Sex-specific and Cell-type Specific Genomic Associations with Brain Changes in Alzheimer’s Disease
Poster No:
82
Submission Type:
Abstract Submission
Authors:
Jiayu Chen1, Armin Iraji1, Zening Fu1, Pablo Andrés-Camazón2, Jingyu Liu1, Vince Calhoun1
Institutions:
1GSU, Atlanta, GA, 2IiSGM, Madrid, Spain
First Author:
Co-Author(s):
Introduction:
Alzheimer's Disease (AD) is one of the leading causes of death. However, effective disease-modifying treatments remain elusive, significantly influenced by substantial pathological heterogeneity. Sex differences play a significant role in AD heterogeneity[1]. For instance, faster cognitive decline and atrophy rates have been noted in females in different brain regions and disease stages[2]. In addition to sex differences, a substantial amount of heterogeneity is believed to be driven by genetics[1]. This scenario is further complicated by cellular heterogeneity, i.e., genetic variants may present differential cellular responses at different stages of AD[3]. This is now considered one of the key challenges in discovering drugs for AD[4]. The current work examined how cell-type specific genomic risk impacted gray matter changes in AD in a sex-stratified manner.
Methods:
We used the single nucleotide polymorphism (SNPs) and baseline structural magnetic resonance imaging data of the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, including 1882 individuals of European ancestry (45% female, aged 54-91, 670 cognitive normal (CN), 878 mild cognitive impairment (MCI), and 334 AD-related dementias (ADRD)). The sMRI images were preprocessed using the standard statistical parametric mapping 12 voxel-based morphometry pipeline to obtain modulated gray matter volume (GMV) images as described in our previous work[5]. The SNPs were imputed and quality-controlled largely following the ENIGMA imputation protocol (http://enigma.usc.edu/). Cell-type specific polygenic risk scores for AD (PRSAD) were computed following Yang et al.[6]. In brief, we leveraged the cell-type marker genes identified for six major cell types (astrocytes (Ast), excitatory neurons (Ex), inhibitory neurons (IN), microglia (Mic), oligodendrocytes (Oli), and oligodendrocyte precursor cells (Opc)) from the postmortem prefrontal tissues by Mathys et al.[7]. For each cell type, its marker genes were utilized to mask the genome, and PRSice was then used to compute cell-type specific PRSAD targeting the masked genome based on the genomic associations of AD[8]. The first five principal components of the SNP data were included as covariates for population stratification.
The cell-type specific risk scores were examined for sex, diagnosis, and sex-diagnosis interaction effects in a regression model. Independent component analysis was conducted on the GMV images[9] to extract eight components, which were examined for significant diagnosis effects after removing age effects. Then for PRSAD showing sex-diagnosis interactions and AD-related GMV components, we further assessed in each diagnosis group, whether the GMV component showed associations with cell-type specific PRSAD, sex, as well as PRSAD-sex interaction in a regression model.
The cell-type specific risk scores were examined for sex, diagnosis, and sex-diagnosis interaction effects in a regression model. Independent component analysis was conducted on the GMV images[9] to extract eight components, which were examined for significant diagnosis effects after removing age effects. Then for PRSAD showing sex-diagnosis interactions and AD-related GMV components, we further assessed in each diagnosis group, whether the GMV component showed associations with cell-type specific PRSAD, sex, as well as PRSAD-sex interaction in a regression model.
Results:
PRSAD for Ex, Oli, and Opc cells showed significant sex-diagnosis interactions (Fig. 1a). Significantly higher PRSAD_Ex (p = 0.05) and PRSAD_Oli risk (p = 0.04) was noted in female than male ADRD, and higher PRSAD_Opc risk noted in female than male MCI (p = 0.002). Four out of eight GMV components showed significant diagnosis effects. Particularly, a significant sex-interacting association was noted between PRSAD-Ex and one AD-related GMV component in the ADRD group (p = 0.03). This GMV component indicated significantly lower GMV in ADRD than CN (p < 1E-16) in the cingulate gyrus, with MCI being intermediate (Fig. 1b and 1c). This regional GMV reduction was significantly negatively correlated with PRSAD_Ex in females (i.e., higher risk, lower GMV) while a positive trend was noted in males (Fig. 1d).
Conclusions:
Cingulate gyrus is well documented in AD[10]. Collectively, these results indicate substantial sex-specific genomic effects, as well as sex-interacting imaging-genomic factors in AD which can be better captured by taking into account cell-type specificity, motivating further dissection of cellular contributions to AD pathogenesis.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Genetics:
Genetic Association Studies
Genetics Other 2
Modeling and Analysis Methods:
Multivariate Approaches
Keywords:
MRI
Multivariate
Other - Alzheimer's, cell-specific, cingulate gyrus
1|2Indicates the priority used for review
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