Individual assessment of gene expression cannot alone explain the complex etiology of AD; thus, an integrative approach to assessing gene expression in a network framework is necessary to unravel the molecular underpinnings of AD. Weighted gene co-expression network analysis (WGCNA) is a tool that has been used to successfully build and identify gene networks involved in various disorders including schizophrenia, major depression, AD, and ARPs [29, 30]. Integrating dysregulated gene networks in AD, identified by WGCNA, with genetic data, identified by GWAS, provides an invaluable tool to further discern the genetic basis of AD susceptibility. This approach, termed ‘genetical genomics’, classifies associations between genetic variants and gene expression as quantitative trait loci (eQTLs), which are then modeled as quantitative traits [31–33]. As the majority of genetic variants are located outside of protein-coding regions, their influence on cell function likely involves subtle modification of gene transcription and translation [34]. The connection between genetic variation and gene expression may identify functional loci not previously associated with AD, as well as offer specific, testable hypotheses for polymorphisms associated with AD [34–36]. Recent studies
