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Schizophrenia is a mental disorder constituting different symptom clusters. Its high heterogeneity in both pathophysiology and clinical manifestations hampered effective prevention and treatment. It has long been recognized that one of the core features of schizophrenia is its intellectual decline. Using the proxy-phenotype method (PPM), we tried to identify core genes, the expression of which in the dorsal lateral prefrontal cortex (DLPFC) showed a genetic dependence between intelligence (IT) and schizophrenia (SCZ). The result revealed ten genes of genetic dependence in their genetic expression in DLPFC between IT and schizophrenia. Further, a clustering analysis using the expression matrix of these ten genes identified four biotypes in our patient group. Subsequent phenotypic profiling of these four biotypes indicated a significant difference in working memory capacity, the gray matter volume (GMV) of five brain regions (lLimbicA_TempPole_2, rLimbicA_TempPole_2 rLimbicB_OFC_1, rContA_IPS_1 and rContB_PFClv_1), structural network and psychopathology. An in-vitro investigation of the biological functions of these core genes indicated their potentially critical role in neuronal growth, especially in dendritic spines. Our current study employed a novel statistical approach to identify the core genes associated with IT and explore the possibility of using the expression knowledge of these core genes to reduce the heterogeneity of schizophrenia. The results pinpointed one biotype that exhibited significant deficits in working memory, GMV in limbic and prefrontal areas, and also showed psychopathology of core negative symptom and worse outcomes.

The last decades of research in schizophrenia witnessed a shift of etiological speculation from neurotransmitters to inflammation. However, identifying definite inflammatory effectors of schizophrenia remains elusive due to confounding factors such as medication and metabolic status. To tackle this issue, we carried out omnigenic-based Mendelian randomization (MR) analysis to explore the inflammatory responses of schizophrenia and the brain morphological consequences caused by these SCZ-triggering inflammation responses. Our results identified seven SCZ-triggering inflammation markers, with P values surviving the Bonferroni multiple comparisons (B_NGF, P = 1.45 × 10−8; GROA (CXCL1) P = 1.15 × 10−4; IL8, P = 3.64 × 10−7; MCSF, P = 9.30 × 10−4; MCP3 (CCL7), P = 1.3 × 10−6; TNF_β, P = 3.63 × 10−4; CRP, P = 1.71 × 10−32). Further, three of them, GROA (CXCL1), IL8 and CRP, could lead to significant linear change rate of brain morphologies, especially white matter in both cerebral and cerebellum. Our study is the first to use an omnigenic conceptual framework to capture the immune pathology of schizophrenia. Although future studies adopting a different methodology are needed to validate our results, our study provides another piece of evidence that extensive and low-grade neuroinflammation exists in schizophrenia and that some of these inflammation markers could be potential targets for the precise diagnosis and treatment of schizophrenia.

Deciphering the relationships between genes and complex traits could help us better understand the biological mechanisms leading to phenotypic variations and disease onset. Univariate gene-based analyses are widely used to characterize gene-phenotype relationships, but are subject to the influence of confounders. Furthermore, while some genes directly contribute to traits variations, others may exert their effects through other genes. How to quantify individual genes’ direct and indirect effects on complex traits remains an important yet challenging question. We presented a novel framework (BN-GWAS) to decipher the total and direct causal effects of individual genes using imputed gene expression data from GWAS and raw gene expression from GTEx. The study was partially motivated by the quest to differentiate “core” genes (genes with direct causal effect on the phenotype) from “peripheral” ones. Our proposed framework is based on a Bayesian network (BN) approach, which produces a directed graph showing the relationship between genes and the phenotype. The approach aims to uncover the overall causal structure, to examine the role of individual genes and quantify the direct and indirect effects by each gene. An important advantage and novelty of the proposed framework is that it allows gene expression and disease trait(s) to be evaluated in different samples, significantly improving the flexibility and applicability of the approach. It uses IDA and jointIDA incorporating a novel p-value-based regularization approach to quantify the causal effects (including total causal effects, direct causal effects, and medication effects) of genes. The proposed approach can be extended to decipher the joint causal network of 2 or more traits, and has high specificity and precision (a.k.a., positive predictive value), making it particularly useful for selecting genes for follow-up studies. We verified the feasibility and validity of the proposed framework by extensive simulations and applications to 52 traits across 14 tissues in the UK Biobank (UKBB). Split-half replication and stability selection analyses were performed to demonstrate the accuracy and efficiency of our proposed method to identify causally relevant genes. The identified (direct) causal genes were found to be significantly enriched for genes highlighted in the OpenTargets database, and the enrichment was stronger than achieved by conventional univariate gene-based tests. Encouragingly, many enriched pathways were supported by the literature, and some of the enriched drugs have been tested or used to treat patients in clinical practice. Our proposed framework provides a powerful way to prioritize genes with large direct or indirect causal effects and to estimate the ‘variable importance’ of such genes.

