Researchers have introduced an artificial intelligence (AI) method to speed up the identification of genes linked to neurodevelopmental disorders, including autism spectrum disorder, epilepsy, and developmental delay. This computational tool aims to provide a comprehensive understanding of the genetic factors involved in these conditions, aiding in accurate diagnosis and the development of targeted treatments. The study was published in the American Journal of Human Genetics.
Dr. Ryan S. Dhindsa, assistant professor at Baylor College of Medicine and principal investigator at Texas Children’s Hospital, highlighted the need for this research: “Although researchers have made major strides identifying different genes associated with neurodevelopmental disorders, many patients with these conditions still do not receive a genetic diagnosis, indicating that there are many more genes waiting to be discovered.”
Traditionally, gene discovery involves sequencing genomes from affected individuals and comparing them with those without the disorders. Dr. Dhindsa explained their alternative approach: “We used AI to find patterns among genes already linked to neurodevelopmental diseases and predict additional genes that might also be involved in these disorders.”
The team analyzed gene expression data from developing human brains at the single-cell level. “We found that AI models trained solely on these expression data can robustly predict genes implicated in autism spectrum disorder, developmental delay and epilepsy,” said Dhindsa.
To improve their models further, they included over 300 biological features such as gene mutation intolerance and interactions with known disease-associated genes. Dhindsa noted the models' effectiveness: “Top-ranked genes were up to two-fold or six-fold...more enriched for high-confidence neurodevelopmental disorder risk genes compared to genic intolerance metrics alone.”
Dhindsa expressed hope for future applications: “We see these models as analytical tools that can validate genes that are beginning to emerge from sequencing studies but don’t yet have enough statistical proof of being involved in neurodevelopmental conditions.”
Contributors to this research include Blake A. Weido, Justin S. Dhindsa, Arya J. Shetty, Chloe F. Sands, Slavé Petrovski, Dimitrios Vitsios, and Anthony W. Zoghbi from institutions such as Baylor College of Medicine and AstraZeneca.
The study received funding from NIH NINDS (F32 NS127854), NIH (DP5 OD036131), Norn Group's Longevity Impetus Grant, Hevolution Foundation, Rosenkranz Foundation, and grant K23MH121669.
