Agentic Artificial Intelligence-Driven Explainable Deep Learning for Deciphering Noncoding Pathogenic Mechanisms of Delirium Through Genomic Big Data Integration

Delirium is a prevalent neuropsychiatric syndrome affecting up to 50% of hospitalized elderly patients, associated with increased mortality, cognitive decline, and health care costs exceeding $164 billion annually. Despite its clinical burden, the genetic architecture underlying susceptibility to delirium remains poorly characterized. We developed an agentic artificial intelligence pipeline integrating genome-wide association study (GWAS) data from FinnGen Release 12 (6854 cases; 384,461 controls) with causal transcriptome-wide association study (cTWAS) using brain-specific eQTL data from Genotype-Tissue Expression. Spatial transcriptomics analysis (gsMap) was performed on the human dorsolateral prefrontal cortex to characterize layer-specific expression patterns. Deep learning models (Enformer, SpliceTransformer) were applied to predict functional consequences of identified variants. GWAS identified a strongly associated locus on chromosome 19q13.32, with lead variant rs429358 ( P = 2.79 × 10 −70 ) defining the apolipoprotein E ( APOE ) ε4 allele. cTWAS analysis identified two causal genes: APOE (posterior inclusion probabilities [PIP] = 0.871, Z = −8.44) and ZNF226 (PIP = 0.604, Z = 5.63), exhibiting opposite directional effects. APOE downregulation increased susceptibility to delirium, while ZNF226 upregulation conferred an elevated risk. Spatial transcriptomics revealed significant GWAS enrichment in Layer 5 ( P < 0.001), with distinct layer-specific expression patterns for both genes. Enformer predicted that rs429358 substantially altered chromatin accessibility at promoter regions (H3K4me3 SNP Activity Difference = 0.30). SpliceTransformer identified significant splice site perturbations (maximum Δ = 0.45). This study provides a comprehensive genetic dissection of delirium through agentic artificial intelligence-driven multiomics integration. We identify APOE and ZNF226 as causal genes with distinct mechanisms and spatial expression patterns. These findings establish a molecular framework for understanding delirium pathophysiology and identify potential therapeutic targets.

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Journal
Big Data
Published
2026-06-29
DOI
https://doi.org/10.1177/2167647x261463929
Primary Topic
Intensive Care Unit Cognitive Disorders
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article
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article

Agentic Artificial Intelligence-Driven Explainable Deep Learning for Deciphering Noncoding Pathogenic Mechanisms of Delirium Through Genomic Big Data Integration

Yuting Xue, Lishuang Peng, Jianfu Tang, Xiong Wang et al.
Big Data
Intensive Care Unit Cognitive Disorders
article

Agentic Artificial Intelligence-Driven Explainable Deep Learning for Deciphering Noncoding Pathogenic Mechanisms of Delirium Through Genomic Big Data Integration

Yuting Xue, Lishuang Peng, Jianfu Tang, Xiong Wang, Shanshan Cai, Jidong Yang
article en

Abstract

Delirium is a prevalent neuropsychiatric syndrome affecting up to 50% of hospitalized elderly patients, associated with increased mortality, cognitive decline, and health care costs exceeding $164 billion annually. Despite its clinical burden, the genetic architecture underlying susceptibility to delirium remains poorly characterized. We developed an agentic artificial intelligence pipeline integrating genome-wide association study (GWAS) data from FinnGen Release 12 (6854 cases; 384,461 controls) with causal transcriptome-wide association study (cTWAS) using brain-specific eQTL data from Genotype-Tissue Expression. Spatial transcriptomics analysis (gsMap) was performed on the human dorsolateral prefrontal cortex to characterize layer-specific expression patterns. Deep learning models (Enformer, SpliceTransformer) were applied to predict functional consequences of identified variants. GWAS identified a strongly associated locus on chromosome 19q13.32, with lead variant rs429358 ( P = 2.79 × 10 −70 ) defining the apolipoprotein E ( APOE ) ε4 allele. cTWAS analysis identified two causal genes: APOE (posterior inclusion probabilities [PIP] = 0.871, Z = −8.44) and ZNF226 (PIP = 0.604, Z = 5.63), exhibiting opposite directional effects. APOE downregulation increased susceptibility to delirium, while ZNF226 upregulation conferred an elevated risk. Spatial transcriptomics revealed significant GWAS enrichment in Layer 5 ( P < 0.001), with distinct layer-specific expression patterns for both genes. Enformer predicted that rs429358 substantially altered chromatin accessibility at promoter regions (H3K4me3 SNP Activity Difference = 0.30). SpliceTransformer identified significant splice site perturbations (maximum Δ = 0.45). This study provides a comprehensive genetic dissection of delirium through agentic artificial intelligence-driven multiomics integration. We identify APOE and ZNF226 as causal genes with distinct mechanisms and spatial expression patterns. These findings establish a molecular framework for understanding delirium pathophysiology and identify potential therapeutic targets.

Big Data
First Affiliated Hospital of Sichuan Medical University (CN), Lancaster University (GB), University of Newcastle Australia (AU)
Good health and well-being
Openalex Percentile: Top 7%
Intensive Care Unit Cognitive Disorders
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