Studies conducted in psychotic disorders have shown that DNA-methylation (DNAm) is sensitive to the impact of Childhood Adversity (CA). However, whether it mediates the association between CA and psychosis is yet to be explored. Epigenome-wide association studies (EWAS) using the Illumina Infinium-Methylation EPIC array in peripheral blood tissue from 366 First-episode of psychosis and 517 healthy controls was performed. Polyvictimization scores were created for abuse, neglect, and composite adversity with the Childhood Trauma Questionnaire (CTQ). Regressions examining (I) CTQ scores with psychosis; (II) with DNAm EWAS level and (III), then between DNAm and caseness, adjusted for a variety of confounders were conducted. Divide-Aggregate Composite-null Test for the composite null hypothesis of no mediation effect was conducted. Enrichment analyses were conducted with missMethyl package and the KEGG database. Our results show that CA was associated with psychosis (Composite: OR = 3.09; p =

Despite many methodological studies on the global heritability estimation of complex traits, very few methods have been designed to dissect local heritability accurately. A precise local heritability would provide a high-resolution map for precision medicine. We report an effective heritability estimator (EHE) for local heritability estimation using p values from genome-wide association studies (GWAS). With a directly converting marginal heritability to the effective one, EHE shows higher accuracy and is the only method correctly calculating standard errors of local heritability estimation among the four compared methods. Importantly, EHE can be directly used to estimate the conditional heritability of nearby regions or genes in which the redundant heritability can be cleaned. The conditional heritability estimation procedure can be supervised by tissue-specific expression profiles to prioritize and quantify functionally more important genes of complex phenotypes. Applying EHE to 42 complex traits from the UK biobank, we found that heritability density in lncRNA genes tends to be higher than protein-coding genes, and genes pleiotropic to more phenotypes are not enriched for more heritability among candidate susceptibility genes. EHE provides an efficient way to finely and precisely dissect the genetic architecture of complex phenotypes.

Hirschsprung disease (HSCR) is characterized by the absence of enteric neurons caused by the defects of enteric neural crest cells (ENCCs), leading to intestinal obstruction. Here, using iPSC-based models of HSCR and single-cell transcriptomic analysis, we identified a core gene set of 118 genes commonly dysregulated in all HSCR-ENCCs, with HDAC1 found to be a master regulator of these genes. Furthermore, upregulation of RNA splicing mediators and enhanced alternative splicing events were associated with severe form of HSCR. In particular, the higher inclusion rate of exon 9 in PTBP1 and the perturbed expression of a PTBP1-target, PKM, were significantly enriched in these HSCR-ENCCs, and associated with the defective oxidative-phosphorylation (OXPHOS) and impaired neurogenesis. Hedgehog-induced OXPHOS significantly enhanced the survival and differentiation capacity of HSCR-ENCCs. In sum, we have defined the core factors underpinning HSCR disease and demonstrated the implications of OXPHOS in ENCC development and HSCR pathogenesis.