Researchers at the University of Chicago and Columbia University have developed a new computational tool named Dandelion that identifies hidden genetic drivers of asthma. Traditional methods like genome-wide association studies often focus on genes located near genetic variants, which frequently provide an incomplete picture of disease biology. Dandelion operates differently by mapping complex gene regulatory networks to find central drivers that exist further downstream of these variants.
In a recent study published in Cell, the team used this tool to analyze health data from the UK Biobank. The process revealed 21 genes associated with asthma, 19 of which were previously unknown. By utilizing CRISPR gene editing and mouse models, researchers confirmed that several of these genes play a direct role in asthma phenotypes. This discovery shifts the perspective from simple proximity to understanding the deeper regulatory cascades that trigger chronic illness.
Experimental validation highlighted two specific genes, SLC27A3 and SCD, which are involved in fatty acid metabolism and protein palmitoylation. Researchers discovered that knocking out SLC27A3 provided protection against asthma effects in both epithelial and T cells. This finding points toward protein palmitoylation as a potential target for future drug development in respiratory care.
The research team aims to apply the Dandelion method to other complex conditions such as inflammatory bowel disease and type 2 diabetes. By identifying these central protein targets, the scientific community may find more effective ways to treat chronic diseases that have historically been difficult to manage. This computational approach marks a significant shift in how geneticists map the root causes of disease beyond traditional GWAS limitations.