Dataset Information

DbMDEGA: a database for meta-analysis of differentially expressed genes in autism spectrum disorder.

ABSTRACT: Autism spectrum disorders (ASD) are hereditary, heterogeneous and biologically complex neurodevelopmental disorders. Individual studies on gene expression in ASD cannot provide clear consensus conclusions. Therefore, a systematic review to synthesize the current findings from brain tissues and a search tool to share the meta-analysis results are urgently needed.Here, we conducted a meta-analysis of brain gene expression profiles in the current reported human ASD expression datasets (with 84 frozen male cortex samples, 17 female cortex samples, 32 cerebellum samples and 4 formalin fixed samples) and knock-out mouse ASD model expression datasets (with 80 collective brain samples). Then, we applied R language software and developed an interactive shared and updated database (dbMDEGA) displaying the results of meta-analysis of data from ASD studies regarding differentially expressed genes (DEGs) in the brain.This database, dbMDEGA ( https://dbmdega.shinyapps.io/dbMDEGA/ ), is a publicly available web-portal for manual annotation and visualization of DEGs in the brain from data from ASD studies. This database uniquely presents meta-analysis values and homologous forest plots of DEGs in brain tissues. Gene entries are annotated with meta-values, statistical values and forest plots of DEGs in brain samples. This database aims to provide searchable meta-analysis results based on the current reported brain gene expression datasets of ASD to help detect candidate genes underlying this disorder.This new analytical tool may provide valuable assistance in the discovery of DEGs and the elucidation of the molecular pathogenicity of ASD. This database model may be replicated to study other disorders.

SUBMITTER: Zhang S

PROVIDER: S-EPMC5691387 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

dbMDEGA: a database for meta-analysis of differentially expressed genes in autism spectrum disorder.

Zhang Shuyun S Deng Libin L Jia Qiyue Q Huang Shaoting S Gu Junwang J Zhou Fankun F Gao Meng M Sun Xinyi X Feng Chang C Fan Guangqin G

BMC bioinformatics 20171116 1

<h4>Background</h4>Autism spectrum disorders (ASD) are hereditary, heterogeneous and biologically complex neurodevelopmental disorders. Individual studies on gene expression in ASD cannot provide clear consensus conclusions. Therefore, a systematic review to synthesize the current findings from brain tissues and a search tool to share the meta-analysis results are urgently needed.<h4>Methods</h4>Here, we conducted a meta-analysis of brain gene expression profiles in the current reported human AS ...[more]

PMID: 29145823

Similar Datasets

Project description:BACKGROUND:Studies of individuals with autism spectrum disorder (ASD) have revealed a strong multigenic basis with the identification of hundreds of ASD susceptibility genes. ASD is characterized by social deficits and a range of other phenotypes, implicating complex genetics and involvement of a variety of brain regions. However, how mutations and mis-expression of select gene sets are associated with the behavioral components of ASD remains unknown. We reasoned that for genes to be associated with ASD core behaviors they must be: (1) expressed in brain regions relevant to ASD social behaviors and (2) expressed during the ASD susceptible window of brain development. METHODS:Focusing on the amygdala, a brain region whose dysfunction has been highly implicated in the social component of ASD, we mined publicly available gene expression databases to identify ASD-susceptibility genes expressed during human and mouse amygdala development. We found that a large cohort of known ASD susceptibility genes is expressed in the developing human and mouse amygdala. We further performed analysis of single-nucleus RNA-seq (snRNA-seq) data from microdissected amygdala tissue from five ASD and five control human postmortem brains ranging in age from 4 to 20?years to elucidate cell type specificity of amygdala-expressed genes and their dysregulation in ASD. RESULTS:Our analyses revealed that of the high-ranking ASD susceptibility genes, 80 are expressed in both human and mouse amygdala during fetal to early postnatal stages of development. Our human snRNA-seq analyses revealed cohorts of genes with altered expression in the ASD amygdala postnatally, especially within excitatory neurons, with dysregulated expression of seven genes predicted from our datamining pipeline. LIMITATIONS:We were limited by the ages for which we were able to obtain human tissue; therefore, the results from our datamining pipeline approach will require validation, to the extent possible, in human tissue from earlier developmental stages. CONCLUSIONS:Our pipeline narrows down the number of amygdala-expressed genes possibly involved in the social pathophysiology of ASD. Our human single-nucleus gene expression analyses revealed that ASD is characterized by changes in gene expression in specific cell types in the early postnatal amygdala.

Project description:Several reports have suggested a role for epigenetic mechanisms in ASD etiology. Epigenome-wide association studies (EWAS) in autism spectrum disorder (ASD) may shed light on particular biological mechanisms. However, studies of ASD cases versus controls have been limited by post-mortem timing and severely small sample sizes. Reports from in-life sampling of blood or saliva have also been very limited in sample size and/or genomic coverage. We present the largest case-control EWAS for ASD to date, combining data from population-based case-control and case-sibling pair studies.DNA from 968 blood samples from children in the Study to Explore Early Development (SEED 1) was used to generate epigenome-wide array DNA methylation (DNAm) data at 485,512 CpG sites for 453 cases and 515 controls, using the Illumina 450K Beadchip. The Simons Simplex Collection (SSC) provided 450K array DNAm data on an additional 343 cases and their unaffected siblings. We performed EWAS meta-analysis across results from the two data sets, with adjustment for sex and surrogate variables that reflect major sources of biological variation and technical confounding such as cell type, batch, and ancestry. We compared top EWAS results to those from a previous brain-based analysis. We also tested for enrichment of ASD EWAS CpGs for being targets of meQTL associations using available SNP genotype data in the SEED sample.In this meta-analysis of blood-based DNA from 796 cases and 858 controls, no single CpG met a Bonferroni discovery threshold of p?<?1.12?×?10-?7. Seven CpGs showed differences at p?<?1?×?10-?5 and 48 at 1?×?10-?4. Of the top 7, 5 showed brain-based ASD associations as well, often with larger effect sizes, and the top 48 overall showed modest concordance (r?=?0.31) in direction of effect with cerebellum samples. Finally, we observed suggestive evidence for enrichment of CpG sites controlled by SNPs (meQTL targets) among the EWAS CpG hits, which was consistent across EWAS and meQTL discovery p value thresholds.No single CpG site showed a large enough DNAm difference between cases and controls to achieve epigenome-wide significance in this sample size. However, our results suggest the potential to observe disease associations from blood-based samples. Among the seven sites achieving suggestive statistical significance, we observed consistent, and stronger, effects at the same sites among brain samples. Discovery-oriented EWAS for ASD using blood samples will likely need even larger samples and unified genetic data to further understand DNAm differences in ASD.

Dataset Information

DbMDEGA: a database for meta-analysis of differentially expressed genes in autism spectrum disorder.

Publications

dbMDEGA: a database for meta-analysis of differentially expressed genes in autism spectrum disorder.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure