Genetic profile differences related to gray matter volumes in amyloid-positive and negative groups
Poster No:
244
Submission Type:
Abstract Submission
Authors:
Seulgi Kim1,2, Bumhee Park2,3
Institutions:
1Department of Biomedical Sciences, Graduate School of Ajou University, Suwon, Korea, Republic of, 2Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea, Republic of, 3Office of Biostatistics, Medical Research Collaborating Center, Ajou Research Institute for innovative Medicine, Ajou University Medical Center, Suwon, Korea, Republic of
First Author:
Seulgi Kim
Department of Biomedical Sciences, Graduate School of Ajou University|Department of Biomedical Informatics, Ajou University School of Medicine
Suwon, Korea, Republic of|Suwon, Korea, Republic of
Department of Biomedical Sciences, Graduate School of Ajou University|Department of Biomedical Informatics, Ajou University School of Medicine
Suwon, Korea, Republic of|Suwon, Korea, Republic of
Co-Author:
Bumhee Park
Department of Biomedical Informatics, Ajou University School of Medicine|Office of Biostatistics, Medical Research Collaborating Center, Ajou Research Institute for innovative Medicine, Ajou University Medical Center
Suwon, Korea, Republic of|Suwon, Korea, Republic of
Department of Biomedical Informatics, Ajou University School of Medicine|Office of Biostatistics, Medical Research Collaborating Center, Ajou Research Institute for innovative Medicine, Ajou University Medical Center
Suwon, Korea, Republic of|Suwon, Korea, Republic of
Introduction:
Alzheimer's disease (AD) is a highly heritable dementia, its heritability reaching out approximately 60-80% (Biffi, Alessandro, et al. 2010) Previous studies have identified several genes as early biomarkers and have suggested some genes as potential therapeutic targets for AD. To identify the impact of genetics on brain anatomy in Alzheimer's disease, we examined patterns of structural variation between the two groups and investigate the genetic influence on neuroanatomical variation in AD.
Methods:
We acquired the resting state T1-weighted image data from 503 patients, which were collected from the BICWALZS (Biobank Innovation for chronic Cerebrovascular disease With ALZheimer's disease Study) (Roh et al., 2022). Voxel-Based Morphometry (VBM) analyses, representing brain structural volumes, was performed to compare Gray Matter Volumes (GMV) between two groups using the Statistical Parametric Mapping software package (SPM). We obtained six post-mortem brains from AHBA and processed the transcriptomic data using the abagen toolbox (Arnatkeviciute, Aurina, et al, 2023; Markello, Ross D., et al., 2021). Tissue samples were assigned to brain regions by finding the closest region using modified MNI coordinates, and the atlas used Schaefer200. Gene expression values were normalised separately for each donor.
We divided the group into amyloid-positive and amyloid-negative and conducted the analysis using general linear model (GLM). Age, gender and education were included as covariates in the model. Additionally, PLS regression was applied to investigate the GMV t-maps and their association with gene transcriptomic signatures across the groups. PLS regression was used to determine the relationship between group-specific GMV changes and the transcriptional profiles of 15,632 genes.
We divided the group into amyloid-positive and amyloid-negative and conducted the analysis using general linear model (GLM). Age, gender and education were included as covariates in the model. Additionally, PLS regression was applied to investigate the GMV t-maps and their association with gene transcriptomic signatures across the groups. PLS regression was used to determine the relationship between group-specific GMV changes and the transcriptional profiles of 15,632 genes.
Results:
Amyloid-positive patients showed a significant reduction in gray matter volume in the temporal cortex, prefrontal cortex, parahippocampal cortex, and visual cortex compared to amyloid-negative patients. The first component of PLS1 genes (e.g., SST, EFCAB1, GDA, PPP4R4) was a linear combination of gene expression values, which correlated most strongly with GMV differences. When we performed meta-enrichment analysis on the PLS1 genes, we found that they are primarily associated with poor synaptic plasticity and the mRNA metabolic process at synapses.
Conclusions:
These areas have been reported to associate with amyloid accumulation and its effects on brain function. Amyloid accumulation in Alzheimer's disease (AD) has been linked to disruptions in nervous system development, contributing to synaptic degeneration and cognitive decline. As dementia begins to cause changes at the molecular level before symptoms appear, investigating how these molecular changes affect brain structure in the brain can provide more insight into disease mechanisms.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Genetics:
Transcriptomics
Lifespan Development:
Aging
Novel Imaging Acquisition Methods:
Anatomical MRI 2
Keywords:
Cortex
Data analysis
MRI
Neurological
Phenotype-Genotype
STRUCTURAL MRI
1|2Indicates the priority used for review
Abstract Information
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
Biffi, A., Anderson, C. D., Desikan, R. S., Sabuncu, M., Cortellini, L., Schmansky, N., ... & Alzheimer's Disease Neuroimaging Initiative (ADNI. (2010). Genetic variation and neuroimaging measures in Alzheimer disease. Archives of neurology, 67(6), 677-685.
Markello, R. D., Arnatkeviciute, A., Poline, J. B., Fulcher, B. D., Fornito, A., & Misic, B. (2021).
Standardizing workflows in imaging transcriptomics with the abagen toolbox. elife, 10, e72129.
Roh, H. W., Kim, N. R., Lee, D. G., Cheong, J. Y., Seo, S. W., Choi, S. H., ... & Hong, C. H. (2022). Baseline clinical and biomarker characteristics of biobank innovations for chronic cerebrovascular disease with Alzheimer’s disease study: BICWALZS. Psychiatry Investigation, 19(2), 100.