Integrative analysis of single-nucleus RNA sequencing and Mendelian randomization to explore novel risk genes for Alzheimer's disease.

Abstract

In this study, we aimed to delineate cellular heterogeneity in Alzheimer's disease (AD) and identify genetic markers contributing to its pathogenesis using integrative analysis of single-nucleus RNA sequencing (sn-RNA-Seq) and Mendelian randomization (MR). The dorsolateral prefrontal cortex sn-RNA-Seq dataset (GSE243292) was sourced from the Gene Expression Omnibus (GEO) database. Data preprocessing was conducted using the Seurat R software package, employing principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) for cell clustering and annotation. MR analysis was used to identify instrumental variables from expression quantitative trait loci (eQTL) and GWAS data by applying inverse variance weighting (IVW), weighted median (WM) and MR-Egger methods. This was complemented by leave-one-out sensitivity analysis to validate the causal relationship on AD risk genes. We identified 23 distinct cell clusters, which were annotated into eight subgroups, including oligodendrocytes, oligodendrocyte precursors, astrocytes, macrophage cells, endothelial cells, glutamatergic neurons, neural stem cells, and neurons. Notably, the number of macrophages significantly increased in the AD group. Using genome-wide association study (GWAS) summaries and eQTL data, MR analysis identified causal relationships for 7 genes with significant impacts on AD risk. Among these genes, CACNA2D3, INPP5D, RBM47, and TBXAS1 were associated with a decreased risk of AD, whereas EPB41L2, MYO1F, and SSH2 were associated with an increased risk. A leave-one-out sensitivity analysis confirmed the robustness of these findings. Expression analysis revealed that these genes were variably expressed across different cell subgroups. Except for the CACNA2D3 gene, the other 6 genes showed increased expression levels in the macrophages, particularly EPB41L2 and SSH2. Our findings highlight the potential of specific genetic markers identified through integrative analysis of sn-RNA-Seq and MR in guiding the diagnosis and therapeutic strategies for Alzheimer's disease.