Xavier Bledsoe; Ting-Chen Wang; Yiyang Wu; Derek Archer; Hung Hsin Chen; Adam C. Naj; William S. Bush; Timothy J. Hohman; Logan Dumitrescu; Jennifer E. Below; Eric R. Gamazon (2026).听.听Alzheimer鈥檚 & Dementia, 22(2), e71217.听
This study looked at how genetic differences linked to Alzheimer鈥檚 disease (AD) may influence the brain, aiming to better understand how these genes actually lead to changes seen in patients. While previous research has identified many AD-related genes, it is still unclear how these genes affect brain structure and function. To explore this, the researchers used a functional genomics approach, meaning they examined how genetic variants influence gene activity (gene expression) and, in turn, brain features seen on imaging scans. They connected known AD genes to specific brain characteristics using a tool called the NeuroimaGene Atlas, and compared these predicted effects with real-world brain imaging data from patients. They also analyzed genetic covariance, which looks at how different traits share common genetic influences, to identify links between brain features and risk factors like family history of dementia.
The results suggest that a gene called PSMC3, which plays a role in breaking down unwanted or damaged proteins, may be important in the development of Alzheimer鈥檚 disease. Changes in AD-related genes were linked to differences in key brain areas involved in memory and thinking, such as the frontal cortex (important for decision-making and cognition), as well as changes in cerebrospinal fluid (the fluid surrounding the brain and spinal cord). The study also found shared genetic influences between Alzheimer鈥檚 risk and features of the hippocampus, a brain region critical for memory. Interestingly, higher activity of the PSMC3 gene was associated with better cognitive performance and lower levels of amyloid beta, a protein that builds up abnormally in Alzheimer鈥檚 disease. Overall, these findings help connect genetic risk factors to specific brain changes, offering a clearer picture of how Alzheimer鈥檚 disease develops and pointing to potential targets for future research and treatment.
FIGURE 1
Schematic overview of the analytical framework. A, Grid summarizing primary data resources integrated in the study. B, Directed acyclic graph illustrating TWAS analyses and downstream imputation of neuroimaging features via NeuroimaGene. C, Visualization of genetic covariance analyses comparing the genetic architecture of clinical AD and parental AD with neuroimaging-derived features. D, Logistic regression models evaluating associations between聽neuroimaging features and parental AD status. E, Integration of clinical neuroimaging data linking brain features to AD status. F, Composite synthesis comparing the neuroimaging features obtained across transcriptomic, genetic covariance, parental history, and clinical approaches. AD, Alzheimer’s disease; Dx, diagnosis; TWAS, transcriptome-wide association study; UKBB, UK Biobank.