A team led by Min Zhang and Dabao Zhang at the University of California, Irvine’s Joe C. Wen School of Population & Public Health has developed the most comprehensive maps yet of how genes directly influence one another in brain cells affected by Alzheimer’s disease. These maps go beyond identifying gene links. They reveal which genes are actively controlling others across different cell types in the brain.
To accomplish this, the researchers created a machine learning platform called SIGNET. Unlike traditional tools that only detect genes that appear to move together, SIGNET is designed to uncover true cause-and-effect relationships. Using this approach, the team identified important biological pathways that may contribute to memory loss and the gradual breakdown of brain tissue.
The findings were published in Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association. The study also highlights newly identified genes that could become promising targets for future treatments. Funding support came in part from the National Institute on Aging and the National Cancer Institute.
Why Understanding Gene Control Matters in Alzheimer’s
Alzheimer’s disease is the leading cause of dementia and is expected to affect nearly 14 million Americans by 2060. Although scientists have linked several genes to the disease, including APOE and APP, they still do not fully understand how these genes interfere with normal brain function.
“Different types of brain cells play distinct roles in Alzheimer’s disease, but how they interact at the molecular level has remained unclear,” said Min Zhang, co-corresponding author and professor of epidemiology and biostatistics. “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.”
How SIGNET Reveals Cause and Effect Between Genes
To build these detailed maps, the team analyzed single-cell molecular data from brain samples donated by 272 participants enrolled in long-term aging studies known as the Religious Orders Study and the Rush Memory and Aging Project. SIGNET was designed as a scalable, high-performance computing system that combines single-cell RNA sequencing with whole-genome sequencing data. This integration allowed the researchers to detect cause-and-effect relationships among genes across the entire genome.
Using this method, they constructed causal gene regulatory networks for six major brain cell types. This made it possible to determine which genes are likely directing the activity of others, something conventional correlation-based methods cannot reliably accomplish.
“Most gene-mapping tools can show which genes move together, but they can’t tell which genes are actually driving the changes,” said Dabao Zhang, co-corresponding author and professor of epidemiology and biostatistics. “Some methods also make unrealistic assumptions, such as ignoring feedback loops between genes. Our approach takes advantage of information encoded in DNA to enable the identification of true cause-and-effect relationships between genes in the brain.”
Major Genetic Rewiring in Excitatory Neurons
The researchers found that the most significant gene disruptions occur in excitatory neurons — the nerve cells that send activating signals — where nearly 6,000 cause-and-effect interactions revealed extensive genetic rewiring as Alzheimer’s progresses.
The team also identified hundreds of “hub genes” that function as central regulators, influencing many other genes and likely playing an important role in harmful changes in the brain. These hub genes could become valuable targets for earlier diagnosis and future therapies. The study further uncovered new regulatory roles for well-known genes such as APP, which was shown to strongly control other genes in inhibitory neurons.
To strengthen their conclusions, the researchers validated their findings using an independent set of human brain samples. This additional confirmation increases confidence that the observed gene relationships reflect genuine biological mechanisms involved in Alzheimer’s disease.
Beyond Alzheimer’s, SIGNET may also be applied to the study of other complex diseases, including cancer, autoimmune disorders and mental health conditions.
