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<p>Despite high heritability, much of the genetic architecture of schizophrenia (SZ) remains unexplained, and early-onset SZ may represent a more genetically homogeneous subtype. We examined genetically regulated expression risk scores (GeRS) to identify gene networks associated with early-onset SZ and characterize their biological functions. Using genotype data from patients in multiplex families (223 early-onset and 372 late-onset) and simplex families (matched for sex and onset age), SZ-GeRS were estimated based on single-nucleotide polymorphism (SNP)–expression prediction models derived from two transcriptomic reference panels (dorsolateral prefrontal cortex tissue from individuals with psychiatric disorders and brain cortex tissue from the general population). Panel-specific GeRS were constructed using transcriptome-wide association studies (TWAS)–derived effect sizes and integrated using a fixed-effects meta-analytic framework. Module-based SZ-GeRS were constructed by aggregating genes within empirically derived co-expression clusters, followed by network analysis to identify hub genes and functional mapping to infer biological context. Among 13 co-expression modules, meta-analysis identified a significant association between early-onset SZ and Module 10 GeRS (M10<sub>sub</sub>-GeRS: adjusted odds ratio [aOR] = 1.22, 95%CI = 1.07–1.40). Hub genes prioritized by complementary network approaches also showed significant associations. Functional enrichment analyses indicated predominant involvement of excitatory neuronal processes and immune-related pathways. Sensitivity analyses using a variance-calibrated TWAS approach to account for polygenicity-driven inflation attenuated gene-level signals but did not materially alter module-level associations, with M10<sub>sub</sub>-GeRS remaining robustly associated with early-onset SZ. These findings highlight gene regulatory networks linking genetic susceptibility to molecular mechanisms in early-onset SZ and may inform risk stratification and targeted interventions.</p>

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Identification of hub genes involved in early-onset schizophrenia: from genetic susceptibility to predicted regulated gene expression

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月3日

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  • Yawen Jen,
  • Sung-Liang Yu,
  • Po-Chang Hsiao,
  • Po-Hsiu Kuo,
  • Chih-Min Liu,
  • Chen-Chung Liu,
  • Tzung-Jeng Hwang,
  • Ming H. Hsieh,
  • Yi-Ling Chien,
  • Yi-Ting Lin,
  • Hailiang Huang,
  • Yen-Chen Anne Feng,
  • Chuhsing K. Hsiao,
  • Yen-Feng Lin,
  • Stephen V. Faraone,
  • Benjamin Neale,
  • Stephen J. Glatt,
  • Ming T. Tsuang,
  • Hai-Gwo Hwu,
  • Wei J. Chen

摘要

Despite high heritability, much of the genetic architecture of schizophrenia (SZ) remains unexplained, and early-onset SZ may represent a more genetically homogeneous subtype. We examined genetically regulated expression risk scores (GeRS) to identify gene networks associated with early-onset SZ and characterize their biological functions. Using genotype data from patients in multiplex families (223 early-onset and 372 late-onset) and simplex families (matched for sex and onset age), SZ-GeRS were estimated based on single-nucleotide polymorphism (SNP)–expression prediction models derived from two transcriptomic reference panels (dorsolateral prefrontal cortex tissue from individuals with psychiatric disorders and brain cortex tissue from the general population). Panel-specific GeRS were constructed using transcriptome-wide association studies (TWAS)–derived effect sizes and integrated using a fixed-effects meta-analytic framework. Module-based SZ-GeRS were constructed by aggregating genes within empirically derived co-expression clusters, followed by network analysis to identify hub genes and functional mapping to infer biological context. Among 13 co-expression modules, meta-analysis identified a significant association between early-onset SZ and Module 10 GeRS (M10sub-GeRS: adjusted odds ratio [aOR] = 1.22, 95%CI = 1.07–1.40). Hub genes prioritized by complementary network approaches also showed significant associations. Functional enrichment analyses indicated predominant involvement of excitatory neuronal processes and immune-related pathways. Sensitivity analyses using a variance-calibrated TWAS approach to account for polygenicity-driven inflation attenuated gene-level signals but did not materially alter module-level associations, with M10sub-GeRS remaining robustly associated with early-onset SZ. These findings highlight gene regulatory networks linking genetic susceptibility to molecular mechanisms in early-onset SZ and may inform risk stratification and targeted interventions.