Identifying candidate genes and drug targets for Alzheimer’s disease by an integrative network approach using genetic and brain region-specific proteomic data

Abstract

Genome-wide association studies (GWAS) have identified more than 75 genetic variants associated with Alzheimer’s disease (ad). However, how these variants function and impact protein expression in brain regions remains elusive. Large-scale proteomic datasets of ad postmortem brain tissues have become available recently. In this study, we used these datasets to investigate brain region-specific molecular pathways underlying ad pathogenesis and explore their potential drug targets. We applied our new network-based tool, Edge-Weighted Dense Module Search of GWAS (EW_dmGWAS), to integrate ad GWAS statistics of 472 868 individuals with proteomic profiles from two brain regions from two large-scale ad cohorts [parahippocampal gyrus (PHG), sample size n = 190; dorsolateral prefrontal cortex (DLPFC), n = 192]. The resulting network modules were evaluated using a scale-free network index, followed by a cross-region consistency evaluation. Our EW_dmGWAS analyses prioritized 52 top module genes (TMGs) specific in PHG and 58 TMGs in DLPFC, of which four genes (CLU, PICALM, PRRC2A, NDUFS3) overlapped. Those four genes were significantly associated with ad (GWAS gene-level false discovery rate < 0.05). To explore the impact of these genetic components on TMGs, we further examined their differentially co-expressed genes at the proteomic level and compared them with investigational drug targets. We pinpointed three potential drug target genes, APP, SNCA, and VCAM1, specifically in PHG. Gene set enrichment analyses of TMGs in PHG and DLPFC revealed region-specific biological processes, tissue-cell type signatures, and enriched drug signatures, suggesting potential region-specific drug repurposing targets for ad.

Unveiling Gene Interactions in Alzheimer’s Disease by Integrating Genetic and Epigenetic Data with a Network-Based Approach

“…EW_dmGWAS is an advanced iteration of the dmGWAS tool, which can integrate gene expression data by applying weighted edges of differential gene co-expression, offering a refined exploration of disease-related genetic and epigenetic interplays. Liu et al conducted an integrative proteomics and GWAS study to investigate the molecular pathways in brain-specific regions to recognize causal genes in AD pathogenies [ 71 ]. While the current study focused on the epigenetic mechanisms occurring in AD, succeeding studies integrating genetic, proteomic, and epigenomic data may provide a more holistic view of AD’s molecular landscape.…”

GRPa-PRS: A risk stratification method to identify genetically-regulated pathways in polygenic diseases

“…Among the common variants, the APOE4 allele is the strongest risk factor, with individuals with this variant showing higher odds for AD with one APOE4 allele (odds ratio [OR] 4.6 [95% confidence interval (CI) 4.1-5.2]) or two APOE4 alleles (OR ) compared with those without the APOE4 allele [17]. However, APOE alone does not fully explain the development of LOAD; as a result, ongoing research has been carried out to investigate additional genetic factors and pathways that play a role in both the development and resilience of AD [18][19][20].…”

“…For instance, individuals with high education attainment had a lower risk of AD and lower cognitive decline, even if they have a high PRS for AD [21]. Several theories [19,21] have been proposed to explain a few resilience metrics that were genetically correlated with these counterfactors and mitigate the effect of genetic risk on AD development. For example, Dumitrescu et al [19] conducted GWAS for validated metrics of cognitive resilience and identified a genome-wide significant locus near ATP8B1.…”

“…Several theories [19,21] have been proposed to explain a few resilience metrics that were genetically correlated with these counterfactors and mitigate the effect of genetic risk on AD development. For example, Dumitrescu et al [19] conducted GWAS for validated metrics of cognitive resilience and identified a genome-wide significant locus near ATP8B1. These counterfactors, in the case of genetic factors, are orthogonal factors, and thus unrelated, to the PRS calculation and not merely the opposite of risk [22,23].…”

Applications of Deep Learning for Drug Discovery Systems with BigData

“…The main reasons for this difficulty are the selection of low-accuracy target genes based mainly on animal studies and the start of the drug discovery process with insufficient stratification of patients to be treated. Therefore, efforts are being made in target gene searches to promote new drug discovery targets based on human or other data [14][15][16]. It is expected that the use of human data such, as omics data and clinical information, can reduce failures due to reliance on animal testing [17,18].…”