Complex disease such as Alzheimer's disease and aging-related cognitive decline are caused by a combination of genetic, environmental, and lifestyle factors. Key molecular pathways have already been identified that are associated with both genetic and environmental contributions. Causal mechanisms, however, have eluded numerous attempts at fine mapping despite intensive research. In this work, we build on the hypothesis that these causal mechanisms can be unraveled as combinatorial regulation programs involving genetic and epigenetic perturbations driving these key molecular pathways. Identifying the different causal mechanisms at play in different individuals and prioritizing these causal regulators and their downstream targets shared among individuals will facilitate the design of increasingly specific therapeutic strategies. Here, we will develop a systematic method for prioritizing targets for drug discovery and development and simultaneously understanding the impact of perturbing these targets in Alzheimer's disease and aging-related cognitive decline. We will use the power of innovative computational strategies to integrate multiple layers of "omic" (genomic, epigenomic, transcriptomic) data that we have collected from the prospectively collected brains of 550 older subjects, thus developing a powerful toolbox for understanding the molecular and mechanistic basis of complex human disease. Inspired by the Module Networks algorithm that allows for inferring modules of co-expressed genes and their associated shared regulators from gene expression data, we will build on this algorithm by (i) identifying candidate key regulators and their associated genetic and epigenetic variants, (ii) identifying key molecular pathways associated with Alzheimer's disease and aging-related cognitive decline, (iii) inferring combinatorial regulation mechanisms by associating key regulators with key molecular pathways and prioritizing key regulators and downstream targets as targets for drug discovery and development.