Gene-based interaction analysis shows GABAergic genes interacting with parenting in adolescent depressive symptoms.

BACKGROUND: Most gene-environment interaction studies (G × E) have focused on single candidate genes. This approach is criticized for its expectations of large effect sizes and occurrence of spurious results. We describe an approach that accounts for the polygenic nature of most psychiatric phenotypes and reduces the risk of false-positive findings. We apply this method focusing on the role of perceived parental support, psychological control, and harsh punishment in depressive symptoms in adolescence. METHODS: Analyses were conducted on 982 adolescents of Caucasian origin (Mage (SD) = 13.78 (.94) years) genotyped for 4,947 SNPs in 263 genes, selected based on a literature survey. The Leuven Adolescent Perceived Parenting Scale (LAPPS) and the Parental Behavior Scale (PBS) were used to assess perceived parental psychological control, harsh punishment, and support. The Center for Epidemiologic Studies Depression Scale (CES-D) was the outcome. We used gene-based testing taking into account linkage disequilibrium to identify genes containing SNPs exhibiting an interaction with environmental factors yielding a p-value per single gene. Significant results at the corrected p-value of p < 1.90 × 10-4 were examined in an independent replication sample of Dutch adolescents (N = 1354). RESULTS: Two genes showed evidence for interaction with perceived support: GABRR1 (p = 4.62 × 10-5 ) and GABRR2 (p = 9.05 × 10-6 ). No genes interacted significantly with psychological control or harsh punishment. Gene-based analysis was unable to confirm the interaction of GABRR1 or GABRR2 with support in the replication sample. However, for GABRR2, but not GABRR1, the correlation of the estimates between the two datasets was significant (r (46) = .32; p = .027) and a gene-based analysis of the combined datasets supported GABRR2 × support interaction (p = 1.63 × 10-4 ). CONCLUSIONS: We present a gene-based method for gene-environment interactions in a polygenic context and show that genes interact differently with particular aspects of parenting. This accentuates the importance of polygenic approaches and the need to accurately assess environmental exposure in G × E.

Genetic Evaluation of Schizophrenia Using the Illumina HumanExome Chip.

INTRODUCTION: Schizophrenia is a genetically heterogeneous disorder that is associated with several common and rare genetic variants. As technology involved, cost advantages of chip based genotyping was combined with information about rare variants, resulting in the Infinium HumanExome Beadchip. Using this chip, a sample of 493 patients with schizophrenia or schizoaffective disorder and 484 healthy controls was genotyped. RESULTS: From the initial 242901 SNVs, 88306 had at least one minor allele and passed quality control. No variant reached genomewide-significant results (p<10(-8)). The SNP with the lowest p-value was rs1230345 in WISP3 (p = 3.05*10(-6)), followed by rs9311525 in CACNA2D3 (p = 1.03*10(-5)) and rs1558557 (p = 3.85*10(-05)) on chromosome 7. At the gene level, 3 genes were of interest: WISP3, on chromosome 6q21, a signally protein from the extracellular matrix. A second candidate gene is CACNA2D3, a regulator of the intracerebral calcium pathway. A third gene is TNFSF10, associated with p53 mediated apoptosis.

Single-subject classification of schizophrenia patients based on a combination of oddball and mismatch evoked potential paradigms.

