Unraveling the immunogenetic landscape of autism spectrum disorder: a comprehensive bioinformatics approach.

Background: Autism spectrum disorder (ASD) is a disease characterized by social disorder. Recently, the population affected by ASD has gradually increased around the world. There are great difficulties in diagnosis and treatment at present. Methods: The ASD datasets were obtained from the Gene Expression Omnibus database and the immune-relevant genes were downloaded from a previously published compilation. Subsequently, we used WGCNA to screen the modules related to the ASD and immune. We also choose the best combination and screen out the core genes from Consensus Machine Learning Driven Signatures (CMLS). Subsequently, we evaluated the genetic correlation between immune cells and ASD used GNOVA. And pleiotropic regions identified by PLACO and CPASSOC between ASD and immune cells. FUMA was used to identify pleiotropic regions, and expression trait loci (EQTL) analysis was used to determine their expression in different tissues and cells. Finally, we use qPCR to detect the gene expression level of the core gene. Results: We found a close relationship between neutrophils and ASD, and subsequently, CMLS identified a total of 47 potential candidate genes. Secondly, GNOVA showed a significant genetic correlation between neutrophils and ASD, and PLACO and CPASSOC identified a total of 14 pleiotropic regions. We annotated the 14 regions mentioned above and identified a total of 6 potential candidate genes. Through EQTL, we found that the CFLAR gene has a specific expression pattern in neutrophils, suggesting that it may serve as a potential biomarker for ASD and is closely related to its pathogenesis. Conclusions: In conclusion, our study yields unprecedented insights into the molecular and genetic heterogeneity of ASD through a comprehensive bioinformatics analysis. These valuable findings hold significant implications for tailoring personalized ASD therapies.