<i>deTS</i>: tissue-specific enrichment analysis to decode tissue specificity

Pei, Guangsheng; Dai, Yulin; Zhao, Zhongming; Jia, Peilin

doi:10.1093/bioinformatics/btz138

Cited by 52 publications

(73 citation statements)

References 24 publications

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“…These 13 traits included lots of psychiatric disorders, including attention deficit hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder and schizophrenia. To extend our previous tissue-level study (Pei et al 2019), we found that for most neuropsychiatric diseases, their susceptible genes were mainly enriched in excitatory and inhibitory neurons (Fig. 8).…”

Section: Cell-type Specific Enrichment Analysis Of 13 Major Brain Asssupporting

confidence: 60%

Gene expression imputation and cell-type deconvolution in human brain with spatiotemporal precision and its implications for brain-related disorders

Pei¹,

Wang²,

Simon³

et al. 2020

Genome Res.

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As the most complex organ of the human body, the brain is composed of diverse regions, each consisting of distinct cell types and their respective cellular interactions. Human brain development involves a finely tuned cascade of interactive events. These include spatiotemporal gene expression changes and dynamic alterations in cell-type composition. However, our understanding of this process is still largely incomplete owing to the difficulty of brain spatiotemporal transcriptome collection. In this study, we developed a tensor-based approach to impute gene expression on a transcriptome-wide level. After rigorous computational benchmarking, we applied our approach to infer missing data points in the widely used BrainSpan resource and completed the entire grid of spatiotemporal transcriptomics. Next, we conducted deconvolutional analyses to comprehensively characterize major cell-type dynamics across the entire BrainSpan resource to estimate the cellular temporal changes and distinct neocortical areas across development. Moreover, integration of these results with GWAS summary statistics for 13 brain-associated traits revealed multiple novel trait–cell-type associations and trait-spatiotemporal relationships. In summary, our imputed BrainSpan transcriptomic data provide a valuable resource for the research community and our findings help further studies of the transcriptional and cellular dynamics of the human brain and related diseases.

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Section: Cell-type Specific Enrichment Analysis Of 13 Major Brain Asssupporting

confidence: 60%

Gene expression imputation and cell-type deconvolution in human brain with spatiotemporal precision and its implications for brain-related disorders

Pei¹,

Wang²,

Simon³

et al. 2020

Genome Res.

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“…We will evaluate these association signals using additional annotations such as epigenetic marks, expression quantitative trait loci (eQTL), tissue-specificity and cell types, among others. Tissue specificity enrichment analysis of genetic variations in association studies has shown interesting results, as demonstrated in our multitrait GWAS analysis [60]. Finally, further experimental validation of supported and proposed MS mechanisms is needed using cell lines and animal models.…”

Section: Limitations and Future Workmentioning

confidence: 67%

Dense module searching for gene networks associated with multiple sclerosis

et al. 2020

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Background: Multiple sclerosis (MS) is a complex disease in which the immune system attacks the central nervous system. The molecular mechanisms contributing to the etiology of MS remain poorly understood. Genome-wide association studies (GWAS) of MS have identified a small number of genetic loci significant at the genome level, but they are mainly non-coding variants. Network-assisted analysis may help better interpret the functional roles of the variants with association signals and potential translational medicine application. The Dense Module Searching of GWAS tool (dmGWAS version 2.4) developed in our team is applied to 2 MS GWAS datasets (GeneMSA and IMSGC GWAS) using the human protein interactome as the reference network. A dual evaluation strategy is used to generate results with reproducibility. Results: Approximately 7500 significant network modules were identified for each independent GWAS dataset, and 20 significant modules were identified from the dual evaluation. The top modules included GRB2, HDAC1, JAK2, MAPK1, and STAT3 as central genes. Top module genes were enriched with functional terms such as "regulation of glial cell differentiation" (adjusted p-value = 2.58 × 10 − 3), "T-cell costimulation" (adjusted p-value = 2.11 × 10 − 6) and "virus receptor activity" (adjusted p-value = 1.67 × 10 − 3). Interestingly, top gene networks included several MS FDA approved drug target genes HDAC1, IL2RA, KEAP1, and RELA, Conclusions: Our dmGWAS network analyses highlighted several genes (GRB2, HDAC1, IL2RA, JAK2, KEAP1, MAPK1, RELA and STAT3) in top modules that are promising to interpret GWAS signals and link to MS drug targets. The genes enriched with glial cell differentiation are important for understanding neurodegenerative processes in MS and for remyelination therapy investigation. Importantly, our identified genetic signals enriched in T cell costimulation and viral receptor activity supported the viral infection onset hypothesis for MS.

