The overall goal of our Center of Excellence in Genome Sciences (CEGS) is to functionally characterize human genetic variants affecting protein-coding genes that are associated with disorders ranging from simple Mendelian diseases to complex traits, which are much harder to model and predict from a cellular network point-of-view. We will develop concepts, technologies and systematic datasets to functionally characterize large numbers of genotypes in terms of the effects they have on the molecular functions and physical and biochemical interactions mediated by the corresponding gene products, with implications for elucidating human gene-gene interactions and understanding heritability and improving genomic medicine. To achieve our goal, we will i) gather existing information, experimentally map, model and predict networks of human macromolecular interactions for selected inherited disorders (""""""""reference edgotypes""""""""), ii) systematically reveal protein interaction maps modified by allelic perturbation (""""""""allelic edgotypes"""""""") to uncover disease subtypes and mechanisms, and iii) model molecular paths that likely explain gene-gene interactions involved in these disorders, and use these models to empower the detection of allele combinations that combine non-additively to predict disease risk. The high-risk/high-reward aspects of this CEGS will be: i) prioritizing disease gene candidates emerging from GWA studies, and mutation sequencing, ii) solving the """"""""missing heritability problem"""""""" in complex traits, and iii) demonstrating how edgotyping will help us to better predict disease outcomes. In summary, our specific aims are to: i) Generate deep, robust interactome networks for selected disease modules ('reference edgotypes'), ii) Generate edgotypic maps of perturbed physical and biochemical interactions amongst gene products implicated in the corresponding human disorders ('allelic edgotypes'), iii) Exploit edgotyping data to identify new disease subtypes, and to empower discovery of allele combinations that non-additively predict disease risk, iv) Establish an inter-disciplinary collaborative environment with human genetics laboratories specialized in one or a few human genetic diseases in the context of the """"""""Edgotyping Initiative"""""""", v) Provide a training and outreach program centered on novel experimental and computational methods.

Public Health Relevance

New information that will emerge from the proposed work should lead to a better understanding of the molecular mechanisms involved in human disease. We propose to improve our understanding of human disease by analyzing how disease-causing mutations relate to network perturbations. An important outcome of this work will be methods and approaches that can better established causality associated with mutations in disease genes and mechanistic insights that can better direct therapeutic intervention.

National Institute of Health (NIH)
National Human Genome Research Institute (NHGRI)
Specialized Center (P50)
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Ethical, Legal, Social Implications Review Committee (GNOM)
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Feingold, Elise A
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Dana-Farber Cancer Institute
United States
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Santolini, Marc; Barabási, Albert-László (2018) Predicting perturbation patterns from the topology of biological networks. Proc Natl Acad Sci U S A 115:E6375-E6383
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