芦澤 琢磨

Recent genome-wide association studies (GWASs) have identified fatty acid desaturase (FADS) genes, which code key enzymes involved in polyunsaturated fatty acid (PUFA) desaturation as susceptibility genes for bipolar disorder (BD). Several quantitative changes in PUFAs suggest their involvement in BD pathogenesis. Therefore, this study aimed to clarify the relationship between BD and PUFAs by conducting lipidomics covariating with the FADS gene variant (rs174550), which is associated with PUFA levels and BD susceptibility. The concentrations of 23 fatty acids were measured using plasma samples from the BD group (n = 535) and the control group (n = 107). Differences in each PUFA concentration ratio were compared between the two groups. Also, differences in each PUFA concentration ratio were compared for each genotype in rs174550. Our results showed that the BD group had significantly lower concentrations of linoleic acid (LA) (β = -0.36, p = 0.023) and arachidonic acid (AA) (β = -0.18, p = 0.013) than the control group. Concerning the effect of FADS on the PUFA concentration ratio, carriers of C-allele at rs174550 had significantly decreased γ-linolenic acid and AA concentration ratios. A previous GWAS reported that the presence of a C-allele at rs174550 increased the BD risk. This direction is consistent with the lipidomic results of the present study. In conclusion, both the FADS and BD were considered to regulate the AA concentration. Thus, as the FADS gene variant is crucial for conducting lipidomics of BD we believe that the allele frequency of FADS must be analyzed.

AIM: The genetic relationship between schizophrenia (SCZ) and other nonpsychiatric disorders remains largely unknown. We examined the shared genetic components between these disorders based on multipopulation data sets. METHODS: We used two data sets for East Asian (EAS) and European (EUR) samples. SCZ data was based on the Psychiatric Genomics Consortium Asia with our own genome-wide association study for EAS and Psychiatric Genomics Consortium for EUR. Nonpsychiatric data (20 binary traits [mainly nonpsychiatric complex disorders] and 34 quantitative traits [mainly laboratory examinations and physical characteristics]) were obtained from Biobank Japan and UK Biobank for EAS and EUR samples, respectively. To evaluate genetic correlation, linkage disequilibrium score regression analysis was utilized with further meta-analysis for each result from EAS and EUR samples to obtain robust evidence. Subsequent mendelian randomization analysis was also included to examine the causal effect. RESULTS: A significant genetic correlation between SCZ and several metabolic syndrome (MetS) traits was detected in the combined samples (meta-analysis between EAS and EUR data) (body mass index [rg  = -0.10, q-value = 1.0 × 10-9 ], high-density-lipoprotein cholesterol [rg  = 0.072, q-value = 2.9 × 10-3 ], blood sugar [rg  = -0.068, q-value = 1.4 × 10-2 ], triglycerides [rg  = -0.052, q-value = 2.4 × 10-2 ], systolic blood pressure [rg  = -0.054, q-value = 3.5 × 10-2 ], and C-reactive protein [rg  = -0.076, q-value = 7.8 × 10-5 ]. However, no causal relationship on SCZ susceptibility was detected for these traits based on the mendelian randomization analysis. CONCLUSION: Our results indicate shared genetic components between SCZ and MetS traits and C-reactive protein. Specifically, we found it interesting that the correlation between MetS traits and SCZ was the opposite of that expected from clinical studies: this genetic study suggests that SCZ susceptibility was associated with reduced MetS. This implied that MetS in patients with SCZ was not associated with genetic components but with environmental factors, including antipsychotics, lifestyle changes, poor diet, lack of exercise, and living conditions.