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“…To identify the disease-related tissues of AUD, we first applied tissue-specific enrichment analysis (TSEA) of the AUD GWAS data and determined three tissues for AUD: pituitary (the most significant raw p=3.2 × 10 -4 ), brain cerebellum (raw p=8.9 × 10 -4 ), and liver (p=1.5 × 10 -3 ) (see below). We further applied a widely used online tool, FUMA, to validate the TSEA by deTS 29 30. As a result, FUMA identified four frequently occurred AUD-associated tissues: brain cerebellum (raw p=0.02), brain frontal cortex (BA9) (raw p=0.02), brain hippocampus (raw p=0.02), and pituitary (raw p=0.03).…”

Section: Resultsmentioning

confidence: 99%

“…To identify the tissues that are most likely related with AUD, we applied two methods to determine the AUD-associated tissues, the decoding Tissue Specificity (deTS) package developed in our previous work29 and the Functional Mapping and Annotation (FUMA) 30. DeTS is an R package that provides a panel with 47 tissues from the GTEx V.7 expression data and implements Fisher’s exact test to examine whether genes in a query list are enriched in a tissue.…”

Section: Methodsmentioning

confidence: 99%

Diverse types of genomic evidence converge on alcohol use disorder risk genes

Dai

Pei

et al. 2020

J Med Genet

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BackgroundAlcohol use disorder (AUD) is one of the most common forms of substance use disorders with a strong contribution of genetic (50%–60%) and environmental factors. Genome-wide association studies (GWAS) have identified a number of AUD-associated variants, including those in alcohol metabolism genes. These genetic variants may modulate gene expression, making individuals more susceptible to AUD. A long-term alcohol consumption can also change the transcriptome patterns of subjects via epigenetic modulations.MethodsTo explore the interactive effect of genetic and epigenetic factors on AUD, we conducted a secondary analysis by integrating GWAS, CNV, brain transcriptome and DNA methylation data to unravel novel AUD-associated genes/variants. We applied the mega-analysis of OR (MegaOR) method to prioritise AUD candidate genes (AUDgenes).ResultsWe identified a consensus set of 206 AUDgenes based on the multi-omics data. We demonstrated that these AUDgenes tend to interact with each other more frequent than chance expectation. Functional annotation analysis indicated that these AUDgenes were involved in substance dependence, synaptic transmission, glial cell proliferation and enriched in neuronal and liver cells. We obtained a multidimensional evidence that AUD is a polygenic disorder influenced by both genetic and epigenetic factors as well as the interaction of them.ConclusionWe characterised multidimensional evidence of genetic, epigenetic and transcriptomic data in AUD. We found that 206 AUD associated genes were highly expressed in liver, brain cerebellum, frontal cortex, hippocampus and pituitary. Our studies provides important insights into the molecular mechanism of AUD and potential target genes for AUD treatment.

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deTS: tissue-specific enrichment analysis to decode tissue specificity

Cited by 52 publications

References 24 publications

Gene expression imputation and cell-type deconvolution in human brain with spatiotemporal precision and its implications for brain-related disorders

Gene expression imputation and cell-type deconvolution in human brain with spatiotemporal precision and its implications for brain-related disorders

Dense module searching for gene networks associated with multiple sclerosis

Diverse types of genomic evidence converge on alcohol use disorder risk genes

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