Genetic interactions linked to heart disease are revealed

Researchers used machine learning and lab experiments to identify interacting gene pairs — including TTN, CCDC141 and IGF1R — that are associated with cardiac hypertrophy, and they tested effects in lab-grown heart cells.

Researchers combined computational analysis and laboratory tests to investigate how interactions between genes influence heart disease. Epistasis — when two or more genes interact — can change whether and how a condition appears. The team led by Euan Ashley and collaborators analyzed large genetic and imaging datasets to search for these hidden partnerships. Their work focuses on better understanding inherited contributors to cardiac hypertrophy. Key findings: - The researchers analyzed genetic data from more than 300 human hearts and about 30,000 cardiac MRI images from the UK Biobank. - They applied machine learning to filter roughly 15 million genetic variants down to about 1,400 candidates and to rank potential gene-gene interactions. - Interactions involving the genes TTN, CCDC141 and IGF1R were identified as linked to heart muscle thickening (cardiac hypertrophy). - Lab experiments using small RNA to reduce pairs of genes in lab-grown heart muscle cells showed that turning off gene pairs reduced cell enlargement, a hallmark of hypertrophic cardiomyopathy. - The approach combined computational epistasis detection with high-throughput cell imaging using microfluidic channels and rapid photography. Summary: The findings indicate that epistatic gene interactions can contribute to cardiac hypertrophy and that combined AI and cellular experiments can reveal such relationships. The research team plans to refine their detection algorithms, identify more interactions, and investigate applications to other complex diseases such as cancer.