Recent genome-wide association studies (GWASs) of schizophrenia (SCZ) identified several susceptibility genes and suggested shared genetic components between SCZ and bipolar disorder (BD). We conducted a genetic association study of single nucleotide polymorphisms (SNPs) selected according to previous SCZ GWAS targeting psychotic disorders (SCZ and BD) in the Japanese population. Fifty-one SNPs were analyzed in a two-stage design using first-set screening samples (all SNPs: 1,032 SCZ, 1,012 BD, and 993 controls) and second-set replication samples ("significant" SNPs in the first-set screening analysis: 1,808 SCZ, 821 BD, and 2,321 controls). We assessed allelic associations between the selected SNPs and the three phenotypes (SCZ, BD, and "psychosis" [SCZ + BD]). Nine SNPs revealed nominal association signals for all comparisons (P(uncorrected) < 0.05), of which two SNPs located in the major histocompatibility complex region (rs7759855 in zinc finger and SCAN domain containing 31 [ZSCAN31] and rs1736913 in HLA-F antisense RNA1 [HLA-F-AS1]) were further assessed in the second-set replication samples. The associations were confirmed for rs7759855 (P(corrected) = 0.026 for psychosis; P(corrected) = 0.032 for SCZ), although the direction of effect was opposite to that in the original GWAS of the Chinese population. Finally, a meta-analysis was conducted using our two samples and using our data and data from Psychiatric GWAS Consortium (PGC), which have shown the same direction of effect. SNP in ZSCAN31 (rs7759855) had the strongest association with the phenotypes (best P = 6.8 × 10(-5) for psychosis: present plus PGC results). These data support shared risk SNPs between SCZ and BD in the Japanese population and association between MHC and psychosis.

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Genome-wide association studies (GWASs) have identified a number of susceptibility genes for schizophrenia (SCZ) and bipolar disorder (BD). However, the identification of risk genes for major depressive disorder (MDD) has been unsuccessful because the etiology of MDD is more influenced by environmental factors; thus, gene–environment (G×E) interactions are important, such as interplay with stressful life events (SLEs). We assessed the G×E interactions and main effects of genes targeting depressive symptoms. Using a case–control design, 922 hospital staff members were evaluated for depressive symptoms according to Beck Depressive Inventory (BDI; “depression” and “control” groups were classified by scores of 10 in the BDI test), SLEs, and personality. A total of sixty-three genetic variants were selected on the basis of previous GWASs of MDD, SCZ, and BD as well as candidate-gene (SLC6A4, BDNF, DBH, and FKBP5) studies. Logistic regression analysis revealed a marginally significant interaction (genetic variant × SLE) at rs4523957 (Puncorrected = 0.0034) with depression and a significant association of single nucleotide polymorphism identified from evidence of BD GWAS (rs7296288, downstream of DHH at 12q13.1) with depression as the main effect (Puncorrected = 9.4×10−4, Pcorrected = 0.0424). We also found that SLEs had a larger impact on depression (odds ratio∼3), as reported previously. These results suggest that DHH plays a possible role in depression etiology; however, variants from MDD or SCZ GWAS evidence or candidate genes showed no significant associations or minimal effects of interactions with SLEs on depression.

Aims:The purpose of this study was to predict the outcome of cognitive behavior therapy (CBT) by trainees for major depressive disorder (MDD) based on the Parental Bonding Instrument (PBI). The hypothesis was that the higher level of care and/or lower level of overprotection score would predict a favorable outcome of CBT by trainees. Methods:The subjects were all outpatients with MDD treated with CBT as a training case. All the subjects were asked to fill out the Japanese version of the PBI before commencing the course of psychotherapy. The difference between the first and the last Beck Depression Inventory (BDI) score was used to represent the improvement of the intensity of depression by CBT. In order to predict improvement (the difference of the BDI scores) as the objective variable, multiple regression analysis was performed using maternal overprotection score and baseline BDI score as the explanatory variables.Results: The multiple regression model was significant (P = 0.0026) and partial regression coefficient for the maternal overprotection score and the baseline BDI was -0.73 (P = 0.0046) and 0.88 (P = 0.0092), respectively. Therefore, when a patient's maternal overprotection score of the PBI was lower, a better outcome of CBT was expected. Conclusion:The hypothesis was partially supported. This result would be useful in determining indications for CBT by trainees for patients with MDD.

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