Dataset Information

Cross-Tissue Exploration of Genetic and Epigenetic Effects on Brain Gray Matter in Schizophrenia.

ABSTRACT: Closely linking genetics and environment factors, epigenetics has been of increasing interest in psychiatric disease studies. In this work, we integrated single nucleotide polymorphisms (SNPs), DNA methylation of blood and saliva, and brain gray matter (GM) measures to explore the role of genetic and epigenetic variation to the brain structure changes in schizophrenia (SZ). By focusing on the reported SZ genetic risk regions, we applied a multi-stage multivariate analysis to a discovery dataset (92 SZ patients and 110 controls, blood) and an independent replication dataset (93 SZ patients and 99 controls, saliva). Two pairs of SNP-methylation components were significantly correlated (r = .48 and .35) in blood DNA, and replicated (r = .46 and .29) in saliva DNA, reflecting cross-tissue SNP cis-effects. In the discovery data, both SNP-related methylation components were also associated with one GM component primarily located in cerebellum, caudate, and thalamus. Additionally, another methylation component in NOSIP gene with significant SZ patient differences (P = .009), was associated with 8 GM components (7 with patient differences) including superior, middle, and inferior frontal gyri, superior, middle, and inferior temporal gyri, cerebellum, insula, cuneus, and lingual gyrus. Of these, 5 methylation-GM associations were replicated (P < .05). In contrast, no pairwise significant associations were observed between SNP and GM components. This study strongly supports that compared to genetic variation, epigenetics show broader and more significant associations with brain structure as well as diagnosis, which can be cross-tissue, and the potential in explaining the mechanism of genetic risks in SZ.

SUBMITTER: Lin D

PROVIDER: S-EPMC5814943 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Cross-Tissue Exploration of Genetic and Epigenetic Effects on Brain Gray Matter in Schizophrenia.

Lin Dongdong D Chen Jiayu J Ehrlich Stefan S Bustillo Juan R JR Perrone-Bizzozero Nora N Walton Esther E Clark Vincent P VP Wang Yu-Ping YP Sui Jing J Du Yuhui Y Ho Beng C BC Schulz Charles S CS Calhoun Vince D VD Liu Jingyu J

Schizophrenia bulletin 20180201 2

Closely linking genetics and environment factors, epigenetics has been of increasing interest in psychiatric disease studies. In this work, we integrated single nucleotide polymorphisms (SNPs), DNA methylation of blood and saliva, and brain gray matter (GM) measures to explore the role of genetic and epigenetic variation to the brain structure changes in schizophrenia (SZ). By focusing on the reported SZ genetic risk regions, we applied a multi-stage multivariate analysis to a discovery dataset ...[more]

PMID: 28521044

Similar Datasets

Project description:BACKGROUND:One of the major challenges in current psychiatric epigenetic studies is the tissue specificity of epigenetic changes since access to brain samples is limited. Peripheral tissues have been studied as surrogates but the knowledge of cross-tissue genetic-epigenetic characteristics remains largely unknown. In this work, we conducted a comprehensive investigation of genetic influence on DNA methylation across brain and peripheral tissues with the aim to characterize cross-tissue genetic-epigenetic effects and their roles in the pathophysiology of psychiatric disorders. METHODS:Genome-wide methylation quantitative trait loci (meQTLs) from brain prefrontal cortex, whole blood, and saliva were identified separately and compared. Focusing on cis-acting effects, we tested the enrichment of cross-tissue meQTLs among cross-tissue expression QTLs and genetic risk loci of various diseases, including major psychiatric disorders. CpGs targeted by cross-tissue meQTLs were also tested for genomic distribution and functional enrichment as well as their contribution to methylation correlation across tissues. Finally, a consensus co-methylation network analysis on the cross-tissue meQTL targeted CpGs was performed on data of the three tissues collected from schizophrenia patients and controls. RESULTS:We found a significant overlap of cis meQTLs (45-73 %) and targeted CpG sites (31-68 %) among tissues. The majority of cross-tissue meQTLs showed consistent signs of cis-acting effects across tissues. They were significantly enriched in genetic risk loci of various diseases, especially schizophrenia, and also enriched in cross-tissue expression QTLs. Compared to CpG sites not targeted by any meQTLs, cross-tissue targeted CpGs were more distributed in CpG island shores and enhancer regions, and more likely had strong correlation with methylation levels across tissues. The targeted CpGs were also annotated to genes enriched in multiple psychiatric disorders and neurodevelopment-related pathways. Finally, we identified one co-methylation network shared between brain and blood showing significant schizophrenia association (p = 5.5 × 10-6). CONCLUSIONS:Our results demonstrate prevalent cross-tissue meQTL effects and their contribution to the correlation of CpG methylation across tissues, while at the same time a large portion of meQTLs show tissue-specific characteristics, especially in brain. Significant enrichment of cross-tissue meQTLs in expression QTLs and genetic risk loci of schizophrenia suggests the potential of these cross-tissue meQTLs for studying the genetic effect on schizophrenia. The study provides compelling motivation for a well-designed experiment to further validate the use of surrogate tissues in the study of psychiatric disorders.

