Toward Robust Group-Wise eQTL Mapping via Integrating Multi-Domain Heterogeneous Data Public Deposited
- Last Modified
- March 19, 2019
- Creator
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Cheng, Wei
- Affiliation: College of Arts and Sciences, Department of Computer Science
- Abstract
- As a promising tool for dissecting the genetic basis of common diseases, expression quantitative trait loci (eQTL) study has attracted increasing research interest. Traditional eQTL methods focus on testing the associations between individual single-nucleotide polymorphisms (SNPs) and gene expression traits. A major drawback of this approach is that it cannot model the joint effect of a set of SNPs on a set of genes, which may correspond to biological pathways. This thesis studies the problem of identifying group-wise associations in eQTL mapping. Based on the intuition of group-wise association, we examine how the integration of heterogeneous prior knowledge on the correlation structures between SNPs, and between genes can improve the robustness and the interpretability of eQTL mapping. To obtain a more accurate knowledgebase on the interactions among SNPs and genes, we developed a robust and flexible approach that can incorporate multiple data sources and automatically identify noisy sources. Extensive experiments demonstrate the effectiveness of the proposed algorithms.
- Date of publication
- December 2015
- Keyword
- Subject
- DOI
- Identifier
- Resource type
- Rights statement
- In Copyright
- Advisor
- Niethammer, Marc
- Zhang, Xiang
- Sullivan, Patrick
- McMillan, Leonard
- Wang, Wei
- Sun, Wei
- Degree
- Doctor of Philosophy
- Degree granting institution
- University of North Carolina at Chapel Hill Graduate School
- Graduation year
- 2015
- Language
- Publisher
- Place of publication
- Chapel Hill, NC
- Access
- There are no restrictions to this item.
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This work has no parents.
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