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

Abstract

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.

Figure 1

Convergent functional genomics. GWAS, genome-wide association study; ISC, International Schizophrenia Consortium; SNP, single-nucleotide polymorphism.

Figure 2

Top candidate genes for schizophrenia. CFG, convergent functional genomics; GWAS, genome-wide association study; ISC, International Schizophrenia Consortium.

Figure 3

Schizophrenia as a disease of disconnection. (a) Biology of schizophrenia, (b) gene–environment interplay.

Figure 4

Genetic risk prediction of schizophrenia in four independent cohorts. AA, African American; EA, European American; GRPS, genetic risk prediction score.

Figure 5

Genetic risk score and age at onset of schizophrenia. AA, African American; AAO, age at onset; EA, European American; GRPS, genetic risk prediction score.

Figure 6

Prototype of how genetic risk prediction score (GRPS) testing could be used at an individual rather than population level, to aid diagnostic and personalized medicine approaches. We used the average values and standard deviation values for GRPS from the GAIN samples from each ethnicity (European American (EA) and African American (AA)) as thresholds for predictive testing in the independent nonGAIN EA and nonGAIN AA cohorts. The average GRPS score for schizophrenics in the GAIN cohort is used as a cut-off for schizophrenics in the test cohort (that is, being above that threshold), and the average GRPS score for controls in the GAIN cohort is used as a cut-off for controls in the test nonGAIN cohort (that is, being below that threshold). The subjects who are in between these two thresholds are called undetermined. Furthermore, to stratify risk, we categorized subjects into risk categories (in red, increased risk; in blue, decreased risk): Category 1 if they fall within one standard deviation above the schizophrenics' threshold, and category −1 if they fall within one standard deviation below the controls threshold. Category 2 and −2, subjects are between one and two standard deviations from the thresholds, category 3 and −3, subjects are between two and three standard deviations, and category 4 and −4, subjects are those who fall beyond three standard deviations of the thresholds. The positive predictive value (PPV) of the tests increases in the higher categories, and the test is somewhat better at distinguishing controls in EA (that is, in a practical application, individuals that are lower risk of developing the illness), and schizophrenics in AA (that is, in a practical application, individuals that are higher risk of developing the illness).

Figure 7

Overlap between independent genome-wide association study (GWAS). AA, African American; EA, European American; CFG, convergent functional genomics; ISC, International Schizophrenia Consortium; SNP, single-nucleotide polymorphism.

Figure 8

Genetic overlap among psychiatric disorders.