Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer's disease.

ABSTRACT: Despite decades of genetic studies on late-onset Alzheimer's disease, the underlying molecular mechanisms remain unclear. To better comprehend its complex etiology, we use an integrative approach to build robust predictive (causal) network models using two large human multi-omics datasets. We delineate bulk-tissue gene expression into single cell-type gene expression and integrate clinical and pathologic traits, single nucleotide variation, and deconvoluted gene expression for the construction of cell type-specific predictive network models. Here, we focus on neuron-specific network models and prioritize 19 predicted key drivers modulating Alzheimer's pathology, which we then validate by knockdown in human induced pluripotent stem cell-derived neurons. We find that neuronal knockdown of 10

SUBMITTER: Merchant JP 

PROVIDER: S-EPMC10185548 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature