A new tool could pave the way for new multi-target therapies, personalized medicine. This form of AI is needed because complex illnesses are not caused by one gene but groups of genes and a number of gene combinations is too enormous to analyse comprehensively. Furthermore, the new AI model focuses on gene expression changes, rather than mutations. This is to help to identify the key genes and their collective impact on disease.
The research comes from Northwestern University biophysicists who 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. Yet, the sheer number of possible gene combinations is huge, making it y difficult for researchers to pinpoint the specific ones that cause disease.
Using a generative 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 in a research note. “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.”
Current methods fall short
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.
“The Human Genome Project showed us that we only have six times as many genes as a single-cell bacterium,” Motter adds. “But humans are much more sophisticated than bacteria, and the number of genes alone does not explain that. This highlights the prevalence of multigenic relationships, and that it must be the interactions among genes that give rise to complex life.”
Not genes but gene expression
To help bridge the long-standing knowledge gap between genetic makeup (genotype) and observable traits (phenotype), the research team developed a sophisticated approach that combines machine learning with optimization.
Called the Transcriptome-Wide conditional Variational auto-Encoder (TWAVE), the model leverages generative AI to identify patterns from limited gene expression data in humans. Accordingly, this technology can emulate diseased and healthy states so that changes in gene expression can be matched with changes in phenotype.
Instead of examining the effects of individual genes in isolation, the model identifies groups of genes that collectively cause a complex trait to emerge. The method then uses an optimization framework to pinpoint specific gene changes that are most likely to shift a cell’s state from healthy to diseased or vice versa.
Focusing on gene expression has multiple benefits. First, it bypasses patient privacy issues. Genetic data — a person’s actual DNA sequence — is inherently unique to an individual, providing a highly personal blueprint of health, genetic predispositions and family relationships. Expression data, on the other hand, is more like a dynamic snapshot of cellular activity. Second, gene expression data implicitly accounts for environmental factors, which can turn genes “up” or “down” to perform various functions.
A path to personalized treatment
To demonstrate TWAVE’s effectiveness, the scientists tested it across several complex diseases. The method successfully identified the genes — some of which were missed by existing methods — that caused those diseases. TWAVE also revealed that different sets of genes can cause the same complex disease in different people. That finding suggests personalized treatments could be tailored to a patient’s specific genetic drivers of disease.
The has been published in Proceedings of the National Academy of Sciences. The study is titled “Generative prediction of causal gene sets responsible for complex traits.”
