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Leveraging biological pathways and gene networks to understand the genetic architecture of diseases and complex traits

Author(s)
Kim, Samuel Sungil.
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Other Contributors
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Alkes L. Price.
Terms of use
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582
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Abstract
Recent studies have highlighted the role of biological pathways and gene networks in disease biology. In this thesis, we formally assess (1) the contribution of disease-associated gene pathways to disease heritability, (2) the contribution of genes with high network connectivity in known gene networks to disease heritability, and (3) the contribution of genes with high network connectivity to disease-associated gene pathways to disease heritability. We constructed a broad set of pathway, network, and pathway+network annotations and applied stratified LD score regression to 42 independent diseases and complex traits (average N=323K) to identify enriched annotations. We demonstrate gene network connectivity is highly informative for disease architectures, but the information in gene networks may be subsumed by regulatory annotations, such that accounting for known annotations is critical to robust inference of biological mechanisms.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
 
Cataloged from PDF version of thesis.
 
Includes bibliographical references (pages 73-82).
 
Date issued
2019
URI
https://hdl.handle.net/1721.1/122548
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.

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