Researchers at the University of Chicago and Columbia University have created a new computational tool called DANDELION to solve a long-standing challenge in genetics. Most diseases arise from complex interactions between dozens or even hundreds of genes rather than a single mutation. Current methods often struggle to filter through this noise to identify the central genes that actually drive disease progression.
DANDELION shifts the focus from simple gene association to the broader regulatory networks that control cellular function. By analyzing data from the human transcriptome and the UK Biobank, the researchers successfully pinpointed 21 candidate genes associated with asthma. Most of these had not been identified through previous standard research methods.
The team validated their findings through CRISPR gene-editing screens and mouse models. They discovered that two specific genes, SLC27A3 and SCD, play a significant role in fatty acid metabolism and protein palmitoylation within lung cells. When researchers inactivated the SLC27A3 gene, the mice showed protection against allergy-induced lung inflammation. Conversely, disabling the SCD gene increased susceptibility to inflammation.
This work demonstrates how interdisciplinary collaboration can bridge the gap between computational data and experimental biology. By identifying specific, druggable targets within these complex genetic networks, the team hopes to provide new pathways for treating inflammatory conditions. The researchers plan to apply this same model to other complex diseases, such as Type 2 diabetes and inflammatory bowel disease, to uncover similar hidden genetic drivers.