New framework offers fresh insights into autism risk factors

Researchers at Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University School of Medicine, and Kaiser Permanente Northern California introduced a new statistical framework to better understand how genetics and environment interact in autism risk. The framework analyzes genetic risk scores using family data—including DNA from autistic children and their parents—to assess factors like maternal diet, lifestyle, and pregnancy complications alongside genetic predisposition. The study, published June 2 in *Nature Genetics*, examined over 18,000 case-parent trios across diverse ancestral populations through the Simons Foundation Powering Autism Research for Knowledge consortium and the Genes and Environment Autism Research Study. It found that existing genetic risk scores accurately predict autism in European-ancestry populations but perform poorly for African-ancestry groups, emphasizing the need for broader data representation. Key findings include maternal genetic susceptibility to traits like obesity and neurocognitive characteristics contributing to autism risk, alongside known maternal factors such as pregnancy complications. Senior author Nilanjan Chatterjee, a biostatistics professor at Johns Hopkins, noted the framework’s potential to uncover new risk factors and biomarkers by integrating family data with population-based studies. The researchers plan to expand the framework to analyze extended family structures and unify findings with larger genetic studies. They are also compiling diverse population data to develop separate genetic scores for children and adults, aiming to improve universal applicability. However, they caution that study design biases may affect results, requiring further validation. The study underscores disparities in genetic research and advocates for inclusive data collection to refine predictive models for autism and other developmental conditions.