Hidden causal inference delineates dynamic lncRNA regulation in autism spectrum disorder.

Long non-coding RNAs (lncRNAs) are increasingly implicated in autism spectrum disorder (ASD), yet their dynamic regulatory roles remain poorly characterized. Conventional static causal inference methods are limited in capturing the dynamic interplay of lncRNAs across diverse brain biological contexts. Here, we develop DCRNet (Dynamic Causal Regulation Network), a hidden causality-based framework to infer lncRNA regulatory relationships in ASD. Our analysis reveals that dark causality, representing a hybrid form of positive and negative interdependencies, is prevalent in lncRNA-mediated regulation. Moreover, these dynamic regulatory patterns emerge across multiple biological contexts, and eight ASD susceptibility lncRNAs may be linked to disrupted gene regulatory programs. We also indicate that lncRNA expression signatures distinguish sex and cell type with high accuracy, offering candidate diagnostic biomarkers. Notably, 20 immune-related lncRNAs are predicted to be involved in neuroimmune regulation. Additionally, cell-cell interaction networks show notable differences between ASD and control cohorts. Furthermore, DCRNet performs comparably to the other two causal inference methods and outperforms a correlation-based approach in capturing dynamic lncRNA regulation. This study provides a framework for delineating dynamic lncRNA regulation and suggests their potential relevance to ASD pathophysiology.