Integrative networks illuminate biological factors underlying gene-disease associations
Integrative networks combine multiple layers of biological data into a model of how genes work together to carry out cellular processes. Such networks become more valuable as they become more context specific, for example, by capturing how genes work together in a certain tissue or cell type. We discuss the applications of these networks to the study of human disease. Once constructed, these networks provide the means to identify broad biological patterns underlying genes associated with complex traits and diseases. We cover the different types of integrative networks that currently exist and how such networks that encompass multiple biological layers are constructed. We highlight how specificity can be incorporated into the reconstruction of different types of biomolecular interactions between genes, using tissue-specificity as a motivating example. We discuss examples of cases where networks have been applied to study human diseases and opportunities for new applications. Integrative networks with specificity to tissue or other biological features provide new capabilities to researchers engaged in the study of human disease. We expect improved data and algorithms to continue to improve such networks, allowing them to provide more detailed and mechanistic predictions into the context-specific genetic etiology of common diseases
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