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dc.contributor.advisorCarlson, Chris Sen_US
dc.contributor.authorRosse, Stephanie Annen_US
dc.date.accessioned2014-02-24T18:29:24Z
dc.date.issued2014-02-24
dc.date.submitted2013en_US
dc.identifier.otherRosse_washington_0250E_12561.pdfen_US
dc.identifier.urihttp://hdl.handle.net/1773/25157
dc.descriptionThesis (Ph.D.)--University of Washington, 2013en_US
dc.description.abstractConsiderable effort has been devoted to uncovering genetic variation that can be used to tailor prevention and treatment strategies for common diseases. Genome-wide association studies (GWAS) have proven to be an extremely powerful approach to identify susceptibility loci for common diseases and other complex traits. However, the profusion of GWAS results over the last eight years has not been accompanied by rapid translation into clinical benefit. In part, this unmet challenge stems from difficulty in interpreting the underlying biology of GWAS loci, and the unwieldiness of functional follow-up studies. Most GWAS signals are localized outside of protein coding regions, implicating transcriptional regulation as an important mechanism for susceptibility of complex disease. Unfortunately, the tools developed to predict and test perturbations to amino acid changes are inadequate for studying regulatory mechanisms. Therefore, interpreting the functional mechanisms underlying GWAS signals will likely be one of the most pressing challenges in the translation of genetic information into clinical application. Each chapter of this dissertation will explore various methods to translate GWAS associations into clinically actionable information. The first chapter develops a statistical and bioinformatics framework to fine-map known loci associated with fasting glucose levels that could be used to study a wide range of GWAS loci. This study leverages the differences in genomic architecture between populations to identify likely functional variants within known signals identified in a single population. Chapter two introduces a novel high-throughput <italic>in vitro</italic> assay to test variants previously prioritized for laboratory analysis. The functional interpretation of particular genetic variants will, in many circumstances, require recruitment of participants possessing particular genetic variants of interest. For that reason, chapter 3 examines the feasibility of applying current ethical recommendations for recruiting genetic research participants based on their individual research results. Specifically, a content analysis of current consent documents retrieved from the database of Genotypes and Phenotypes (dbGaP) was conducted to evaluate the consistency of consent documentation with ethical recommendations for re-contacting participants to invite their enrollment into a new study based on their genetic research results.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoen_USen_US
dc.rightsCopyright is held by the individual authors.en_US
dc.subjectbioinformatics; ENCODE; genetic epidemiology; GWAS; research recruitment; transethnicen_US
dc.subject.otherGeneticsen_US
dc.subject.otherEpidemiologyen_US
dc.subject.otherEthicsen_US
dc.subject.otherpublic health geneticsen_US
dc.titleThe post-GWAS era: paving the way from association to functional insighten_US
dc.typeThesisen_US
dc.embargo.termsDelay release for 2 years -- then make Open Accessen_US
dc.embargo.lift2016-02-14T18:29:24Z


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