Show simple item record

dc.contributor.advisorDaly, Mark Joseph
dc.contributor.advisorHirschhorn, Joel Naom
dc.contributor.authorRossin, Elizabeth Jeffries
dc.date.accessioned2012-11-15T15:59:42Z
dash.embargo.terms2014-10-05en_US
dash.embargo.terms2014-10-05
dc.date.issued2012-11-15
dc.date.submitted2012
dc.identifier.citationRossin, Elizabeth Jeffries. 2012. The Proteomic Landscape of Human Disease: Construction and Evaluation of Networks Associated to Complex Traits. Doctoral dissertation, Harvard University.en_US
dc.identifier.otherhttp://dissertations.umi.com/gsas.harvard:10514en
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:9909632
dc.description.abstractGenetic mapping of complex traits has been successful over the last decade, with over 2,000 regions in the genome associated to disease. Yet, the translation of these findings into a better understanding of disease biology is not straightforward. The true promise of human genetics lies in its ability to explain disease etiology, and the need to translate genetic findings into a better understanding of biological processes is of great relevance to the community. We hypothesized that integrating genetics and protein- protein interaction (PPI) networks would shed light on the relationship among genes associated to complex traits, ultimately to help guide understanding of disease biology. First, we discuss the design, testing and implementation of a novel in silico approach (“DAPPLE”) to rigorously ask whether loci associated to complex traits code for proteins that form significantly connected networks. Using a high-confidence set of publically available physical interactions, we show that loci associated to autoimmune diseases code for proteins that assemble into significantly connected networks and that these networks are predictive of new genetic variants associated to the phenotypes in question. Next, we study variation in the electrocardiographic QT-interval, a heritable phenotype that when prolonged is a risk factor for cardiac arrhythmia and sudden cardiac death. We show that a large proportion of QT-associated loci encode proteins that are members of complexes identified by immunoprecipitations in mouse cardiac tissue of proteins known to be causal of Mendelian long-QT syndrome. For several of the identified proteins, we show they affect cardiac ion channel currents in model organisms. Using replication genotyping in 17,500 individuals, we use the complexes to identify genome-wide significant loci that would have otherwise been missed. Finally, we consider whether PPIs can be used to interpret rare and de novo variation discovered through recent technological advances in exome-sequencing. We report a highly connected network underlying de novo variants discovered in an autism trio exome-sequencing effort, and we design, test and implement a novel statistical framework (“DAPPLE/SEQ”) to analyze rare inherited variants in the context of PPIs in a way that significantly boosts power to detect association.en_US
dc.language.isoen_USen_US
dash.licenseLAA
dc.subjectGWASen_US
dc.subjectnetworken_US
dc.subjectprotein-protein interactionen_US
dc.subjectproteomicsen_US
dc.subjectsequencingen_US
dc.subjectgeneticsen_US
dc.titleThe Proteomic Landscape of Human Disease: Construction and Evaluation of Networks Associated to Complex Traitsen_US
dc.typeThesis or Dissertationen_US
dash.depositing.authorRossin, Elizabeth Jeffries
dc.date.available2014-10-06T07:30:37Z
thesis.degree.date2012en_US
thesis.degree.disciplineBiological and Biomedical Sciencesen_US
thesis.degree.grantorHarvard Universityen_US
thesis.degree.leveldoctoralen_US
thesis.degree.namePh.D.en_US
dc.contributor.committeeMemberRegev, Aviven_US
dc.contributor.committeeMemberStranger, Barbaraen_US
dc.contributor.committeeMemberKraft, Peteren_US
dash.contributor.affiliatedRossin, Elizabeth


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record