Antipsychotic medications are the gold-standard treatment for schizophrenia, and are often prescribed for other mental conditions. However, the efficacy and side-effect profiles of these drugs are heterogeneous, with large interindividual variability. As a result, treatment selection remains a largely trial-and-error process, with many failed treatment regimens endured before finding a tolerable balance between symptom management and side effects. Much of the interindividual variability in response and side effects is due to genetic factors (heritability, h2~ 0.60-0.80). Pharmacogenetics is an emerging field that holds the potential to facilitate the selection of the best medication for a particular patient, based on his or her genetic information. In this review we discuss the most promising genetic markers of antipsychotic treatment outcomes, and present current translational research efforts that aim to bring these pharmacogenetic findings to the clinic in the near future.
Structural variation in the neurexin-1 (NRXN1) gene increases risk for both autism spectrum disorders (ASD) and schizophrenia. However, the manner in which NRXN1 gene variation may be related to brain morphology to confer risk for ASD or schizophrenia is unknown.53 healthy individuals between 18-59 years of age were genotyped at 11 single nucleotide polymorphisms of the NRXN1 gene. All subjects received structural MRI scans, which were processed to determine cortical gray and white matter lobar volumes, and volumes of striatal and thalamic structures. Each subject's sensorimotor function was also assessed. The general linear model was used to calculate the influence of genetic variation on neural and cognitive phenotypes. Finally, in silico analysis was conducted to assess potential functional relevance of any polymorphisms associated with brain measures. A polymorphism located in the 3' untranslated region of NRXN1 significantly influenced white matter volumes in whole brain and frontal lobes after correcting for total brain volume, age and multiple comparisons. Follow-up in silico analysis revealed that this SNP is a putative microRNA binding site that may be of functional significance in regulating NRXN1 expression. This variant also influenced sensorimotor performance, a neurocognitive function impaired in both ASD and schizophrenia.Our findings demonstrate that the NRXN1 gene, a vulnerability gene for SCZ and ASD, influences brain structure and cognitive function susceptible in both disorders. In conjunction with our in silico results, our findings provide evidence for a neural and cognitive susceptibility mechanism by which the NRXN1 gene confers risk for both schizophrenia and ASD.
The Ontario Brain Institute's “Brain-CODE” is a large-scale informatics platform designed to support the collection, storage and integration of diverse types of data across several brain disorders as a means to understand underlying causes of brain dysfunction and developing novel approaches to treatment. By providing access to aggregated datasets on participants with and without different brain disorders, Brain-CODE will facilitate analyses both within and across diseases and cover multiple brain disorders and a wide array of data, including clinical, neuroimaging, and molecular. To help achieve these goals, consensus methodology was used to identify a set of core demographic and clinical variables that should be routinely collected across all participating programs. Establishment of Common Data Elements within Brain-CODE is critical to enable a high degree of consistency in data collection across studies and thus optimize the ability of investigators to analyze pooled participant-level data within and across brain disorders. Results are also presented using selected common data elements pooled across three studies to better understand psychiatric comorbidity in neurological disease (Alzheimer's disease/amnesic mild cognitive impairment, amyotrophic lateral sclerosis, cerebrovascular disease, frontotemporal dementia, and Parkinson's disease).
Atypical antipsychotics (AP) have high affinity for many neurotransmitter receptors. Among these receptors, APs are antagonist at α-adrenergic and β-adrenergic receptors, and this pharmacological property has been postulated to be involved in the mechanism of action of these drugs with respect to both clinical response and adverse effects.We tested the hypotheses that AP-induced weight gain is associated with genetic variation in adrenergic receptors and pathway enzymes. We analyzed nine genetic polymorphisms across seven adrenergic genes (ADRA1A, ADRA2A, ADRA2C, ADRB3, DBH, MAOA and COMT).One hundred thirty-nine patients with schizophrenia were prospectively assessed for AP-induced weight gain. The HelixTree software (Golden Helix, Bozeman, MT, USA) was employed to detect differences in genotypic distribution between weight gainer and non-weight gainer groups. Furthermore, for the dopamine β-hydroxylase haplotype, we were able to obtain both the molecular and the statistical phases, analyzing the phenotype considering both phases.Weight gain was not associated with any adrenergic gene.Our results suggest that genetic polymorphisms in the adrenergic system may not play a major role in AP-induced weight gain; however, adrenergic 2A receptor gene that produced previously the most consistent associations with this phenotype showed a significant interaction with the monoamine oxidase A in weight gainers.
