A research team led by Min Zhang and Dabao Zhang at the University of California, Irvine’s Joe C. Wen School of Population & Public Health has created the most detailed maps to date of how genes directly influence one another in brain cells affected by Alzheimer’s disease. Their work goes beyond identifying gene associations to reveal which genes are actively controlling others across different brain cell types. This breakthrough was made possible by a new machine learning platform called SIGNET, designed to uncover true cause-and-effect relationships in gene activity—something traditional tools, which only detect correlations, cannot do reliably. The study, published in Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, identifies key biological pathways linked to memory loss and brain tissue degeneration. It also highlights previously unrecognized genes that could serve as promising targets for future treatments. Funding was provided by the National Institute on Aging and the National Cancer Institute. Alzheimer’s disease is the leading cause of dementia and is projected to affect nearly 14 million Americans by 2060. While genes like APOE and APP have long been associated with the disease, the precise mechanisms by which they disrupt normal brain function remain unclear. Min Zhang, co-corresponding author and professor of epidemiology and biostatistics, emphasized the importance of understanding how different brain cell types interact at the molecular level. “Our work provides cell type-specific maps of gene regulation in the Alzheimer’s brain, shifting the field from observing correlations to uncovering the causal mechanisms that actively drive disease progression,” she said. To build these maps, the team analyzed single-cell molecular data from brain tissue donated by 272 participants in two long-term aging studies: the Religious Orders Study and the Rush Memory and Aging Project. SIGNET integrates single-cell RNA sequencing with whole-genome sequencing data, enabling it to detect causal gene interactions across the entire genome. The platform was specifically designed to avoid common pitfalls of other methods, such as ignoring feedback loops between genes or making unrealistic assumptions about gene behavior. Using SIGNET, the researchers constructed causal gene regulatory networks for six major brain cell types. They discovered that excitatory neurons—the brain cells responsible for sending activating signals—experience the most extensive genetic rewiring in Alzheimer’s, with nearly 6,000 cause-and-effect interactions identified. The study also uncovered hundreds of “hub genes” that act as central regulators, influencing numerous downstream genes and likely playing a key role in disease progression. Notably, the well-known APP gene was found to exert strong regulatory control over other genes in inhibitory neurons, revealing a new layer of its involvement in Alzheimer’s. To validate their findings, the team tested their results on an independent set of human brain samples, confirming the biological relevance of the identified gene relationships. This strengthens the confidence that these networks reflect real mechanisms driving the disease. Beyond Alzheimer’s, the SIGNET platform holds promise for studying other complex conditions, including cancer, autoimmune disorders, and mental health diseases, where understanding gene regulation is critical. By revealing the hidden control centers within the genome, this research opens new doors for earlier diagnosis and more effective, targeted therapies.