Present-day biological research has generated a vast body of data related to variation in the human genome, but in many cases the biological role of this variation is unknown or only partially understood. In order to integrate the diverse body of experimental genetic and genomic data, systems biologists pioneered computational approaches to infer functional networks. These networks provide a powerful platform to investigate genomic findings at a functional level. Recently, systems biologists designed a second generation of functional networks that reflect tissue-specificity in gene functional interactions. We examine both characteristics of these tissue-specific functional networks and the topology of genome-wide association study (GWAS) variant-related genes in these networks. We find significant variation in network quality and suggest metrics to identify well-performing networks. Finally, we show GWAS trait-associated genes have non-random topology in tissue-specific networks and that this must be taken into account when applying network-enabled methods to genomic data.
University of Minnesota M.S. thesis. January 2016. Major: Biomedical Informatics and Computational Biology. Advisors: Chad Myers, Heather Nelson. 1 computer file (PDF); vii, 61 pages.
Characterization of Tissue-Specific Functional Networks and Genome-Wide Association Study Genes.
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