New Study Shows Genetic and Developmental Data Can Predict Intellectual Disability in Children with Autism.

A new study led by researchers at the University of Montreal and McGill University reveals that merging genetic data with early developmental indicators can significantly improve the ability to predict intellectual disability (ID) in children diagnosed with autism spectrum disorder (ASD). The findings, published this week in JAMA Pediatrics, demonstrate how combining these two data types enhances clinical prognostic models, offering better support for early diagnosis and personalized care strategies.

“Our approach builds on prior findings that suggest rare genetic variants interact with an individual’s broader genetic background,” said senior and co-corresponding author Sébastien Jacquemont from the University of Montreal.

To build a comprehensive prognostic model, researchers analyzed information from 5,633 children diagnosed with ASD. Participants were drawn from several large cohorts, including the Simons Foundation Powering Autism Research (SPARK), the Simons Simplex Collection (SSC), and MSSNG. Of these children, 4,574 were boys and 1,059 were girls. ASD was typically diagnosed around age 4, and assessments for intellectual disability were conducted between ages 8 and 14.

The team noted that while early indicators of autism often appear between 18 and 36 months of age, it remains difficult to determine which children will later also exhibit intellectual disabilities. Current clinical tools fall short in predicting these developmental outcomes.

Initial analyses showed that genetic variants alone—whether common variants aggregated into polygenic scores or rare, high-impact mutations—offered limited predictive power. Polygenic scores linked to cognitive ability and ASD showed a negative association with ID. However, predictive performance improved when models included rare genetic variations such as de novo missense and loss-of-function mutations, as well as structural variants like deletions and duplications associated with developmental delays.

"Each additional variant type enhanced the model’s positive predictive value (PPV),” the authors reported. By integrating all genetic data, sensitivity rose from 6% (with polygenic scores alone) to 30%, while maintaining a stable PPV of roughly 30%.

However, the most notable improvements occurred when genetic data was combined with information on developmental milestones. Markers such as the age a child first walked or spoke provided added context that strengthened predictions—particularly for children who exhibited delayed milestones.

The researchers found that incorporating genetic information with milestone data substantially increased negative predictive value (NPV), improving the model’s ability to identify children unlikely to develop ID. The predictive utility of genetic variants was found to be twice as strong in children with developmental delays compared to those with typical early development.

The model was initially trained using data from 4,085 children in the SPARK cohort and validated using 1,183 SSC and 365 MSSNG participants. The validation confirmed the enhanced predictive value when early developmental and genetic data were assessed together.

“This study supports the use of predictive modeling as a tool to guide clinical evaluations for children referred for autism,” the researchers concluded. “Such models can offer families clearer insights into potential developmental outcomes and help navigate the uncertainty often associated with ASD diagnoses.”

Source:https://www.genomeweb.com/genetic-research/autism-study-finds-prognostic-value-combining-genetic-early-milestone-data

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