Convergent functional genomics of schizophrenia: from comprehensive understanding to genetic risk prediction

• Published in: Molecular psychiatry Vol. 17; no. 9; pp. 887 - 905
• Main Authors: Ayalew, M, Le-Niculescu, H, Levey, D F, Jain, N, Changala, B, Patel, S D, Winiger, E, Breier, A, Shekhar, A, Amdur, R, Koller, D, Nurnberger, J I, Corvin, A, Geyer, M, Tsuang, M T, Salomon, D, Schork, N J, Fanous, A H, O'Donovan, M C, Niculescu, A B
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.09.2012; Nature Publishing Group
• Subjects: 631/208/191; 631/208/205/2138; 692/699/476/1799; Adult and adolescent clinical studies; Animals; Behavioral Sciences; Biological and medical sciences; Biological Psychology; Biomarkers; Bipolar disorder; Brain research; Case-Control Studies; Consortia; Databases, Genetic - statistics & numerical data; Disease Models, Animal; Gene expression; Genes; Genetic aspects; Genetic Association Studies - statistics & numerical data; Genetic Predisposition to Disease - genetics; Genomes; Genomics; Genomics - methods; Genomics - statistics & numerical data; Humans; Illnesses; Immediate Communication; Medical sciences; Medicine; Medicine & Public Health; Mental Disorders - genetics; Neurosciences; Pharmacotherapy; Physiological aspects; Polymorphism, Single Nucleotide - genetics; Psychiatry; Psychology. Psychoanalysis. Psychiatry; Psychopathology. Psychiatry; Psychoses; Reelin Protein; Reproducibility; Risk factors; Schizophrenia; Schizophrenia - diagnosis; Schizophrenia - genetics; United States; La Jolla California; Indiana; United States > US; Indianapolis Indiana; convergent functional genomics; pathways; schizophrenia; biomarkers; genetic risk prediction; Psychosis; Genomics; Prediction; Biological marker; Schizophrenia; Genetics; Comprehension; Genetic determinism
• ISSN: 1359-4184; 1476-5578
• DOI: 10.1038/mp.2012.37

We have used a translational convergent functional genomics (CFG) approach to identify and prioritize genes involved in schizophrenia, by gene-level integration of genome-wide association study data with other genetic and gene expression studies in humans and animal models. Using this polyevidence scoring and pathway analyses, we identify top genes (DISC1, TCF4, MBP, MOBP, NCAM1, NRCAM, NDUFV2, RAB18, as well as ADCYAP1, BDNF, CNR1, COMT, DRD2, DTNBP1, GAD1, GRIA1, GRIN2B, HTR2A, NRG1, RELN, SNAP-25, TNIK), brain development, myelination, cell adhesion, glutamate receptor signaling, G-protein–coupled receptor signaling and cAMP-mediated signaling as key to pathophysiology and as targets for therapeutic intervention. Overall, the data are consistent with a model of disrupted connectivity in schizophrenia, resulting from the effects of neurodevelopmental environmental stress on a background of genetic vulnerability. In addition, we show how the top candidate genes identified by CFG can be used to generate a genetic risk prediction score (GRPS) to aid schizophrenia diagnostics, with predictive ability in independent cohorts. The GRPS also differentiates classic age of onset schizophrenia from early onset and late-onset disease. We also show, in three independent cohorts, two European American and one African American, increasing overlap, reproducibility and consistency of findings from single-nucleotide polymorphisms to genes, then genes prioritized by CFG, and ultimately at the level of biological pathways and mechanisms. Finally, we compared our top candidate genes for schizophrenia from this analysis with top candidate genes for bipolar disorder and anxiety disorders from previous CFG analyses conducted by us, as well as findings from the fields of autism and Alzheimer. Overall, our work maps the genomic and biological landscape for schizophrenia, providing leads towards a better understanding of illness, diagnostics and therapeutics. It also reveals the significant genetic overlap with other major psychiatric disorder domains, suggesting the need for improved nosology.