Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases
By Daniel Marbach, David Lamparter, G. Quon, Manolis Kellis, Zoltán Kutalik, Sven Bergmann. Nature Methods, 2016
303 citations recorded by the source corpus.
Mapping perturbed molecular circuits that underlie complex diseases remains a great challenge. We developed a comprehensive resource of 394 cell type– and tissue-specific gene regulatory networks for human, each specifying the genome-wide connectivity among transcription factors, enhancers, promoters and genes. Integration with 37 genome-wide association studies (GWASs) showed that disease-associated genetic variants—including variants that do not reach genome-wide significance—often perturb regulatory modules that are highly specific to disease-relevant cell types or tissues. Our resource opens the door to systematic analysis of regulatory programs across hundreds of human cell types and tissues (http://regulatorycircuits.org).
Subfields: Genetics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/2e0dceca54f59fd489e9afbfdfe211b4f45068cb
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In Genetics
In Molecular Biology
- Gustavo A. Stolovitzky
- J. Sáez-Rodríguez
- Lenore J. Cowen
- Sven Bergmann
- Zoltán Kutalik
- Konrad J. Karczewski
- Daniel Marbach
- David Lamparter
Citations in the corpus
- Assessment of network module identification across complex diseases (2019, background): “Recent studies have further introduced comprehensive collections of tissue-specific networks (Greene et al., 2015; Marbach et al., 2016).”
- Open Community Challenge Reveals Molecular Network Modules with Key Roles in Diseases (2018, background): “Recent studies have further introduced comprehensive collections of tissue-specific networks (Greene et al., 2015; Marbach et al., 2016).”
- Integrative omics for health and disease (2018, background): “In many cases, the disease-relevant tissue may be well described, such as muscle tissue for MD; however, if the disease is less well defined or the tissue is not available, a tissue may be identified from a network analysis of the disease 99 .”