Browsing by Author "Jacobson Rosewood, Thea"

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    Integration of GWAS summary statistics with cell type‐specific eQTLs prioritizes potential causal genes for Alzheimer’s disease
    (Wiley, 2025-01-09) Liu, Shiwei; Huang, Yen-Ning; Park, Tamina; Chaudhuri, Soumilee; Cho, Min Young; Jacobson Rosewood, Thea; Bennett, David A.; Saykin, Andrew J.; Nho, Kwangsik; Radiology and Imaging Sciences, School of Medicine
    Background: Analyzing disease‐linked genetic variants via expression quantitative trait loci (eQTLs) is crucial for identifying disease‐causing genes. Previous research prioritized genes by integrating Genome‐Wide Association Study (GWAS) results with tissue‐level eQTLs. Recent studies explored brain cell type‐specific eQTLs, but they lack a systematic analysis across various AD GWAS datasets, nor did they compare effects between tissue and cell type levels or across different cell type‐specific eQTL datasets. Here, we integrated brain cell type‐specific eQTL datasets with AD GWAS datasets to identify potential causal genes at the cell type level. Method: To prioritize disease‐causing genes, we used summary data‐based Mendelian Randomization (SMR) and Bayesian colocalization (COLOC) methods to integrate the AD GWAS summary statistics with cell type‐specific eQTLs in human brain. We utilized five latest AD GWAS datasets and a cell type‐specific eQTL dataset comprising 424 participants of the Religious Orders Study (ROS) and Rush Memory and Aging Project (MAP) cohort. We replicated our analysis using a cell type‐specific eQTL dataset of 192 participants from Bryois et al., 2021. For comparison, we utilized a previous tissue‐level metabrain eQTL dataset from a meta‐analysis of 14 datasets. Furthermore, we visualized the colocalization of novel candidate causal genes using eQTpLot. Result: We identified 17 cell type‐specific candidate causal genes using the ROSMAP eQTL dataset. Our results showed that the largest number of candidate causal genes are identified in microglia, followed by astrocytes, oligodendrocytes, excitatory neurons, inhibitory neurons, and oligodendrocyte progenitor cells (OPCs). Four candidate causal genes were common across different cell types. Interestingly, JAZF1, detected as a candidate causal gene affected by the same leading variant in both microglia and OPCs, showed a congruous (same direction) colocalized SNP effect on the gene expression level and AD in OPCs, but an incongruous (opposite direction) colocalized SNP effect in microglia. After comparing our results with previously known prioritized causal genes, we identified PABPC1 in astrocyte as a novel potential causal gene. Conclusion: We systematically prioritized AD candidate causal genes based on cell type‐specific molecular evidence. The integrative approach enhances our understanding of molecular mechanisms of AD‐related genetic variants and facilitates the interpretation of AD GWAS results.
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    Multi-Omics Analysis for Identifying Cell-Type-Specific Druggable Targets in Alzheimer’s Disease
    (medRxiv, 2025-01-09) Liu, Shiwei; Cho, Min Young; Huang, Yen-Ning; Park, Tamina; Chaudhuri, Soumilee; Jacobson Rosewood, Thea; Bice, Paula J.; Chung, Dongjun; Bennett, David A.; Ertekin-Taner, Nilüfer; Saykin, Andrew J.; Nho, Kwangsik; Radiology and Imaging Sciences, School of Medicine
    Background: Analyzing disease-linked genetic variants via expression quantitative trait loci (eQTLs) is important for identifying potential disease-causing genes. Previous research prioritized genes by integrating Genome-Wide Association Study (GWAS) results with tissue-level eQTLs. Recent studies have explored brain cell type-specific eQTLs, but they lack a systematic analysis across various Alzheimer's disease (AD) GWAS datasets, nor did they compare effects between tissue and cell type levels or across different cell type-specific eQTL datasets. In this study, we integrated brain cell type-specific eQTL datasets with AD GWAS datasets to identify potential causal genes at the cell type level. Methods: To prioritize disease-causing genes, we used Summary Data-Based Mendelian Randomization (SMR) and Bayesian Colocalization (COLOC) to integrate AD GWAS summary statistics with cell-type-specific eQTLs. Combining data from five AD GWAS, three single-cell eQTL datasets, and one bulk tissue eQTL meta-analysis, we identified