Background: Topological Limitations of Existing Single-Gene-Centric cis-eQTL Analysis Models and the Multifactorial Genomic Data Bottleneck in Psychiatric Neuroscience R&D

Existing GWAS and cis-eQTL-based linear expression imputation models have exposed critical blind spots in R&D for highly complex, multifactorial psychiatric disorders such as schizophrenia. Conventional transcriptome-wide association studies (TWAS) rely heavily on variants near target genes (cis), failing to reflect the cellular dissociation-induced noise and the regulatory flux of trans-eQTLs, which are distal regulatory factors. The platform cannot computationally control in silico the high-dimensional interactions of complex co-expressed transcriptional networks within the brain microenvironment, interspecies expression differences, and cell-type-specific, feedback fluxes, leading to repeated failures in achieving clinical efficacy and maintaining the target drug concentration. Existing analysis guidelines have neglected the trans-regulatory pathways of non-coding genomic variants, which account for the vast majority of expression variation, creating data barriers and computational bottlenecks.

Discovery: Activation of the INGENE-MODULE Tensor Interconnection Algorithm and Demonstration of Single-Cell Resolution, Multidimensional Genetic Variation Independent Variable Tensor Synchronization

This study introduces the INGENE and MODULE computational architecture, which integrates the mechanism of action of trans-eQTLs into a network-based transcriptional model, disruptively exceeding the baseline of complex disease genomic analysis. This platform fine-tunes the subtle dynamics of how distal genetic variations within co-expression networks affect the binding free energy of target transcription factors and proactively calculates single-cell rate constants based on differential equations in silico. It computationally eliminates batch effects that inevitably occur in large-scale sequencing datasets and successfully synchronizes discrete cis and trans-eQTL independent variables into a three-dimensional proxy matrix tensor. This enables the ultra-high-resolution mapping of downstream transcriptional topological variation curves in human prefrontal cortical cells, computationally demonstrating the molecular integrity of the schizophrenia pathogenesis pathway.

Establishment of a Model for Fine-Layered, Reversible Neuro-Homeostatic Regulation by Coordinating Synaptic Plasticity Control Pathways

Based on the synchronized omics matrices, a personalized molecular phenotype for each patient and a precision stratification model based on family genomic data are established. The study simulates the transcriptional kinetics of how schizophrenia susceptibility variants affect the formation of synaptic junction protein complexes, calculating precise up- and down-clamping scenarios for rate-limiting step constants. An autonomous regulation backbone is constructed to ensure that intracellular calcium channel and glutamate receptor densities can reversibly restore neuro-homeostasis even in aberrant neuronal network stress situations. This implements a multifactorial, dynamic clamping control loop on the omics interface to overcome schizophrenia-specific subtype classification and individual drug response variability.

Prospects: Establishment of a Programmable Computational Systems Biology Standard and Launch of a Next-Generation IND Digital Governance System

The advent of this framework shifts the governance of neuropsychiatric R&D from the conventional, post-hoc, candidate-based drug discovery system to a fully reset, multidimensional tensor-based programmable computational infrastructure. In the $30 billion global schizophrenia drug market, which has grown rapidly since the FDA approval of Cobenfy in 2024, this provides a computational moat that will accelerate the pipeline expansion of multinational pharmaceutical companies and B2B biotech companies. It interlinks multidimensional genetic gradient correction coefficients generated during high-throughput screening to eliminate batch-to-batch variation and simultaneously meets digital healthcare and companion diagnostic (CDx) specifications. This is a key digital governance asset that will disruptively shorten the IND approval evaluation framework timeline and enable early compliance with cGMP commercial production standards.