Project description:One application of imaging genomics is to explore genetic variants associated with brain structure and function, presenting a new means of mapping genetic influences on mental disorders. While there is growing interest in performing genome-wide searches for determinants, it remains challenging to identify genetic factors of small effect size, especially in limited sample sizes. In an attempt to address this issue, we propose to take advantage of a priori knowledge, specifically to extend parallel independent component analysis (pICA) to incorporate a reference (pICA-R), aiming to better reveal relationships between hidden factors of a particular attribute. The new approach was first evaluated on simulated data for its performance under different configurations of effect size and dimensionality. Then pICA-R was applied to a 300-participant (140 schizophrenia (SZ) patients versus 160 healthy controls) dataset consisting of structural magnetic resonance imaging (sMRI) and single nucleotide polymorphism (SNP) data. Guided by a reference SNP set derived from ANK3, a gene implicated by the Psychiatric Genomic Consortium SZ study, pICA-R identified one pair of SNP and sMRI components with a significant loading correlation of 0.27 (p=1.64×10(-6)). The sMRI component showed a significant group difference in loading parameters between patients and controls (p=1.33×10(-15)), indicating SZ-related reduction in gray matter concentration in prefrontal and temporal regions. The linked SNP component also showed a group difference (p=0.04) and was predominantly contributed to by 1030 SNPs. The effect of these top contributing SNPs was verified using association test results of the Psychiatric Genomic Consortium SZ study, where the 1030 SNPs exhibited significant SZ enrichment compared to the whole genome. In addition, pathway analyses indicated the genetic component majorly relating to neurotransmitter and nervous system signaling pathways. Given the simulation and experiment results, pICA-R may prove a promising multivariate approach for use in imaging genomics to discover reliable genetic risk factors under a scenario of relatively high dimensionality and small effect size.

Project description:Prior postmortem studies have shown gray matter (GM) microstructural abnormalities in schizophrenia. However, few studies to date have examined GM microstructural integrity in schizophrenia in vivo. Here, we employed diffusion kurtosis imaging (DKI) to test for differences in GM microstructure in eighteen schizophrenia (SZ) patients versus nineteen healthy controls (HC). GM microstructure was characterized in each participant using DKI-derived metrics of mean kurtosis (MK) and mean diffusivity (MD). Individual T1-weighted images were used to create subject-specific cortically-labelled regions of interest (ROIs) of the four cortical lobes and sixty-eight cortical GM regions delineated by the Desikan-Killiany atlas, and to derive the associated cortical thickness and area measures. The derived ROIs were also registered to the diffusion space of each subject and used to generate region-specific mean MK and MD values. We additionally administered the Wisconsin Card Sorting Test (WCST), Stroop test, and Trail Making Test part B (Trails-B) to test the relationship between GM metrics and executive function in SZ. We found significantly increased MK and MD in SZ compared to HC participants in the temporal lobe, sub-lobar temporal cortical regions (fusiform, inferior temporal, middle temporal and temporal pole), and posterior cingulate cortex after correcting for multiple comparisons. Correlational analyses revealed significant associations of MK and MD with executive function scores derived from the WCST, Stroop, and Trails-B tests, along with an inverse relationship between MK and MD and cortical thickness and area. A hierarchical multiple linear regression analysis showed that up to 85% of the inter-subject variability in cognitive function in schizophrenia measured by the WCST could be explained by MK in combination with either GM thickness or area. MK and MD appear to be sensitive to GM microstructural pathology in schizophrenia and may provide useful biomarkers of abnormal cortical microstructure in this disorder.

Dataset Information

Cross-Tissue Exploration of Genetic and Epigenetic Effects on Brain Gray Matter in Schizophrenia.

Publications

Cross-Tissue Exploration of Genetic and Epigenetic Effects on Brain Gray Matter in Schizophrenia.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets