We conducted a cross-trait meta-analysis of genome-wide association study on schizophrenia (SCZ) (n = 65,967), bipolar disorder (BD) (n = 41,653), autism spectrum disorder (ASD) (n = 46,350), attention deficit hyperactivity disorder (ADHD) (n = 55,374), and depression (DEP) (n = 688,809). After the meta-analysis, the number of genomic loci

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Accumulating evidence indicates a putative association of telomere length and mitochondrial function with antipsychotics response in schizophrenia (SCZ). However, pharmacological findings were limited and no previous work has assessed this in a prospective longitudinal study. This study assessed telomere length and mitochondrial DNA copy number in first-episode antipsychotic-naïve SCZ patients with 8-week risperidone treatment to evaluate the association between these biomarkers and clinical treatment response. We recruited 137 first-episode antipsychotic-naive SCZ patients (and 144 controls) at baseline and 89 patients completed the 8-week follow-up. Patients, completed follow-up, were divided into Responders (N = 46) and Non-Responders (N = 43) according to the percentage of symptoms improvement. Linear regression analyses show that SCZ patients had significantly lower mtDNA copy number (β = −0.108, p = 0.002), and no alteration of telomere length when compared with healthy controls. In addition, compared with Non-Responders, Responders had significantly lower mtDNA copy number (β = −0.178, p = 0.001), and longer telomere length (β = 0.111, p = 0.071) before the 8-week treatment. After treatment, Responders persisted lower mtDNA copy number comparing with No-Responders (partial η2 = 0.125, p = 0.001). These findings suggest that telomere length and mtDNA copy number may hold the potential to serve as predictors of antipsychotic response of SCZ patients.

Our results confirmed previous findings that AD pathology was related to dysconnectivity both within and between resting-state networks but revealed more spatial details. Moreover, the SAL network, reportedly flexibly coupling either with the DAN or DMN networks during different brain states, demonstrated interesting alterations specifically in the early stage of the disease.

Integrative analysis of multiple data types can take advantage of their complementary information and therefore may provide higher power to identify potential biomarkers that would be missed using individual data analysis. Due to different nature of diverse data modality, data integration is challenging. Here we address the data integration problem by developing a generalized sparse model (GSM) using weighting factors to integrate multi-modality data for biomarker selection. As an example, we applied the GSM model to a joint analysis of two types of schizophrenia data sets: 759075 SNPs and 153594 functional magnetic resonance imaging (fMRI) voxels in 208 subjects (92 cases/116 controls). To solve this small-sample-large-variable problem, we developed a novel sparse representation based variable selection (SRVS) algorithm, with the primary aim to identify biomarkers associated with schizophrenia. To validate the effectiveness of the selected variables, we performed multivariate classification followed by a ten-fold cross validation. We compared our proposed SRVS algorithm with an earlier sparse model based variable selection algorithm for integrated analysis. In addition, we compared with the traditional statistics method for univariant data analysis (Chi-squared test for SNP data and ANOVA for fMRI data). Results showed that our proposed SRVS method can identify novel biomarkers that show stronger capability in distinguishing schizophrenia patients from healthy controls. Moreover, better classification ratios were achieved using biomarkers from both types of data, suggesting the importance of integrative analysis.