AI Accelerates Discovery of Neurodevelopmental Disorder-Associated Genes

Researchers have developed an artificial intelligence (AI) approach that accelerates the identification of genes that contribute to neurodevelopmental conditions such as autism spectrum disorder, epilepsy and developmental delay. This new powerful computational tool can help fully characterize the genetic landscape of neurodevelopmental disorders, which is key to making accurate molecular diagnosis, elucidating disease mechanism and developing targeted therapies. The study appeared in the American Journal of Human Genetics.

"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," said first and co-corresponding author Dr. Ryan S. Dhindsa, assistant professor of pathology and immunology at Baylor College of Medicine and principal investigator at the Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital.

Typically, to discover new genes associated with a disease, researchers sequence the genomes of many individuals with the disorders and compare them to the genomes of people without the disorders. "We took a complementary approach," Dhindsa said. "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 researchers looked for patterns in gene expression measured at the single-cell level from the developing human brain. "We found that AI models trained solely on these expression data can robustly predict genes implicated in autism spectrum disorder, developmental delay and epilepsy. But we wanted to take this work a step further," Dhindsa said.

To enhance the models even further, the team incorporated more than 300 other biological features, including measures of how intolerant genes are to mutations, whether they interact with other known disease-associated genes and their functional roles in different biological pathways.

"These models have exceptionally high predictive value," Dhindsa said. "Top-ranked genes were up to two-fold or six-fold, depending on the mode of inheritance, more enriched for high-confidence neurodevelopmental disorder risk genes compared to genic intolerance metrics alone. Additionally, some top-ranking genes were 45 to 500 times more likely to be supported by the literature than lower ranking genes."

"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," Dhindsa said. "We hope that our models will accelerate gene discovery and patient diagnoses, and future studies will assess this possibility."

Dhindsa RS, Weido BA, Dhindsa JS, Shetty AJ, Sands CF, Petrovski S, Vitsios D, Zoghbi AW.
Genome-wide prediction of dominant and recessive neurodevelopmental disorder-associated genes.
Am J Hum Genet. 2025 Mar 6;112(3):693-708. doi: 10.1016/j.ajhg.2025.02.001

Most Popular Now

Giving Doctors an AI-Powered Head Start …

Detection of melanoma and a range of other skin diseases will be faster and more accurate with a new artificial intelligence (AI) powered tool that analyses multiple imaging types simultaneously...

AI Medical Receptionist Modernizing Doct…

A virtual medical receptionist named "Cassie," developed through research at Texas A&M University, is transforming the way patients interact with health care providers. Cassie is a digital-human assistant created by Humanate...

Using Data and AI to Create Better Healt…

Academic medical centers could transform patient care by adopting principles from learning health systems principles, according to researchers from Weill Cornell Medicine and the University of California, San Diego. In...

Northern Ireland Completes Nationwide Ro…

Go-lives at Western and Southern health and social care trusts mean every pathology service is using the same laboratory information management system; improving efficiency and quality. An ambitious technology project to...

AI Detects Hidden Heart Disease Using Ex…

Mass General Brigham researchers have developed a new AI tool in collaboration with the United States Department of Veterans Affairs (VA) to probe through previously collected CT scans and identify...

AI Tool Set to Transform Characterisatio…

A multinational team of researchers, co-led by the Garvan Institute of Medical Research, has developed and tested a new AI tool to better characterise the diversity of individual cells within...

Human-AI Collectives Make the Most Accur…

Diagnostic errors are among the most serious problems in everyday medical practice. AI systems - especially large language models (LLMs) like ChatGPT-4, Gemini, or Claude 3 - offer new ways...

Highland Marketing Announced as Official…

Highland Marketing has been named, for the second year running, the official communications partner for HETT Show 2025, the UK's leading digital health conference and exhibition. Taking place 7-8 October...

MHP-Net: A Revolutionary AI Model for Ac…

Liver cancer is the sixth most common cancer globally and a leading cause of cancer-related deaths. Accurate segmentation of liver tumors is a crucial step for the management of the...

Groundbreaking TACIT Algorithm Offers Ne…

Researchers at VCU Massey Comprehensive Cancer Center have developed a novel algorithm that could provide a revolutionary tool for determining the best options for patients - both in the treatment...

The Many Ways that AI Enters Rheumatolog…

High-resolution computed tomography (HRCT) is the standard to diagnose and assess progression in interstitial lung disease (ILD), a key feature in systemic sclerosis (SSc). But AI-assisted interpretation has the potential...