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.

Assessment of first and second degree relatives of individuals with bipolar disorder shows increased genetic risk scores in both affected relatives and young At‐Risk Individuals

“…Examination of the cumulative effects of inheriting multiple risk alleles—each of which are significantly or nominally associated with disease risk—has been able to powerfully differentiate groups of cases from controls in independent population‐based studies [Purcell et al, 2014, 2009; Patel et al, 2010; Ayalew et al, 2012; Terwisscha van Scheltinga et al, 2012]. However, despite phenotypic aggregation within families [McGuffin et al, 2003; Lichtenstein et al, 2009], no studies have so far examined polygenic risk incorporating these common genetic factors in a family context in adults, with only one group to date reporting on polygenic risk in adolescent offspring of individuals with BP [Whalley et al, 2012, 2013].…”

Variation in Dopamine D2 and Serotonin 5-HT2A Receptor Genes is Associated with Working Memory Processing and Response to Treatment with Antipsychotics

“…The gene coding for 5-HT2A (HTR2A, 13q14-21) has been weakly associated with schizophrenia (Ayalew et al, 2012;Collins et al, 2012;Sanders et al, 2008). Other investigations have linked genetic variation within HTR2A to response to antipsychotics (Arranz et al, 1998;Chen et al, 2009;Masellis et al, 1998;Olajossy-Hilkesberger et al, 2011).…”

Genetic studies of schizophrenia: an update

“…Using a convergent functional genomics approach, which utilizes poly-evidence scoring and pathway analyses, Ayalew et al identified several genes involved in SCZ, including DISC1 and TCF4 [32] . By combining gene expression profi ling and GWAS data, Luo et al showed that CAMKK2 is differentially expressed in SCZ and controls, and a marker in the gene, rs1063843, is associated with the expression and diagnosis of SCZ in a large GWAS dataset [33] .…”