An artificial intelligence (AI) algorithm has been developed to identify genes associated with autoimmune diseases, offering the potential for earlier intervention and the development of novel therapeutics. Researchers at Penn State College of Medicine trained the algorithm to predict autoimmune disease risk by analyzing genetic data.
EXPRESSO: Predicting Autoimmune Disease Risk
Autoimmune diseases arise from genetic mutations, but identifying which mutations predispose individuals to these conditions has been challenging. The newly developed AI method, called EXPRESSO (EXpression PREdiction with Summary Statistics Only), analyzes data from single-cell expression quantitative trait loci, linking genetic variants to the genes they regulate. It also integrates 3D genomic data and epigenetics. Dajiang Liu, co-senior author of the study, noted, "We all carry some DNA mutations, and we need to figure out how any one of these mutations may influence gene expression linked to disease so we can predict disease risk early. This is especially important for autoimmune disease."
Identifying Risk Genes and Therapeutic Compounds
The researchers applied EXPRESSO to genome-wide association studies' datasets for 14 different autoimmune diseases to identify additional risk genes. Bibo Jiang, co-senior author, reported, "With this new method, we were able to identify many more risk genes for autoimmune disease that actually have cell-type specific effects, meaning that they only have effects in a particular cell type and not others." The team further identified potential therapeutic compounds, including some already FDA-approved, that could reverse gene expression in autoimmune disease-associated cell types, addressing the need for treatments with fewer side effects.
Future Directions
The team plans to validate their findings and EXPRESSO in the lab and, eventually, in clinical trials. This validation is crucial to confirm the algorithm's predictive capabilities and the therapeutic potential of the identified compounds.
