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

IF 2 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Big Data Pub Date : 2026-08-01 Epub Date: 2026-06-29 DOI:10.1177/2167647X261463929
Jidong Yang, Xiong Wang, Lishuang Peng, Jianfu Tang, Shanshan Cai, Yuting Xue
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引用次数: 0

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.

代理人工智能驱动的可解释深度学习通过基因组大数据集成破解谵妄的非编码致病机制。
谵妄是一种常见的神经精神综合征,影响多达50%的住院老年患者,与死亡率增加、认知能力下降和每年超过1640亿美元的医疗保健费用相关。尽管它的临床负担,遗传结构的潜在易感性谵妄仍然缺乏特征。我们开发了一个代理人工智能管道,将FinnGen Release 12(6854例;384,461例对照)的全基因组关联研究(GWAS)数据与来自基因型-组织表达的脑特异性eQTL数据的因果转录组关联研究(cTWAS)整合在一起。对人类背外侧前额叶皮层进行空间转录组学分析(gsMap)以表征层特异性表达模式。应用深度学习模型(Enformer, SpliceTransformer)来预测已识别变体的功能后果。GWAS在染色体19q13.32上发现了一个与载脂蛋白E (APOE) ε4等位基因密切相关的位点,其中铅变异位点rs429358 (P = 2.79 × 10-70)定义载脂蛋白E (APOE) ε4等位基因。cTWAS分析发现两个致病基因:APOE(后验包含概率[PIP] = 0.871, Z = -8.44)和ZNF226 (PIP = 0.604, Z = 5.63),表现出相反的方向效应。APOE下调会增加谵妄的易感性,而ZNF226上调会增加谵妄的风险。空间转录组学显示GWAS在第5层显著富集(P < 0.001),两种基因具有不同的层特异性表达模式。Enformer预测rs429358显著改变了启动子区域的染色质可及性(H3K4me3 SNP活性差异= 0.30)。SpliceTransformer发现了显著的剪接位点扰动(最大值Δ = 0.45)。本研究通过人工智能驱动的多组学整合提供了谵妄的全面遗传解剖。我们发现APOE和ZNF226是具有不同机制和空间表达模式的致病基因。这些发现为理解谵妄的病理生理和确定潜在的治疗靶点建立了分子框架。
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来源期刊
Big Data
Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
9.10
自引率
2.20%
发文量
60
期刊介绍: Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.
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