Multi-tissue coexpression networks reveal unexpected subnetworks associated with disease

Dobrin, Radu; Zhu, Jun; Molony, Cliona; Argman, Carmen; Parrish, Mark L.; Carlson, Sonia; Allan, M. F.; Pomp, Daniel; Schadt, Eric E.

doi:10.1186/gb-2009-10-5-r55

Cited by 148 publications

(139 citation statements)

References 58 publications

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“…Therefore, it is quite surprising to observe that, with notable exceptions, [26][27][28][29][30] coexpression relationships based on DNA microarrays are not used extensively to support the identification of new diseasecausing genes. The main reason for this discrepancy may well reside in the low specificity of the functional predictions obtained by coexpression analysis.…”

Section: Discussionmentioning

confidence: 99%

An atlas of tissue-specific conserved coexpression for functional annotation and disease gene prediction

et al. 2011

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Gene coexpression relationships that are phylogenetically conserved between human and mouse have been shown to provide important clues about gene function that can be efficiently used to identify promising candidate genes for human hereditary disorders. In the past, such approaches have considered mostly generic gene expression profiles that cover multiple tissues and organs. The individual genes of multicellular organisms, however, can participate in different transcriptional programs, operating at scales as different as single-cell types, tissues, organs, body regions or the entire organism. Therefore, systematic analysis of tissue-specific coexpression could be, in principle, a very powerful strategy to dissect those functional relationships among genes that emerge only in particular tissues or organs. In this report, we show that, in fact, conserved coexpression as determined from tissue-specific and condition-specific data sets can predict many functional relationships that are not detected by analyzing heterogeneous microarray data sets. More importantly, we find that, when combined with disease networks, the simultaneous use of both generic (multi-tissue) and tissue-specific conserved coexpression allows a more efficient prediction of human disease genes than the use of generic conserved coexpression alone. Using this strategy, we were able to identify high-probability candidates for 238 orphan disease loci. We provide proof of concept that this combined use of generic and tissue-specific conserved coexpression can be very useful to prioritize the mutational candidates obtained from deep-sequencing projects, even in the case of genetic disorders as heterogeneous as XLMR.

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Section: Discussionmentioning

confidence: 99%

An atlas of tissue-specific conserved coexpression for functional annotation and disease gene prediction

et al. 2011

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“…The key to solving this challenge is the realization that new mutations and segregating variants do not affect organismal phenotypes directly, but do so via complex interacting networks of transcriptional, protein, metabolic, and other molecular endophenotypes (Anholt et al 2003;Sieberts and Schadt 2007;Chen et al 2008;Emilsson et al 2008;Keller et al 2008;Ayroles et al 2009;Cookson et al 2009;Dobrin et al 2009;Edwards et al 2009a;Harbison et al 2009;Morozova et al 2009;Schadt 2009;Schadt et al 2009;Winrow et al 2009). If we know the network elements (genes) and connections (cis-and trans-regulation) associated with any complex phenotype, we can begin to make predictions about the effects of genetic perturbations on the organismal trait and whole network responses to such perturbations.…”

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confidence: 99%

Transcriptional Networks for Alcohol Sensitivity in Drosophila melanogaster

2011

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Understanding the genetic architecture of polygenic traits requires investigating how complex networks of interacting molecules mediate the effect of genetic variation on organismal phenotypes. We used a combination of P-element mutagenesis and analysis of natural variation in gene expression to predict transcriptional networks that underlie alcohol sensitivity in Drosophila melanogaster. We identified 139 unique P-element mutations (124 in genes) that affect sensitivity or resistance to alcohol exposure. Further analyses of nine of the lines showed that the P-elements affected expression levels of the tagged genes, and P-element excision resulted in phenotypic reversion. The majority of the mutations were in computationally predicted genes or genes with unexpected effects on alcohol phenotypes. Therefore we sought to understand the biological relationships among 21 of these genes by leveraging genetic correlations among genetically variable transcripts in wild-derived inbred lines to predict coregulated transcriptional networks. A total of 32 ''hub'' genes were common to two or more networks associated with the focal genes. We used RNAi-mediated inhibition of expression of focal genes and of hub genes connected to them in the network to confirm their effects on alcohol-related phenotypes. We then expanded the computational networks using the hub genes as foci and again validated network predictions. Iteration of this approach allows a stepwise expansion of the network with simultaneous functional validation. Although coregulated transcriptional networks do not provide information about causal relationships among their constituent transcripts, they provide a framework for subsequent functional studies on the genetic basis of alcohol sensitivity.

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“…4) (27) is, in our view, critical to elucidating how physiological molecular processes are turned into pathological ones and thereby reveal the full complexity of CCDs. Networks specific to interactions between organs are particularly interesting from the CCD perspective because they are likely more important in later phases of disease development, when pathological changes are spreading across the borders of individual organs (27) (Fig. 4; see overlapping networks).…”

Section: Defining Inherited Risk Dependent On Environmentsmentioning

confidence: 99%

“…1). Some genes specialize in cross-tissue communication (27), so that some parts of networks (in this example, shown as orange nodes) are shared among different tissues and likely are responsible for related molecular activities. Genes in crosstissue networks do not appear to belong to tissue-specific networks (and visa versa).…”

Section: New Health Carementioning

confidence: 99%

NEW: Network-Enabled Wisdom in Biology, Medicine, and Health Care

2012

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Complete repertoires of molecular activity in and between tissues provided by new high-dimensional "omics" technologies hold great promise for characterizing human physiology at all levels of biological hierarchies. The combined effects of genetic and environmental perturbations at any level of these hierarchies can lead to vicious cycles of pathology and complex systemic diseases. The challenge lies in extracting all relevant information from the rapidly increasing volumes of omics data and translating this information first into knowledge and ultimately into wisdom that can yield clinically actionable results. Here, we discuss how molecular networks are central to the implementation of this new biology in medicine and translation to preventive and personalized health care.

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Multi-tissue coexpression networks reveal unexpected subnetworks associated with disease

Abstract: Background: Obesity is a particularly complex disease that at least partially involves genetic and environmental perturbations to gene-networks connecting the hypothalamus and several metabolic tissues, resulting in an energy imbalance at the systems level.

Cited by 148 publications

References 58 publications

An atlas of tissue-specific conserved coexpression for functional annotation and disease gene prediction

An atlas of tissue-specific conserved coexpression for functional annotation and disease gene prediction

Transcriptional Networks for Alcohol Sensitivity in Drosophila melanogaster

NEW: Network-Enabled Wisdom in Biology, Medicine, and Health Care

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