Enhancing genetic discovery through narrow phenotyping in schizophrenia

Area of Science:

• Psychiatric Genetics
• Molecular Psychiatry
• Computational Biology

Background:

• Schizophrenia exhibits significant heterogeneity due to its polygenic nature, leading to distinct patient subphenotypes with varied symptoms and prognoses.
• Identifying genetic factors contributing to severe schizophrenia outcomes is crucial for personalized medicine.

Purpose of the Study:

• To identify genetic variants associated with disruptive schizophrenia phenotypes.
• To develop and validate a polygenic risk score (PRS) model for early prediction of severe schizophrenia outcomes.
• To explore biological pathways implicated in schizophrenia's severe progression.

Main Methods:

• Genome-wide and transcriptome-wide association studies (GWAS/TWAS) were performed on 4257 adults with schizophrenia.
• A cohort of 817 participants met criteria for disruptive schizophrenia (early onset, functional disability, severe negative symptoms, continuous course).
• Linear regression and PrediXcan algorithm were used to develop a PRS model, followed by internal and external validation.

Main Results:

• Significant genetic associations were identified with variants in CAMTA1, TRHDE, and NELFE genes.
• The developed PRS model demonstrated high predictive performance across training, internal, and external validation datasets (AUCs 0.9, 0.89, 0.68).
• Functional pathway analysis implicated ATP metabolism, myeloid cell differentiation, and apoptotic processes in severe schizophrenia.

Conclusions:

• Subphenotyping schizophrenia is essential for discovering specific genetic influences on disease development and progression.
• GWAS and TWAS findings highlight key mechanisms including synapse regulation, inflammation, and apoptosis in severe schizophrenia.
• The study provides a foundation for early risk prediction and targeted interventions for disruptive schizophrenia phenotypes.