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.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Specialized Center (P50)
Project #
5P50HG004233-07
Application #
8735017
Study Section
Ethical, Legal, Social Implications Review Committee (GNOM)
Program Officer
Feingold, Elise A
Project Start
2007-06-20
Project End
2018-08-31
Budget Start
2014-09-01
Budget End
2015-08-31
Support Year
7
Fiscal Year
2014
Total Cost
$3,011,271
Indirect Cost
$878,220
Name
Dana-Farber Cancer Institute
Department
Type
DUNS #
076580745
City
Boston
State
MA
Country
United States
Zip Code
02215
Labunskyy, Vyacheslav M; Suzuki, Yo; Hanly, Timothy J et al. (2014) The insertion Green Monster (iGM) method for expression of multiple exogenous genes in yeast. G3 (Bethesda) 4:1183-91
Corominas, Roser; Yang, Xinping; Lin, Guan Ning et al. (2014) Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism. Nat Commun 5:3650
Yilancioglu, Kaan; Weinstein, Zohar B; Meydan, Cem et al. (2014) Target-independent prediction of drug synergies using only drug lipophilicity. J Chem Inf Model 54:2286-93
Wang, W; Zhong, Q; Teng, L et al. (2014) Mutations that disrupt PHOXB interaction with the neuronal calcium sensor HPCAL1 impede cellular differentiation in neuroblastoma. Oncogene 33:3316-24
Musso, Gabriel; Tasan, Murat; Mosimann, Christian et al. (2014) Novel cardiovascular gene functions revealed via systematic phenotype prediction in zebrafish. Development 141:224-35
Shen, Hua-Wei; Barabási, Albert-László (2014) Collective credit allocation in science. Proc Natl Acad Sci U S A 111:12325-30
Rolland, Thomas; Ta?an, Murat; Charloteaux, Benoit et al. (2014) A proteome-scale map of the human interactome network. Cell 159:1212-26
Cokol, Murat; Weinstein, Zohar B; Yilancioglu, Kaan et al. (2014) Large-scale identification and analysis of suppressive drug interactions. Chem Biol 21:541-51
Sharma, Amitabh; Gulbahce, Natali; Pevzner, Samuel J et al. (2013) Network-based analysis of genome wide association data provides novel candidate genes for lipid and lipoprotein traits. Mol Cell Proteomics 12:3398-408
Mehra, Alka; Zahra, Aleena; Thompson, Victor et al. (2013) Mycobacterium tuberculosis type VII secreted effector EsxH targets host ESCRT to impair trafficking. PLoS Pathog 9:e1003734

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