MRI; levels of 962 brain tissue metabolites, 440 cerebrospinal fluid (CSF) metabolites, and 1,400 plasma metabolites; and eQTL based gene expression across 13 brain regions. A two-step MR design was employed for mediation analysis, and the key gene identified, HEATR3, underwent clinical correlation and in vitro functional validation. Results TSMR identified 6 IDPs significantly associated with all glioma subtypes. Specifically, elevated intracellular volume fraction (ICVF) in the corpus callosum and cingulum increased risk, while increased mean diffusivity (MD) in the posterior limb of the right internal capsule was protective. Integrated SMR and TSMR revealed that elevated HEATR3 expression across all 13 brain regions significantly increases glioma risk. Functional experiments confirmed HEATR3 is upregulated in glioma, correlates with poor patient prognosis, and promotes malignant progression in vitro. Mediation analysis showed that HEATR3’s effect on glioma risk is partially mediated by specific white matter IDPs. Metabolic analysis revealed that higher levels of orotate—a product of de novo pyrimidine synthesis—in plasma, CSF, and brain tissue significantly increase GBM risk. Mediation analysis suggested that the effect of plasma orotate on glioma risk is partially mediated by the ICVF in the right cingulum hippocampus. Enrichment analyses indicated that significant regional genes are heavily involved in DNA metabolism and cell cycle pathways. Conclusion This study provides robust multi-layered evidence for the causal roles of white matter microstructural abnormalities, metabolic dysregulation, and regional gene expression in glioma development. These findings offer novel genetic insights and potential therapeutic targets for precision medicine in glioma.
It is revealed that the genetically predisposed higher risk of diabetic maculopathy was associated with increased salience network connectivity, shedding light on the neural drivers of diabetic pathologies.
Lin Chen, Hsin-Yu Hsieh, Nan Cheng et al.· Brain Research Bulletin· 0 citations
These findings support a neuroimmune model in which pleiotropic germline variants act via microglia and excitatory neurons to link seizure biology with tumor immunity and prognosis, and reveal a common genetic architecture between glioma and epilepsy.
Xiangling Feng, Jiahao Zhou, Zhen He et al.· Current Cancer Drug Targets· 0 citations
An atlas of brain IDPs associated with bipolar disorder is established, by integrating epidemiological and genetic evidence, and candidate IDPs that were consistently associated with BD are highlighted across complementary analyses.
Wenzhuo Yang, Lin Pan, Haoqun Xie et al.· BMC Medicine· 0 citations
Genome-wide association studies (GWAS) have advanced the quest to understand how specific genetic variants influence human brain structure and function. Recent work has identified hundreds of common variants associated with subcortical brain volumes, sparking interest in how these genetic markers overlap across brain networks. While this can be estimated by hierarchical clustering of the genetic correlation matrix to identify modular patterns of shared architecture, no brain-wide maps of these effects are available. To address this, we computed polygenic scores (PGS) from loci associated with ten brain volume regions of interest (ROIs): nine major subcortical structures and intracranial volume, with each locus weighted by its association with regional volume. In an independent sample from the discovery GWAS, we performed large-scale segmentation of 3D volumetric T1-weighted MRI scans using voxel-based morphometry (VBM) to map 3D profile of regions where gray matter volume (GMV) was associated with each PGS. We found statistically significant, localized effects for PGS defined for the amygdala, thalamus, and basal ganglia, but PGS for brainstem volume was associated with widespread differences throughout the brain. These brain-wide maps reveal patterns consistent with both localized and distributed genetic influences, offering a novel approach to interpret the genomic architecture of brain structure.
Emma J Gleave, L. García-Marín, Z. Ceja et al.· bioRxiv· 0 citations
Local brain age (LBA) is a spatially resolved biomarker of brain aging that captures regional deviations from chronological age, yet its genetic architecture in the subcortex remains unexplored. Here, we present the first genome-wide association study (GWAS) of subcortical LBA, estimated using a deep neural network applied to T1-weighted MRI scans from 41,957 cognitively normal participants in the UK Biobank. We computed LBA across 14 subcortical structures and identified 14 significant single-nucleotide polymorphisms (SNPs) across nine independent loci. These variants map to genes involved in cellular homeostasis, gene regulation, and synaptic and developmental signaling. A prominent signal emerged at the 17q21.31 haplotype, encompassing MAPT-related regulatory architecture, with significant associations across all subcortical regions. Across loci, we observed a recurring spatial pattern in which effect sizes are relatively larger in metabolically central structures such as the pallidum and thalamus compared to limbic regions. Together, these findings support a spatially structured pattern of genetic associations in subcortical brain aging. This work supports subcortical LBA as a genetically informed phenotype and provides a framework for linking common genetic variation to region-specific vulnerability and resilience in neurodegenerative disease.
Nicholas J. Kim, Ayati Mishra, Jeremy S Yu et al.· GeroScience· 0 citations
Genome-wide association studies (GWAS) have identified hundreds of common genetic variants associated with regional brain volumes, enabling the construction of polygenic scores (PGS) that summarize genetic predisposition for variation in specific neuroanatomical traits. To investigate how these genetic influences are exerted spatially throughout the brain, we computed PGS for ten brain volume phenotypes, including nine major subcortical structures and intracranial volume. Each locus was weighted by its estimated GWAS effect size on regional volume in the original GWAS. In an independent, non-overlapping sample of 2,830 UK Biobank participants, we performed whole-brain voxel-based morphometry (VBM) analyses of 3D volumetric brain MRI to reveal voxel-wise associations between each PGS and modulated gray matter volume (GMV). To probe genetic effects across multiple spatial scales, analyses were repeated across Gaussian smoothing kernels ranging from 2-mm to 12-mm full-width at half-maximum (FWHM). Several PGS demonstrated highly significant associations with GMV, including localized effects in the hippocampus, amygdala, thalamus, and basal ganglia, whereas the brainstem PGS showed more widespread associations throughout the brain. For most of the PGS, the fraction of voxels surviving the false discovery rate (FDR) correction increased with increasing FWHM. Peak voxel-wise significance was often strongest at intermediate smoothing levels. Hippocampal significance maps showed progressively larger regions of significant signal at higher smoothing levels, and subsampling showed that detectable signal remained present even with substantial reductions in sample size. These findings suggest that genetic influences on brain morphology are expressed across multiple spatial scales, with consequences that may help to guide the design of deep learning methods to discover genomic loci associated with brain structure and brain diseases.
Emma J Gleave, L. García-Marín, Z. Ceja et al.· bioRxiv· 0 citations