OBJECTIVE: The diagnostic process for schizophrenia is mainly clinical and has to be performed by an experienced psychiatrist, relying primarily on clinical signs and symptoms. Current neurophysiological measurements can distinguish groups of healthy controls and groups of schizophrenia patients. Individual classification based on neurophysiological measurements mostly shows moderate accuracy. We wanted to examine whether it is possible to distinguish controls and patients individually with a good accuracy. To this end we used a combination of features extracted from the auditory and visual P300 paradigms and the mismatch negativity paradigm. METHODS: We selected 54 patients and 54 controls, matched for age and gender, from the data available at the UPC Kortenberg. The EEG-data were high- and low-pass filtered, epoched and averaged. Features (latencies and amplitudes of component peaks) were extracted from the averaged signals. The resulting dataset was used to train and test classification algorithms. First on separate paradigms and then on all combinations, we applied Naïve Bayes, Support Vector Machine and Decision Tree, with two of its improvements: Adaboost and Random Forest. RESULTS: For at least two classifiers the performance increased significantly by combining paradigms compared to single paradigms. The classification accuracy increased from at best 79.8% when trained on features from single paradigms, to 84.7% when trained on features from all three paradigms. CONCLUSION: A combination of features originating from three evoked potential paradigms allowed us to accurately classify individual subjects as either control or patient. Classification accuracy was mostly above 80% for the machine learners evaluated in this study and close to 85% at best.

No association between genetic or epigenetic variation in insulin growth factors and antipsychotic-induced metabolic disturbances in a cross-sectional sample.

AIM: Second-generation antipsychotics (SGA) are known to induce metabolic disturbances. Genetic pathways, such as the IGF pathway could be associated with increased metabolic syndrome (MetS). Additionally, IGF2 methylation varies as a function of environmental influences and is associated with schizophrenia and MetS. The current study aims to evaluate whether genetic and epigenetic variation in genes of the IGF pathway are associated with metabolic disturbances in patients under treatment with SGAs. METHODS: Cross-sectional metabolic data from 438 patients with schizophrenia spectrum disorder was analyzed. Using the Sequenom MassARRAY iPLEX(TM) platform, 27 SNPs of the IGF1 and IGF2 genes and the IGF receptors IGF1R and IGF2R were genotyped. Methylation status of seven IGF2 CpG dinucleotides was evaluated using a Sequenom MALDI-TOF spectrometer. RESULTS & CONCLUSION: There was a significant association between IGF2 methylation and genotype, but no significant association between genetic or epigenetic variability and metabolic parameters in the present study.

CNR1 gene and risk of the metabolic syndrome in patients with schizophrenia.

Metabolic disturbances are more prevalent in patients with schizophrenia (SCZ) than in the general population. The endocannabinoid system plays an important role in the regulation of dopamine transmission and several metabolic pathways, and the endocannabinoid receptor type 1 gene (CNR1) is considered a candidate gene for both SCZ and metabolic disorders. We examined whether genetic variation in CNR1 was associated with metabolic syndrome (MetS) in a naturalistic cohort of 407 patients with SCZ. The minor alleles of rs6928499, rs1535255, and rs2023239 were nominally associated with a lower risk of MetS [odds ratio (OR), 0.56; 95% confidence interval (CI), 0.37-0.84; P = 0.006; OR, 0.56; 95% CI, 0.37-0.84; P = 0.006; and OR, 0.44; 95% CI, 0.27-0.72; P = 0.001, respectively, adjusted for age, sex, duration of illness, clozapine or olanzapine treatment). These differences were mainly due to differences in high-density lipoprotein cholesterol and fasting glucose but not in body mass index or waist circumference. No significant association of the other polymorphisms (rs806377, rs1049353, rs6454674, and rs806379) with MetS was found. These results provide evidence that the prevalence of MetS is associated with the CNR1 gene in patients with SCZ during long-term treatment with antipsychotic treatment. Further studies are needed to uncover the exact molecular basis for this association, which could provide novel treatment targets for the MetS.

Relationship between P-glycoprotein and second-generation antipsychotics.

