Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases

Abstract

Mapping the molecular circuits that are perturbed by genetic variants underlying complex traits and diseases remains a great challenge. We present a comprehensive resource of 394 cell type and tissue-specific gene regulatory networks for human, each specifying the genome-wide connectivity between transcription factors, enhancers, promoters and genes. Integration with 37 genome-wide association studies (GWASs) shows 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.

Early Transcriptional Divergence Marks Virus-Specific Primary Human CD8+ T Cells in Chronic versus Acute Infection

“…Links to the individual resources are given in the KEY RESOURCES TABLE. Furthermore, we included interactions from two weighted, cell type-specific regulatory networks for CD8 + T cells and lymphocytes inferred recently published by Marbach et al (Marbach et al, 2016). For purposes of this study, only interactions with an interaction weight >0.005 were included from these two networks.…”

Integrative omics for health and disease

“…The GTEx, Roadmap Epigenomics and Functional Annotation of Mammalian Genome 5 (FANTOM5) projects provide reference data sets for multi-tissue gene expression and epigenomics data 47,57,98 . 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 . Indeed, using the disease-relevant tissue has proved beneficial in diagnosis of patients with MD, where transcriptome analysis of the muscle tissue resulted in diagnoses that would not have been made via easily accessible proxy tissue, such as blood or fibroblasts, owing to the relatively low expression of disease-relevant genes 16 .…”

Crowdsourcing biomedical research: leveraging communities as innovation engines

“…By contrast, performance was poor for S. cerevisiae , suggesting that additional inputs besides expression data are needed to accurately reconstruct transcriptional networks for eukaryotes 43,45 . As rich data sets (such as epigenetic and chromatin conformation data) are becoming available for human cell types and tissues, integrative methods are being developed to reconstruct fine-grained regulatory circuits connecting TFs, enhancers, promoters and genes 49 . Consequently, there will be a need for novel benchmarks and Challenges to rigorously assess these methods on human regulatory circuits.…”