AI Sheds Light on Genetic Mutations Associated with Spina Bifida

Spina bifida (SB) is a complex genetic disorder that shows non-Mendelian inheritance, with genetic factors contributing to an estimated 70% of cases. In SB, the neural tube that forms the spinal cord during pregnancy does not close. As a result, spinal nerves are exposed resulting in paralysis and a high risk of other complications.

Genetic investigations of most structural birth defects, including SB, congenital heart disease, and craniofacial anomalies, have been underpowered for genome-wide association studies because of their rarity, genetic heterogeneity, incomplete penetrance, and environmental influences. To that end, it is challenging to investigate the role of genetic variation in individual families, coupled with environmental factors. Now, researchers have developed a systems biology approach to investigate SB predisposition.

These findings are published in PNAS in the paper, “Systems biology analysis of human genomes points to key pathways conferring spina bifida risk.”

This work “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 Margaret Elizabeth Ross, MD, PhD, the Nathan Cummings professor in neurology and director of the Center for Neurogenetics at the Feil Family Brain and Mind Research Institute.

Ross and her team devise new ways for genome-wide investigations of complex genetic conditions that are less common but still impact many families. To do this, they developed an unbiased approach to study a smaller number of people to find genes that distinguish patients with SB versus individuals without the condition, and apply further systems biology tools to assess relevance of those genes to SB.

The researchers examined the genomes of 149 people with SB and 149 healthy controls with similar genetic backgrounds—in both the United States and Qatar. Because SB is rare, studying people from around the globe is necessary to obtain enough data, Ross 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. “These processes are relevant to conditions like diabetes and obesity,” Ross said. Diabetes and obesity during pregnancy are both known risk factors for neural tube defects. “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.

“We continue to build an international consortium of clinicians and families to increase the power of this approach toward understanding human spina bifida,” Ross said. Ultimately, she hopes it will be possible to analyze the genomes of couples who want to conceive, to identify their optimal strategies for preventing spina bifida. For example, for some couples, additional folic acid may be an excellent preventive measure, while for others, taking a supplement like inositol—which can support cell membrane function—may help lower the risk of spina bifida, according to some studies.

“One day,” Ross added, “we will be able to counsel individual couples on what is the most effective route for them to have a healthy birth outcome, and for a child affected by spina bifida, to optimize their development and quality of life into adulthood.”