A genome-wide association study identifies a gene network associated with paranoid schizophrenia and antipsychotics-induced tardive dyskinesia.

In the present study we conducted a genome-wide association study (GWAS) in a cohort of 505 patients with paranoid schizophrenia (SCZ), of which 95 had tardive dyskinesia (TD), and 503 healthy controls. Using data generated by the PsychENCODE Consortium (PEC) and other bioinformatic databases, we revealed a gene network, implicated in neurodevelopment and brain function, associated with both these disorders. Almost all these genes are in gene or isoform co-expression PEC network modules important for the functioning of the brain; the activity of these networks is also altered in SCZ, bipolar disorder and autism spectrum disorders. The associated PEC network modules are enriched for gene ontology terms relevant to the brain development and function (CNS development, neuron development, axon ensheathment, synapse, synaptic vesicle cycle, and signaling receptor activity) and to the immune system (inflammatory response). Results of the present study suggest that orofacial and limbtruncal types of TD seem to share the molecular network with SCZ. Paranoid SCZ and abnormal involuntary movements that indicate the orofacial type of TD are associated with the same genomic loci on chromosomes 3p22.2, 8q21.13, and 13q14.2. The limbtruncal type of TD is associated with a locus on chromosome 3p13 where the best functional candidate is FOXP1, a high-confidence SCZ gene. The results of this study shed light on common pathogenic mechanisms for SCZ and TD, and indicate that the pathogenesis of the orofacial and limbtruncal types of TD might be driven by interacting genes implicated in neurodevelopment.

Current challenges and possible future developments in personalized psychiatry with an emphasis on psychotic disorders.

A personalized medicine approach seems to be particularly applicable to psychiatry. Indeed, considering mental illness as deregulation, unique to each patient, of molecular pathways, governing the development and functioning of the brain, seems to be the most justified way to understand and treat disorders of this medical category. In order to extract correct information about the implicated molecular pathways, data can be drawn from sampling phenotypic and genetic biomarkers and then analyzed by a machine learning algorithm. This review describes current difficulties in the field of personalized psychiatry and gives several examples of possibly actionable biomarkers of psychotic and other psychiatric disorders, including several examples of genetic studies relevant to personalized psychiatry. Most of these biomarkers are not yet ready to be introduced in clinical practice. In a next step, a perspective on the path personalized psychiatry may take in the future is given, paying particular attention to machine learning algorithms that can be used with the goal of handling multidimensional datasets.

NRG1, PIP4K2A, and HTR2C as Potential Candidate Biomarker Genes for Several Clinical Subphenotypes of Depression and Bipolar Disorder.

GSK3B, BDNF, NGF, NRG1, HTR2C, and PIP4K2A play important roles in molecular mechanisms of psychiatric disorders. GSK3B occupies a central position in these molecular mechanisms and is also modulated by psychotropic drugs. BDNF regulates a number of key aspects in neurodevelopment and synaptic plasticity. NGF exerts a trophic action and is implicated in cerebral alterations associated with psychiatric disorders. NRG1 is active in neural development, synaptic plasticity, and neurotransmission. HTR2C is another important psychopharmacological target. PIP4K2A catalyzes the phosphorylation of PI5P to form PIP2, the latter being implicated in various aspects of neuronal signal transduction. In the present study, the six genes were sequenced in a cohort of 19 patients with bipolar affective disorder, 41 patients with recurrent depressive disorder, and 55 patients with depressive episode. The study revealed a number of genetic variants associated with antidepressant treatment response, time to recurrence of episodes, and depression severity. Namely, alleles of rs35641374 and rs10508649 (NRG1 and PIP4K2A) may be prognostic biomarkers of time to recurrence of depressive and manic/mixed episodes among patients with bipolar affective disorder. Alleles of NC_000008.11:g.32614509_32614510del, rs61731109, and rs10508649 (also NRG1 and PIP4K2A) seem to be predictive biomarkers of response to pharmacological antidepressant treatment on the 28th day assessed by the HDRS-17 or CGI-I scale. In particular, the allele G of rs10508649 (PIP4K2A) may increase resistance to antidepressant treatment and be at the same time protective against recurrent manic/mixed episodes. These results support previous data indicating a biological link between resistance to antidepressant treatment and mania. Bioinformatic functional annotation of associated variants revealed possible impact for transcriptional regulation of PIP4K2A. In addition, the allele A of rs2248440 (HTR2C) may be a prognostic biomarker of depression severity. This allele decreases expression of the neighboring immune system gene IL13RA2 in the putamen according to the GTEx portal. The variant rs2248440 is near rs6318 (previously associated with depression and effects of psychotropic drugs) that is an eQTL for the same gene and tissue. Finally, the study points to several protein interactions relevant in the pathogenesis of mood disorders. Functional studies using cellular or animal models are warranted to support these results.