Leveraging biological pathways and gene networks to understand the genetic architecture of diseases and complex traits
Name
1122565145-MIT.pdf
Size
6.33 MB
Format
Adobe PDF
Checksum (MD5)
0aff24d1728654eef1aa0c75f9ef60f4
Author(s)
Kim, Samuel Sungil.
Advisor(s)
Alkes L. Price.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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