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The combined effects of thousands of genetic polymorphisms account for Alzheimer's disease (AD) genetic risk. Most AD polymorphisms affect gene expression. Thus, the transcriptome, the set of all gene expression levels for every gene in the genome, is a major mediator between the genotype to phenotype. This study uses genotypes, transcriptomes, and clinical phenotypes to identify the transcriptomic signature that mediates the causal effect of genotype on AD. By utilizing a causal inference method known as high dimensional mediation analysis (HDMA) on the Religious Orders Study/Memory and Aging Project (ROSMAP) longitudinal cohort, the genotype, transcriptome, and phenotype data were reduced to single scores encoding genotype, transcriptome, and phenotype correlations, and produce a ranked gene list based on putative causal importance of each gene for AD. Analysis of the up- and down-regulated genes prevalent in AD through Gene Ontology (GO) and KEGG databases reveals findings such as up-regulated functions which include angiogenesis and immune responses while down-regulated functions of genes include synaptic activity. Furthermore, utilizing Clue.io to identify candidate drugs to suppress AD-pathology reveals a plausible list of therapeutic candidates, including targeted genes and compounds such asSMAD3, TM7SF2, andABCB1, which counteract the transcriptomic signature identified and may block the devastating effects of AD related to inflammatory responses, Aβ-induced toxicity, and neuronal death. 

Abstract We present a new clustering-enabled regression approach to investigate how functional connectivity (FC) of the entire brain changes from childhood to old age. By applying this method to resting-state functional magnetic resonance imaging data aggregated from three Human Connectome Project studies, we cluster brain regions that undergo identical age-related changes in FC and reveal diverse patterns of these changes for different region clusters. While most brain connections between pairs of regions show minimal yet statistically significant FC changes with age, only a tiny proportion of connections exhibit practically significant age-related changes in FC. Among these connections, FC between region clusters from the same functional network tends to decrease over time, whereas FC between region clusters from different networks demonstrates various patterns of age-related changes. Moreover, our research uncovers sex-specific trends in FC changes. Females show much higher FC mainly within the default mode network, whereas males display higher FC across several more brain networks. These findings underscore the complexity and heterogeneity of FC changes in the brain throughout the lifespan.