ArticleJournal of molecular cell biology2019
Multi-scale analysis of schizophrenia risk genes, brain structure, and clinical symptoms reveals integrative clues for subtyping schizophrenia patients.
Article in Journal of molecular cell biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 19 citations in OpenAlex.
- Brain Structure Measurements Predict Individualized Treatment Outcome of 12-Week Antipsychotic Monotherapies in First-episode Schizophrenia.Schizophrenia bulletin · 2023Trial
- Neuroimaging-Based Subgroups in Schizophrenia: A Critical Appraisal of Clustering Studies.Biological psychiatry global open science · 2026Review
- Multi-omics approaches to major psychiatric disorders.Neuropsychiatrie : Klinik, Diagnostik, Therapie und Rehabilitation : Organ der Gesellschaft Osterreichischer Nervenarzte und Psychiater · 2026Review
- Recent developments in imaging transcriptomics for psychiatric disorders.Psychoradiology · 2026Review
- Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade.Schizophrenia bulletin · 2025Review
- Evaluating the causal relationship of Levo-carnitine and risk of schizophrenia: a bidirectional two-sample mendelian randomization study.BMC psychiatry · 2024Article
- STW-MD: a novel spatio-temporal weighting and multi-step decision tree method for considering spatial heterogeneity in brain gene expression data.Briefings in bioinformatics · 2024Article
- Imaging Transcriptomics of the Brain for Schizophrenia.Alpha psychiatry · 2024Review
- Dissociation between neuroanatomical and symptomatic subtypes in schizophrenia.European psychiatry : the journal of the Association of European Psychiatrists · 2023Article
- Transcriptomics and magnetic resonance imaging in major psychiatric disorders.Frontiers in psychiatry · 2023Review
- Current challenges and possible future developments in personalized psychiatry with an emphasis on psychotic disorders.Heliyon · 2020Review
Corrections and comments
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Authors and funding
10 authors at 7 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Analysis linking directly genomics, neuroimaging phenotypes and clinical measurements is crucial for understanding psychiatric disorders, but remains rare. Here, we describe a multi-scale analysis using genome-wide SNPs, gene expression, grey matter volume (GMV), and the positive and negative syndrome scale scores (PANSS) to explore the etiology of schizophrenia. With 72 drug-naive schizophrenic first episode patients (FEPs) and 73 matched heathy controls, we identified 108 genes, from schizophrenia risk genes, that correlated significantly with GMV, which are highly co-expressed in the brain during development. Among these 108 candidates, 19 distinct genes were found associated with 16 brain regions referred to as hot clusters (HCs), primarily in the frontal cortex, sensory-motor regions and temporal and parietal regions. The patients were subtyped into three groups with distinguishable PANSS scores by the GMV of the identified HCs. Furthermore, we found that HCs with common GMV among patient groups are related to genes that mostly mapped to pathways relevant to neural signaling, which are associated with the risk for schizophrenia. Our results provide an integrated view of how genetic variants may affect brain structures that lead to distinct disease phenotypes. The method of multi-scale analysis that was described in this research, may help to advance the understanding of the etiology of schizophrenia.
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