Northwestern University biophysicists have developed a new computational tool for identifying the gene combinations underlying complex illnesses like diabetes, cancer and asthma. Unlike single-gene disorders, these conditions are influenced by a network of multiple genes working together. But the sheer number of possible gene combinations is huge, making it incredibly difficult for researchers to pinpoint the specific ones that cause disease.

Using a generative artificial intelligence (AI) model, the new method amplifies limited gene expression data, enabling researchers to resolve patterns of gene activity that cause complex traits. This information could lead to new and more effective disease treatments involving molecular targets associated with multiple genes. Many diseases are determined by a combination of genes not just one, said Northwestern’s Adilson Motter, the study’s senior author.

You can compare a disease like cancer to an airplane crash. In most cases, multiple failures need to occur for a plane to crash, and different combinations of failures can lead to similar outcomes. This complicates the task of pinpointing the causes. Our model helps simplify things by identifying the key players and their collective influence. For decades, researchers have struggled to unravel the genetic underpinnings of complex human traits and diseases.

Even non-disease traits like height, intelligence and hair color depend on collections of genes. Existing methods, such as genome-wide association studies, try to find individual genes linked to a trait. But they lack the statistical power to detect the collective effects of groups of genes.

Source: http://news.northwestern.edu/stories/2025/06/ai-identifies-key-gene-sets-that-cause-complex-disease/