Liu Shiwei, Cho Minyoung, Huang Yen-Ning, Park Tamina, Chaudhuri Soumilee, Rosewood Thea J, Bice Paula J, Chung Dongjun, Bennett David A, Ertekin-Taner Nilüfer, Saykin Andrew J, Nho Kwangsik
Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 550 N. University Blvd., Indianapolis, IN, 46202, USA.
Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, 355 W. 16th Street, Goodman Hall, Suite 4100, Indianapolis, IN, 46202, USA.
J Transl Med. 2025 Jul 13;23(1):788. doi: 10.1186/s12967-025-06739-1.
Analyzing disease-linked genetic variants via expression quantitative trait loci (eQTLs) helps identify 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 a systematic analysis across multiple Alzheimer's disease (AD) genome-wide association study (GWAS) datasets or comparisons between tissue-level and cell type-specific effects remain limited. Here, we integrated brain cell type-level and bulk-level eQTL datasets with AD GWAS datasets to identify potential causal genes.
We used Summary Data-Based Mendelian Randomization (SMR) and Bayesian Colocalization (COLOC) to integrate AD GWAS summary statistics with eQTLs datasets. Combining data from five AD GWAS, two single-cell eQTL datasets, and one bulk eQTL dataset, we identified novel candidate causal genes and further confirmed known ones. We investigated gene regulation through enhancer activity using H3K27ac and ATAC-seq data, performed protein-protein interaction (PPI) and pathway enrichment, and conducted a drug/compound enrichment analysis with Drug Signatures Database (DSigDB) to support drug repurposing for AD.
We identified 28 candidate causal genes for AD, of which 12 were uniquely detected at the cell-type level, 9 were exclusive to the bulk level and 7 detected in both. Among the 19 cell-type level candidate causal genes, microglia contributed the highest number of candidate genes, followed by excitatory neurons, astrocytes, inhibitory neurons, oligodendrocytes, and oligodendrocyte precursor cells (OPCs). PABPC1 emerged as a novel candidate causal gene in astrocytes. We generated PPI networks for the candidate causal genes and found that pathways such as membrane organization, cell migration, and ERK1/2 and PI3K/AKT signaling were enriched. The AD-risk variant associated with candidate causal gene PABPC1 is located near or within enhancers only active in astrocytes. We classified the 28 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.
We systematically prioritized AD candidate causal genes based on cell type-level and bulk level molecular evidence. The integrative approach enhances our understanding of molecular mechanisms of AD-related genetic variants and facilitates interpretation of AD GWAS results.
通过表达数量性状基因座（eQTL）分析与疾病相关的遗传变异有助于识别潜在的致病基因。先前的研究通过整合全基因组关联研究（GWAS）结果与组织水平的eQTL对基因进行优先级排序。最近的研究探索了脑细胞类型特异性eQTL，但在多个阿尔茨海默病（AD）全基因组关联研究（GWAS）数据集上进行的系统分析或组织水平与细胞类型特异性效应之间的比较仍然有限。在这里，我们将脑细胞类型水平和整体水平的eQTL数据集与AD GWAS数据集整合，以识别潜在的因果基因。
我们使用基于汇总数据的孟德尔随机化（SMR）和贝叶斯共定位（COLOC）将AD GWAS汇总统计数据与eQTL数据集整合。结合来自五个AD GWAS、两个单细胞eQTL数据集和一个整体eQTL数据集的数据，我们确定了新的候选因果基因，并进一步证实了已知的基因。我们使用H3K27ac和ATAC-seq数据通过增强子活性研究基因调控，进行蛋白质-蛋白质相互作用（PPI）和通路富集，并使用药物特征数据库（DSigDB）进行药物/化合物富集分析，以支持AD的药物再利用。
我们确定了28个AD候选因果基因，其中12个在细胞类型水平上被独特检测到，9个仅在整体水平上被检测到，7个在两者中都被检测到。在19个细胞类型水平的候选因果基因中，小胶质细胞贡献的候选基因数量最多，其次是兴奋性神经元、星形胶质细胞、抑制性神经元、少突胶质细胞和少突胶质细胞前体细胞（OPC）。PABPC1成为星形胶质细胞中的一个新的候选因果基因。我们为候选因果基因生成了PPI网络，发现膜组织、细胞迁移以及ERK1/2和PI3K/AKT信号传导等通路得到了富集。与候选因果基因PABPC1相关的AD风险变异位于仅在星形胶质细胞中活跃的增强子附近或内部。我们将这28个基因分为三个药物层级，并确定了可成药的相互作用，甲磺酸伊马替尼成为关键候选药物。创建了一个药物-靶基因网络以探索AD的潜在药物靶点。
我们基于细胞类型水平和整体水平的分子证据系统地对AD候选因果基因进行了优先级排序。这种综合方法增强了我们对AD相关遗传变异分子机制的理解，并有助于解释AD GWAS结果。