The membrane transport protein P-glycoprotein (P-gp) is an interesting candidate for individual differences in response to antipsychotics. To present an overview of the current knowledge of P-gp and its interaction with second-generation antipsychotics (SGAs), an internet search for all relevant English original research articles concerning P-gp and SGAs was conducted. Several SGAs are substrates for P-gp in therapeutic concentrations. These include amisulpride, aripiprazole, olanzapine, perospirone, risperidone and paliperidone. Clozapine and quetiapine are not likely to be substrates of P-gp. However, most antipsychotics act as inhibitors of P-gp, and can therefore influence plasma and brain concentrations of other substrates. No information was available for sertindole, ziprasidone or zotepine. Research in animal models demonstrated significant differences in antipsychotic brain concentration and behavior owing to both P-gp knockout and inhibition. Results in patients are less clear, as several external factors have to be accounted for. Patients with polymorphisms which decrease P-gp functionality tend to perform better in clinical settings. There is some variability in the findings concerning adverse effects, and no definitive conclusions can be drawn at this point.

Clock genes and body composition in patients with schizophrenia under treatment with antipsychotic drugs.

CONTEXT: In the healthy population, several pathways are known to exert an effect on basal metabolic factors. Previous studies have found associations between single nucleotide polymorphisms in clock genes or downstream hormone receptors such as the leptin receptor (LEPR) or glucocorticoid receptor (NR3C1) and obesity in the healthy population, but this association remains to be examined in patients with schizophrenia treated with antipsychotics. OBJECTIVE: To assess anthropomorphic parameters in patients taking second-generation antipsychotics (SGA) as a function of nine polymorphisms in three core genes of the clock pathway, and two genes of downstream hormone receptors. METHODS: Clinical parameters were evaluated in 261 patients with schizophrenia spectrum disorder. Polymorphisms in LEPR, MC3R, NR3C1, PER2 and SDC3 were genotyped. In order to control for multiple testing, permutation tests were used to generate corrected empirical p-values using the Max(T) procedure in PLINK. RESULTS: A significant effect of the rs6196 polymorphism in the NR3C1 on weight (ß=-4.18; SE=2.02; p=0.018), BMI (ß=-1.88; SE=0.64; p=0.004), waist (ß=-5.77; SE=1.75; p=0.001) and waist/hip ratio (ß=-0.03; SE=0.012; p=0.009) was found. Permutation tests confirmed the findings for BMI (p=0.037) and waist (p=0.024). Carriers of the G allele consistently displayed better parameters than patients with the wild type allele. A weak effect of rs4949184 in SDC3 on BMI was found, but this did not sustain permutation testing (ß=-1.27; SE=0.58; p=0.030, p=0.270 after permutations). CONCLUSION: Variations in genes implicated in circadian regulation or its related downstream pathways may be important in the regulation of antropomorphic parameters in patients with schizophrenia during long-term treatment with SGA.

MTHFR genotype and differential evolution of metabolic parameters after initiation of a second generation antipsychotic: an observational study.

Most second-generation antipsychotics (SGAs) induce metabolic disturbances, but large differences exist in the degree to which individual patients develop these. Little is known about genetic factors associated with differential liability. Cross-sectional studies suggested an association between polymorphisms in 5,10-methylenetetraydrofolate reductase (MTHFR) and metabolic syndrome in patients with schizophrenia. This study aimed to assess whether the C677T (rs1801133) or A1298C (rs1801131) polymorphism in the MTHFR gene predict differential evolution of metabolic parameters over the course of a 3-month follow-up period after initiation of an SGA. One hundred and four patients with schizophrenia initiated on a SGA were measured at baseline, 6 weeks and 3 months. MTHFR A1298C, but not C677T, genotype predicted pos-baseline increases in weight [beta=2.5, standard error (SE)=0.92, P=0.006], waist circumference (beta=2.0, SE=1.0, P=0.050), fasting glucose (beta=2.8, SE=1.2, P=0.024) and glucose at 120 min during the Oral Glucose Tolerance Test (beta=10.7, SE=4.5, P=0.018) following a de novo metabolic challenge with a specific SGA. A1298C allele carriers consistently displayed the most unfavorable evolution of metabolic parameters. Thus, MTHFR A1298C genotype may explain part of the individual liability to metabolic disturbances in patients with schizophrenia.