Genetic Mutations Associated With Spina Bifida Identified

Weill Cornell Medicine researchers are using machine learning to shed light on genetic mutations associated with spina bifida. Their study was published in PNAS.

The scientists developed a new machine-based approach to study a smaller number of people to find genes that distinguish patients with spina bifida versus individuals without the condition, and apply further systems biology tools to assess the relevance of those genes to human spina bifida.

The researchers examined the genomes of 149 people with spina bifida and 149 healthy controls with similar genetic backgrounds. Because spina bifida is rare, studying people from around the globe is necessary to obtain enough data, the team said. Using machine learning, they were able to determine which genes bearing predicted function-changing variants had the greatest potential for distinguishing cases from controls.

The researchers then analyzed how these genes relate to activities at the molecular level. The pathways that were most highly significant involved glucose and lipid metabolism, meaning the body’s ability to break down and use sugar and fats for cell energy and function. “These processes are relevant to conditions like diabetes and obesity,” lead researcher Margaret Ross said. “This really gave us a lot of encouragement that our machine learning approach was coming up with clinically relevant information,” she said, and the method is identifying additional significant molecular pathways that underpin the condition.