Genomic and Developmental Models to Predict Cognitive and Adaptive Outcomes in Autistic Children
Authors: Vincent-Raphaël Bourque, Zoe Schmilovich, Guillaume Huguet, Jade England, Adeniran Okewole, Cécile Poulain, Thomas Renne, Martineau Jean-Louis, Zohra Saci, Xinhe Zhang, Thomas Rolland, Aurélie Labbé, Jacob Vorstman, Guy A. Rouleau, Simon Baron-Cohen, Laurent Mottron, Richard A. I. Bethlehem, Varun Warrier, Sébastien Jacquemont
Journal: JAMA Pediatrics (2025)

Importance Although early signs of autism are often observed between 18 and 36 months of age, there is considerable uncertainty regarding future development. Clinicians lack predictive tools to identify those who will later be diagnosed with co-occurring intellectual disability (ID). Objective To predict ID in children diagnosed with autism. Design, Setting, and Participants This prognostic study involved the development and validation of models integrating genetic variants and developmental milestones to predict ID. Models were trained, cross-validated, and tested for generalizability across 3 autism cohorts: Simons Foundation Powering Autism Research (SPARK), Simons Simplex Collection, and MSSNG. Autistic participants were assessed older than 6 years of age for ID. Study data were analyzed from January 2023 to July 2024. Exposures Ages at attaining early developmental milestones, occurrence of language regression, polygenic scores for cognitive ability and autism, rare copy number variants, de novo loss-of-function and missense variants impacting constrained genes. Main Outcomes and Measures The out-of-sample performance of predictive models was assessed using the area under the receiver operating characteristic curve (AUROC), positive predictive values (PPVs), and negative predictive values (NPVs). Results A total of 5633 autistic participants (4574 male [81.2%]) were included in this analysis. On average, participants were diagnosed with autism at 4 (IQR, 3-7) years of age and assessed for ID at 11 (8-14) years of age, with 1159 participants (20.6%) being diagnosed with ID. The model integrating all predictors yielded an AUROC of 0.653 (95% CI, 0.625-0.681), and this predictive performance was cross-validated and generalized across cohorts. This modest performance reflected that only a subset of individuals carried large-effect variants, high polygenic scores, or presented delayed milestones. However, combinations of genetic variants that are typically not considered clinically relevant by diagnostic laboratories achieved PPVs of 55% and correctly identified 10% of individuals developing ID. The addition of polygenic scores to developmental milestones specifically improved NPVs rather than PPVs. Notably, the ability to stratify ID probabilities using genetic variants was up to 2-fold higher in individuals with delayed milestones compared with those with typical development. Conclusions and Relevance Results of this prognostic study suggest that the growing number of neurodevelopmental condition–associated variants cannot, in most cases, be used alone for predicting ID. However, models combining different classes of variants with developmental milestones provide clinically relevant individual-level predictions that could be useful for targeting early interventions.