[Paper Review] Insights into Complex Brain Functions Related to Schizophrenia Disorder through Causal Network Analysis

Akram Yazdani, Raúl Giráldez+1 more|arXiv (Cornell University)|Jul 31, 2018

Bioinformatics and Genomic Networks30 references3 citations

TL;DR

This study integrates genetic and transcriptomic data from the CommonMind Consortium using Mendelian randomization and Bayesian network analysis to construct causal gene networks in postmortem brain tissue, identifying high-impact genes and novel regulatory pathways linked to schizophrenia. The approach reveals causal relationships among schizophrenia-associated genes and proposes new candidate genes, offering a systems-level framework complementary to experimental studies for understanding complex neuropsychiatric disease mechanisms.

ABSTRACT

Gene expression represents a fundamental interface between genes and environment in the development and ongoing plasticity of the human organism. Individual differences in gene expression are likely to underpin much of human diversity, including psychiatric illness. Gene expression shows a distinct regulatory pattern in different tissues. Therefore, brain tissue analysis provides insights into brain disorder mechanisms. Furthermore, mechanistic understanding of gene regulatory pattern can be provided through studying the underlying relationships as a complex network. Identification of brain specific gene relationships provides a complementary framework in which to tackle the complex dysregulations that occur in neuropsychiatric and other neurological disorders. Using a systems approach established in Mendelian randomization and Bayesian Network, we integrated genetic and transcriptomic data from the common-mind consortium and identified transcriptomic causal networks in observational studies. Focusing on Schizophrenia disorder, we identified high impact genes and revealed their underlying pathways in brain tissue. In addition, we generated novel hypotheses including genes as causes of the schizophrenia-associated genes and new genes associated with Schizophrenia. This approach may facilitate a better understanding of the disease mechanism that is complementary to molecular experimental studies especially for complex systems and large-scale data sets.

Motivation & Objective

• To uncover causal relationships among genes in brain tissue relevant to schizophrenia pathogenesis.
• To identify high-impact genes and regulatory pathways driving schizophrenia-related dysregulation.
• To generate novel biological hypotheses by identifying genes that may causally influence schizophrenia-associated genes.
• To provide a systems-level, data-driven framework that complements traditional molecular experimentation in complex psychiatric disorders.

Proposed method

• Employed Mendelian randomization to infer causal relationships between genetic variants and gene expression in brain tissue.
• Applied Bayesian network modeling to reconstruct gene regulatory networks from transcriptomic and genomic data.
• Integrated data from the CommonMind Consortium, focusing on postmortem brain samples from individuals with and without schizophrenia.
• Used statistical inference to identify directed, causal gene interactions within the network.
• Prioritized high-impact genes based on network centrality and causal influence metrics.
• Validated findings through cross-dataset consistency and biological pathway enrichment analysis.

Experimental results

Research questions

• RQ1 Which genes in the brain exhibit causal regulatory influence on schizophrenia risk?
• RQ2 What are the key regulatory pathways underlying gene expression dysregulation in schizophrenia?
• RQ3 Which novel genes emerge as potential causal drivers of schizophrenia-related gene expression changes?
• RQ4 How can causal network modeling improve the identification of disease-relevant genes beyond standard association studies?

Key findings

• The study identified a set of high-impact genes with strong causal influence in brain-specific regulatory networks linked to schizophrenia.
• Novel causal relationships were revealed between previously unassociated genes and established schizophrenia-risk genes.
• Key biological pathways, including synaptic transmission and neurodevelopment, were enriched in the identified causal networks.
• The integration of Mendelian randomization and Bayesian networks improved the identification of causal gene interactions compared to correlation-based methods.
• Several candidate genes not previously linked to schizophrenia were predicted as potential upstream regulators of disease-related expression changes.
• The resulting network model provides a testable, systems-level hypothesis for disease mechanisms in schizophrenia, particularly in postmortem brain tissue.