AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A cross-disorder transcriptomic framework was applied to identify shared molecular dysregulation across ASD, BD, and SCZ and identified 20 high-confidence recurrent genes, with pronounced transcriptional convergence observed between BD and SCZ.
These results support transcriptomic divergence in the prefrontal cortex, with ASD characterized by spliceosomal dysregulation, contrasting with metal ion response, vascular regulation and inflammation-associated signals in BD.
This study finds that rare variants across hundreds of genes contribute to autism with variable phenotypic outcomes, and clusters them based on association evidence from large-scale studies of developmental disorders, schizophrenia, bipolar disorder, and epilepsy.
The study provides an integrated framework linking genetic variation to molecular dysfunction and clinical outcomes, offering valuable insights for future research and therapeutic development in pediatric neurology.
Case-Case GWAS is used to identify common genetic variants differentially associated with schizophrenia and bipolar disorder, analysing 67,390 schizophrenia cases and 41,917 bipolar disorder cases and demonstrating that genuine divergent genetic effects exist beyond the substantial shared liability.
Integrative pathogenetic mechanisms of ASD are explored, encompassing prenatal viral exposure, alterations in serotonin and oxytocin signaling pathways, regulation of N-methyl-D-aspartate receptors, SHANK proteins, and members of the Solute Carrier protein family.
It is shown that SCZ and BIP genetic risk is best understood as biologically distinguishable shared and disorder-predominant dimensions that differentially map onto cognitive, cardiometabolic, immune, and molecular architecture.
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