and confirmed both novel and known candidate causal genes. We investigated gene regulation through enhancer activity using H3K27ac and ATAC-seq data, performed protein-protein interaction and pathway enrichment analyses, and conducted a drug/compound enrichment analysis with the Drug Signatures Database (DSigDB) to support drug repurposing for AD. Results: We identified 27 candidate causal genes for AD using cell type-specific eQTL datasets, with the highest numbers in microglia, followed by excitatory neurons, astrocytes, inhibitory neurons, oligodendrocytes, and oligodendrocyte precursor cells (OPCs). PABPC1 emerged as a novel astrocyte-specific gene. Our analysis revealed protein-protein interaction (PPI) networks for these causal genes in microglia and astrocytes. We found the "regulation of aspartic-type peptidase activity" pathway being the most enriched among all the causal genes. AD-risk variants associated with candidate causal gene PABPC1 is located near or within enhancers only active in astrocytes. We classified the genes into three drug tiers and identified druggable interactions, with imatinib mesylate emerging as a key candidate. A drug-target gene network was created to explore potential drug targets for AD. Conclusions: We systematically prioritized AD candidate causal genes based on cell type-specific molecular evidence. The integrative approach enhances our understanding of molecular mechanisms of AD-related genetic variants and facilitates the interpretation of AD GWAS results.
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    Pathway Enrichment of Longitudinal AD Endophenotypes Identifies Potential Therapeutic Targets for Modifying Disease Trajectory
    (Wiley, 2025-01-09) Jacobson Rosewood, Thea; Nho, Kwangsik; Risacher, Shannon L.; Liu, Shiwei; Gao, Sujuan; Saykin, Andrew J.; Radiology and Imaging Sciences, School of Medicine
    Background: Alzheimer’s disease (AD) is characterized by longitudinal changes of biomarker endophenotypes over the course of the disease prodrome, onset, and progression. The genetic pathways that influence these heterogenous changes in longitudinal endophenotype trajectories may provide insight into disease mechanisms and represent potential therapeutic targets. Methods: Longitudinal endophenotypes from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were selected: amyloid‐β (Amyloid PET and CSF), total tau and phosphorylated tau (CSF), glucose metabolism (FDG PET), neurodegeneration (atrophy on MRI), and cognition (composite scores for memory and executive functioning). Genome‐wide association analysis for the selected longitudinal endophenotypes was performed using Linear Mixed Modelling (LMM; LME4 R package), with (Time x Subject) as a random effect and age as the time variable. Gene‐based association analysis was performed using MAGMA on SNP P values from the LMM. The SNP to gene assignment was performed in two steps to select SNPs with a functional relation to each target gene: SNPs within gene transcription start and end positions, and SNPs that have significant eQTLs in brain tissue from the MetaBrain eQTL project. Gene‐based analysis results were then processed for gene‐set enrichment with MAGMA and the C2 curated gene set collection from the Gene Set Enrichment Analysis (GSEA) Molecular Signatures Database (MSigDB). Results: Pathway enrichment analysis identified 19 pathways (Figure 1) as significantly associated with longitudinal trajectories of AD endophenotypes. These pathways fall into six groups, with each pathway group having stronger association with different types of endophenotypes. Immune and cytoskeletal pathways largely associated with changes in amyloid trajectory. Metabolic pathways associated strongly with changes in amyloid and tau trajectories. Glycosylation pathways were associated with changes in brain atrophy. Pathways related to cell and neuronal signaling associated with changes in cognition, tau, and amyloid trajectories. Cell growth and survival was associated with changes in neurodegeneration trajectory (structural atrophy and hypometabolism). Conclusions: Pathway enrichment analysis of genetic variation associated with longitudinal changes of AD endophenotypes identified pathways that uniquely associate with trajectories of key AD biomarkers and cognition. These pathways may provide insight into AD pathological mechanisms and constitute new potential therapeutic targets to modify disease trajectory.