New approach to analyzing genetics underlying spina bifida

Weill Cornell Medicine researchers are using machine learning, a form of artificial intelligence, to shed light on genetic mutations associated with spina bifida. In this birth defect, the neural tube that forms the spinal cord during pregnancy does not close so that spinal nerves are exposed, resulting in paralysis and high risk of other complications. Their new study, published Dec. 16 in PNAS, "brings us closer to being able to provide a precision medicine approach to families who are looking to ensure healthy birth outcomes and the greatest potential for infants affected by spina bifida," said senior author Dr. Margaret Elizabeth Ross, director of the Center for Neurogenetics and professor of neuroscience in the Feil Family Brain and Mind Research Institute and the Nathan Cummings Professor in Neurology at Weill Cornell Medicine. Spina bifida is a complex genetic disorder, meaning it's not generally caused by malfunction in a single gene but usually requires an interplay of several genes that have been altered in relatively small ways. Environmental conditions such as nutrition and the medications and supplements women are taking can also impact fetal health.