Type I interferon signaling genes in recurrent major depression: increased expression detected by whole-blood RNA sequencing

Mostafavi, Sara; Battle, Alexis; Zhu, Xiaowei; Potash, James B.; Weissman, Myrna M.; Shi, Jianxin; Beckman, Kenneth B.; Haudenschild, Christian; McCormick, Courtney; Mei, Rui; Gameroff, Marc J.; Gindes, H; Adams, Phil; Goes, Fernando S.; Mondimore, Francis M.; MacKinnon, Dean F.; Notes, Lisa D.; Schweizer, Barbara; Furman, David; Montgomery, Stephen B.; Urban, Alexander; Koller, Daphne; Levinson, Douglas F.

doi:10.1038/mp.2013.161

Cited by 158 publications

(183 citation statements)

References 51 publications

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“…While there are not other large-scale RNA-seq data sets for most GTEx tissue types, we replicated relationships identified by our GTEx whole blood TWN using an independent whole blood RNA-seq data set on 922 individuals of European ancestry from the Depression Genes and Networks study (DGN) Mostafavi et al 2014). First, we tested whether TE and IR nodes connected by an edge in the GTEx whole blood TWN were also correlated in DGN.…”

Section: Resultsmentioning

confidence: 49%

“…A "TE-IR" hub is a TE node with multiple IR neighbors, and an "IR-TE" hub is an IR node with multiple TE neighbors. (B) Fraction of whole blood TWN edges replicating in an independent RNA-seq data set (DGN) Mostafavi et al 2014). …”

Section: Resultsmentioning

confidence: 99%

“…We replicated our network edges with GTEx whole blood tissue in an independent RNA-seq data set: Depression Genes and Networks Mostafavi et al 2014). DGN includes quantifications of 15,231 genes and 12,080 isoforms from whole blood in 922 samples, out of which 5609 genes and 1464 isoforms were uniquely mapped to the set of genes and isoforms used in GTEx whole blood TWN reconstruction.…”

Section: Replication Of Whole Blood Twnmentioning

confidence: 99%

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Co-expression networks reveal the tissue-specific regulation of transcription and splicing

et al. 2017

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Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues.

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Section: Resultsmentioning

confidence: 49%

Section: Resultsmentioning

confidence: 99%

Section: Replication Of Whole Blood Twnmentioning

confidence: 99%

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Co-expression networks reveal the tissue-specific regulation of transcription and splicing

et al. 2017

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“…These differences can have significant consequences: tissue-specific genes are especially likely to be drug targets [6] and tissue-specific transcription factors are especially likely to be implicated in complex diseases [2,7,8]. Understanding these differences is also essential for understanding pleiotropic genes, and for interpreting studies in which genomics data can only be collected for an accessible or a proxy tissue (such as use of blood in studying psychiatric disorders [9][10][11]). …”

Section: Introductionmentioning

confidence: 99%

Sharing and specificity of co-expression networks across 35 human tissues

Pierson

Koller

Battle

et al. 2014

Preprint

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To understand the regulation of tissue-specific gene expression, the GTEx Consortium generated RNA-seq expression data for more than thirty distinct human tissues. This data provides an opportunity for deriving shared and tissue specific gene regulatory networks on the basis of co-expression between genes. However, a small number of samples are available for a majority of the tissues, and therefore statistical inference of networks in this setting is highly underpowered. To address this problem, we infer tissue-specific gene co-expression networks for 35 tissues in the GTEx dataset using a novel algorithm, GNAT, that uses a hierarchy of tissues to share data between related tissues. We show that this transfer learning approach increases the accuracy with which networks are learned. Analysis of these networks reveals that tissue-specific transcription factors are hubs that preferentially connect to genes with tissue specific functions. Additionally, we observe that genes with tissue-specific functions lie at the peripheries of our networks. We identify numerous modules enriched for Gene Ontology functions, and show that

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“…Gene expression is an important link between DNA sequence variation and higherorder cellular phenotypes, and has facilitated the discovery of molecular mechanisms for a range of common brain-related disorders (13,14). In migraine, it has been shown that blood gene expression patterns can differentiate: (i) chronic migraine and acute migraine from healthy controls (15); (ii) chronic migraine without medication overuse from patients with medication overuse (16); and (iii) menstrual-related migraine from non-menstrual-related migraine (17).…”

Section: Introductionmentioning

confidence: 99%

Integrating Genome-Wide Association and Blood Genomic Profiling Data to Characterise Migraine Risk Loci

Gerring¹

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Migraine is a frequent, debilitating and painful headache disorder that normally affects people during their most productive years of life (25% of females and 8% of males).Genome-wide association studies have identified 44 independent single nucleotide polymorphisms associated with migraine, however the causal genes and pathogenic mechanisms underlying the disorder remain unknown. Therefore, we performed a multi-staged integrated study of gene expression and DNA methylation in a large sample of migraine cases and non-migraine controls from the Brisbane Systems Genetics Study. We identified several gene expression networks and differentially methylated regions associated with migraine, which were subsequently characterised using robust statistical and pathway-based approaches. Integration of existing genomewide association data with gene expression and methylation data prioritised migraine susceptibility genes for further functional characterisation. This study suggests the use of multiple molecular data to study existing migraine loci has the potential to provide a substantial contribution to understanding the underlying genetic architecture and biological mechanisms of migraine, and may help in the development of diagnostic tests and new targets for drug therapy.3

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Type I interferon signaling genes in recurrent major depression: increased expression detected by whole-blood RNA sequencing

Cited by 158 publications

References 51 publications

Co-expression networks reveal the tissue-specific regulation of transcription and splicing

Co-expression networks reveal the tissue-specific regulation of transcription and splicing

Sharing and specificity of co-expression networks across 35 human tissues

Integrating Genome-Wide Association and Blood Genomic Profiling Data to Characterise Migraine Risk Loci

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