Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Heritability and polygenic predictionIn the EUR sample, the SNP-based heritability (h 2 SNP ) (that is, the proportion of variance in liability attributable to all measured SNPs)

Schizophrenia is a debilitating psychiatric disorder with approximately 1% lifetime risk globally. Large-scale schizophrenia genetic studies have reported primarily on European ancestry samples, Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:

79 80 * These authors contributed equally to the work 81 §These authors jointly supervised the work 82 †Lists of participants and their affiliations appear in the Supplementary Information 83 84 85 86Finally, polygenic risk score (PRS) prediction is emerging as a useful tool for studying 129 the effects of genetic liability, identifying more homogeneous phenotypes, and stratifying 130 patients, but the applicability of training data from EUR studies to those of non-European 131 ancestry has not been fully assessed, leaving us with an uncertainty as to the biological 132 implications and utility in non-Europeans 20 . 133 134 Schizophrenia genetic associations in the East Asian populations 135To systematically examine the genetic architecture of schizophrenia in individuals of East Asian 136 ancestry (EAS), we compiled 22,778 schizophrenia cases and 35,362 controls from 20 samples 137

Schizophrenia has been associated with abnormal task-related brain activation in sensory and motor regions as well as social cognition network. Recently, two studies investigated temporal correlation between resting-state functional magnetic resonance imaging (R-fMRI) low-frequency oscillations (LFOs) in schizophrenia but reported mixed results. This may be due to the different frequency bands used in these studies. Here we utilized R-fMRI to measure the amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF) in three different frequency bands (slow-5: 0.01-0.027 Hz; slow-4: 0.027-0.08 Hz; and typical band: 0.01-0.08 Hz) in 69 patients with schizophrenia and 62 healthy controls. We showed that there were significant differences in ALFF/fALFF between the two bands (slow-5 and slow-4) in regions including basal ganglia, midbrain, and ventromedial prefrontal cortex. Importantly, we also identified significant interaction between frequency bands and groups in inferior occipital gyrus, precuneus, and thalamus. The results suggest that the abnormalities of LFOs in schizophrenia is dependent on the frequency band and suggest that future studies should take the different frequency bands into account when measure intrinsic brain activity.

Since brain tissue is not readily accessible, a new focus in search of biomarkers for schizophrenia is blood-based expression profiling of non-protein coding genes such as microRNAs (miRNAs), which regulate gene expression by inhibiting the translation of messenger RNAs. This study aimed to identify potential miRNA signature for schizophrenia by comparing genome-wide miRNA expression profiles in patients with schizophrenia vs. healthy controls. A genome-wide miRNA expression profiling was performed using a Taqman array of 365 human miRNAs in the mononuclear leukocytes of a learning set of 30 cases and 30 controls. The discriminating performance of potential biomarkers was validated in an independent testing set of 60 cases and 30 controls. The expression levels of the miRNA signature were then evaluated for their correlation with the patients' clinical symptoms, neurocognitive performances, and neurophysiological functions. A seven-miRNA signature (hsa-miR-34a, miR-449a, miR-564, miR-432, miR-548d, miR-572 and miR-652) was derived from a supervised classification with internal cross-validation, with an area under the curve (AUC) of receiver operating characteristics of 93%. The putative signature was then validated in the testing set, with an AUC of 85%. Among these miRNAs, miR-34a was differentially expressed between cases and controls in both the learning (P = 0.005) and the testing set (P = 0.002). These miRNAs were differentially correlated with patients' negative symptoms, neurocognitive performance scores, and event-related potentials. The results indicated that the mononuclear leukocyte-based miRNA profiling is a feasible way to identify biomarkers for schizophrenia, and the seven-miRNA signature warrants further investigation.

A positive linkage of schizophrenia with chromosome 1q loci has been reported in Caucasian patients. This study was designed to evaluate the linkage of schizophrenia with markers of the 1q22-44 region in 52 Taiwanese families with at least two affected siblings. In the region 1q22-31 (17.8 cM), marker D1S1679 had a maximal proportion (0.57, P ¼ 0.03) of shared identity by descent (IBD) under a narrow phenotype (DSM-IV schizophrenia only). In the region 1q42-44 (26.8 cM), the marker D1S251, located near the breakpoint of a balanced translocation t (1;11) (q42.1;q14.3) segregated with schizophrenia, and also near the neurodevelopment-related 'Disrupted in Schizophrenia 1' gene, had a maximum NPL score of 1.73 (P ¼ 0.03) under the narrow phenotype model and 2.18 (P ¼ 0.01) under the broad phenotype model comprised of schizophrenia, schizoaffective disorder, and other nonaffective psychotic disorders as defined by DSM-IV criteria. The marker D1S2836 also had a maximal proportion (0.57, P ¼ 0.05) of shared IBD under the broad model. These findings may provide guidance for positional cloning studies on candidate genes in the 1q22-31 and 1q41-44 regions.

Automated tract-based analysis of diffusion MRI is an important tool for investigating tract integrity of the cerebral white matter. Current template-based automatic analyses still lack a comprehensive list of tract atlas and an accurate registration method. In this study, tract-based automatic analysis (TBAA) was developed to meet the demands. Seventy-six major white matter tracts were reconstructed on a high-quality diffusion spectrum imaging (DSI) template, and an advanced two-step registration strategy was proposed by incorporating anatomical information of the gray matter from T1-weighted images in addition to microstructural information of the white matter from diffusion-weighted images. The automatic analysis was achieved by establishing a transformation between the DSI template and DSI dataset of the subject derived from the registration strategy. The tract coordinates in the template were transformed to native space in the individual's DSI dataset, and the microstructural properties of major tract bundles were sampled stepwise along the tract coordinates of the subject's DSI dataset. In a validation study of eight well-known tracts, our results showed that TBAA had high geometric agreement with manual tracts in both deep and superficial parts but significantly smaller measurement variability than manual method in functional difference. Additionally, the feasibility of the method was demonstrated by showing tracts with altered microstructural properties in patients with schizophrenia. Fifteen major tract bundles were found to have significant differences after controlling the family-wise error rate. In conclusion, the proposed TBAA method is potentially useful in brain-wise investigations of white matter tracts, particularly for a large cohort study.

Low frequency oscillations are essential in cognitive function impairment in schizophrenia. While functional connectivity can reveal the synchronization between distant brain regions, the regional abnormalities in task-independent baseline brain activity are less clear, especially in specific frequency bands. Here, we used a regional homogeneity (ReHo) method combined with resting-state functional magnetic resonance imaging to investigate low frequency spontaneous neural activity in the three different frequency bands (slow-5∶0.01–0.027 Hz; slow-4∶0.027–0.08 Hz; and typical band: 0.01–0.08 Hz) in 69 patients with schizophrenia and 62 healthy controls. Compared with controls, schizophrenia patients exhibited decreased ReHo in the precentral gyrus, middle occipital gyrus, and posterior insula, whereas increased ReHo in the medial prefrontal cortex and anterior insula. Significant differences in ReHo between the two bands were found in fusiform gyrus and superior frontal gyrus (slow-4> slow-5), and in basal ganglia, parahippocampus, and dorsal middle prefrontal gyrus (slow-5> slow-4). Importantly, we identified significant interaction between frequency bands and groups in the inferior occipital gyrus and caudate body. This study demonstrates that ReHo changes in schizophrenia are widespread and frequency dependent.