Hundreds of genomic loci have been identified with the recent advances of schizophrenia in genome-wide association studies (GWAS) and sequencing studies. However, the functional interactions among those genes remain largely unknown. We developed a network-based approach to integrate multiple genetic risk factors, which lead to the discovery of new susceptibility genes and causal sub-networks, or pathways in schizophrenia. We identified significantly and consistently over-represented pathways in the largest schizophrenia GWA studies, which are highly relevant to synaptic plasticity, neural development and signaling transduction, such as long-term potentiation, neurotrophin signaling pathway, and the ERBB signaling pathway. We also demonstrated that genes targeted by common SNPs are more likely to interact with genes harboring de novo mutations (DNMs) in the protein-protein interaction (PPI) network, suggesting a mutual interplay of both common and rare variants in schizophrenia. We further developed an edge-based search algorithm to identify the top-ranked gene modules associated with schizophrenia risk. Our results suggest that the N-methyl-D-aspartate receptor (NMDAR) interactome may play a leading role in the pathology of schizophrenia, as it is highly targeted by multiple types of genetic risk factors.

Mental disorders present a global health concern, while the diagnosis of mental disorders can be challenging. The diagnosis is even harder for patients who have more than one type of mental disorder, especially for young toddlers who are not able to complete questionnaires or standardized rating scales for diagnosis. In the past decade, multiple genomic association signals have been reported for mental disorders, some of which present attractive drug targets. Concurrently, machine learning algorithms, especially deep learning algorithms, have been successful in the diagnosis and/or labeling of complex diseases, such as attention deficit hyperactivity disorder (ADHD) or cancer. In this study, we focused on eight common mental disorders, including ADHD, depression, anxiety, autism, intellectual disabilities, speech/language disorder, delays in developments, and oppositional defiant disorder in the ethnic minority of African Americans. Blood-derived whole genome sequencing data from 4179 individuals were generated, including 1384 patients with the diagnosis of at least one mental disorder. The burden of genomic variants in coding/non-coding regions was applied as feature vectors in the deep learning algorithm. Our model showed ~65% accuracy in differentiating patients from controls. Ability to label patients with multiple disorders was similarly successful, with a hamming loss score less than 0.3, while exact diagnostic matches are around 10%. Genes in genomic regions with the highest weights showed enrichment of biological pathways involved in immune responses, antigen/nucleic acid binding, chemokine signaling pathway, and G-protein receptor activities. A noticeable fact is that variants in non-coding regions (e.g., ncRNA, intronic, and intergenic) performed equally well as variants in coding regions; however, unlike coding region variants, variants in non-coding regions do not express genomic hotspots whereas they carry much more narrow standard deviations, indicating they probably serve as alternative markers.

Digestive and psychiatric disorders tend to co-occur, yet mechanisms remain unclear. Leveraging genetic and transcriptomic data integration, we conduct multi-trait analysis of GWAS (MTAG) and weighted gene co-expression network analysis (WGCNA) to explore shared mechanism between psychiatric and gastrointestinal disorders. Significant genetic correlations were found between these disorders, especially in irritable bowel syndrome (IBS), gastroesophageal reflux disease (GERD), depression (DEP), and neuroticism (NE). MTAG identify 60 novel pleiotropic loci for IBS and 14 for GERD, predominantly located near genes associated with neurological pathways. Further WGCNA identifies multiple co-expression modules enriched with genes involved in neurological pathways in digestive tissues, with some modules strongly preserved across brain and digestive tissues. Moreover, our network analysis suggests BSN, CELF4, and NRXN1 as central players in the regulation of the gut-brain axis (GBA). This study enhances our understanding of the GBA and underscores BSN, CELF4, and NRXN1 as crucial targets for future research.

Although mitochondrial dysfunction has been implicated in the pathophysiology of attention deficit and hyperactivity disorder ADHD, the role of mitochondrial DNA (mtDNA) has not been extensively investigated. To determine whether mtDNA haplogroups influence risk of ADHD, we performed a case-control study comprising 2076 ADHD cases and 5078 healthy controls, all of whom were European decedents recruited from The Children’s Hospital of Philadelphia (CHOP). Associations between eight major European mtDNA Haplogroups and ADHD risk were assessed in three independent European cohorts. Meta-analysis of the three studies indicated that mtDNA haplogroups K (odds ratio = 0.69, P = 2.24 × 10⁻⁴, P_corrected = 1.79 × 10⁻³) and U (odds ratio = 0.77, P = 8.88 × 10⁻⁴, P_corrected = 7.11 × 10⁻³) were significantly associated with reduced risk of ADHD. In contrast, haplogroup HHV* (odds ratio = 1.18, P = 2.32 × 10⁻³, P_corrected = 0.019) was significantly associated with increased risk of ADHD. Our results provide novel insight into the genetic basis of ADHD, implicating mitochondrial mechanisms in the pathophysiology of this relatively common psychiatric disorder.