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

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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.

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Most cited references 26

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Variations in DNA elucidate molecular networks that cause disease.

Yanqing Chen, Jun Zhu, Pek Yee Lum … (2008)

Identifying variations in DNA that increase susceptibility to disease is one of the primary aims of genetic studies using a forward genetics approach. However, identification of disease-susceptibility genes by means of such studies provides limited functional information on how genes lead to disease. In fact, in most cases there is an absence of functional information altogether, preventing a definitive identification of the susceptibility gene or genes. Here we develop an alternative to the classic forward genetics approach for dissecting complex disease traits where, instead of identifying susceptibility genes directly affected by variations in DNA, we identify gene networks that are perturbed by susceptibility loci and that in turn lead to disease. Application of this method to liver and adipose gene expression data generated from a segregating mouse population results in the identification of a macrophage-enriched network supported as having a causal relationship with disease traits associated with metabolic syndrome. Three genes in this network, lipoprotein lipase (Lpl), lactamase beta (Lactb) and protein phosphatase 1-like (Ppm1l), are validated as previously unknown obesity genes, strengthening the association between this network and metabolic disease traits. Our analysis provides direct experimental support that complex traits such as obesity are emergent properties of molecular networks that are modulated by complex genetic loci and environmental factors.

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A human phenome-interactome network of protein complexes implicated in genetic disorders.

Kasper Lage, E Olof Karlberg, Zenia Størling … (2007)

We performed a systematic, large-scale analysis of human protein complexes comprising gene products implicated in many different categories of human disease to create a phenome-interactome network. This was done by integrating quality-controlled interactions of human proteins with a validated, computationally derived phenotype similarity score, permitting identification of previously unknown complexes likely to be associated with disease. Using a phenomic ranking of protein complexes linked to human disease, we developed a Bayesian predictor that in 298 of 669 linkage intervals correctly ranks the known disease-causing protein as the top candidate, and in 870 intervals with no identified disease-causing gene, provides novel candidates implicated in disorders such as retinitis pigmentosa, epithelial ovarian cancer, inflammatory bowel disease, amyotrophic lateral sclerosis, Alzheimer disease, type 2 diabetes and coronary heart disease. Our publicly available draft of protein complexes associated with pathology comprises 506 complexes, which reveal functional relationships between disease-promoting genes that will inform future experimentation.

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Prioritizing candidate disease genes by network-based boosting of genome-wide association data.

Insuk Lee, U Martin Blom, Peggy Wang … (2011)

Network "guilt by association" (GBA) is a proven approach for identifying novel disease genes based on the observation that similar mutational phenotypes arise from functionally related genes. In principle, this approach could account even for nonadditive genetic interactions, which underlie the synergistic combinations of mutations often linked to complex diseases. Here, we analyze a large-scale, human gene functional interaction network (dubbed HumanNet). We show that candidate disease genes can be effectively identified by GBA in cross-validated tests using label propagation algorithms related to Google's PageRank. However, GBA has been shown to work poorly in genome-wide association studies (GWAS), where many genes are somewhat implicated, but few are known with very high certainty. Here, we resolve this by explicitly modeling the uncertainty of the associations and incorporating the uncertainty for the seed set into the GBA framework. We observe a significant boost in the power to detect validated candidate genes for Crohn's disease and type 2 diabetes by comparing our predictions to results from follow-up meta-analyses, with incorporation of the network serving to highlight the JAK-STAT pathway and associated adaptors GRB2/SHC1 in Crohn's disease and BACH2 in type 2 diabetes. Consideration of the network during GWAS thus conveys some of the benefits of enrolling more participants in the GWAS study. More generally, we demonstrate that a functional network of human genes provides a valuable statistical framework for prioritizing candidate disease genes, both for candidate gene-based and GWAS-based studies.

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Author and article information

Journal

Journal ID (nlm-journal-id): 101215604

Journal ID (pubmed-jr-id): 32338

Journal ID (nlm-ta): Nat Methods

Journal ID (iso-abbrev): Nat. Methods

Title: Nature methods

ISSN (Print): 1548-7091

ISSN (Electronic): 1548-7105

Publication date Nihms-submitted: 15 July 2016

Publication date (Electronic): 07 March 2016

Publication date (Print): April 2016

Publication date PMC-release: 01 October 2016

Volume: 13

Issue: 4

Pages: 366-370

Affiliations

[1 ]Department of Medical Genetics, University of Lausanne, Switzerland

[2 ]Swiss Institute of Bioinformatics, Lausanne, Switzerland

[3 ]Broad Institute, MIT, Cambridge, MA, USA

[4 ]Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA

[5 ]Institute of Social and Preventive Medicine, University Hospital of Lausanne, Switzerland

Author notes

Correspondence should be addressed to D.M. ( daniel.marbach@ 123456unil.ch )

Article

Accession ID: PMC4967716 Pmcid ID: PMC4967716 Pmc-uid ID: 4967716 Manuscript ID: nihpa802956

DOI: 10.1038/nmeth.3799

PMC ID: 4967716

PubMed ID: 26950747

SO-VID: 03582535-2a22-4b13-a603-3f3c4129a54b

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