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

• Main Author: Kim, Samuel Sungil.
• Other Authors: Alkes L. Price.
• Format: Others
• Language: English
• Published: Massachusetts Institute of Technology 2019
• Subjects: Electrical Engineering and Computer Science.
• Online Access: https://hdl.handle.net/1721.1/122548

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). === 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. === by Samuel Sungil Kim. === S.M. === S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science