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Type of Document Dissertation Author Aylor, David Lawrence, URN etd-03202008-165813 Title Not Just Another Trait: Methods for the Genetic Analysis of Gene Expression Degree PhD Graduate Program Bioinformatics Advisory Committee
Advisor Name Title Zhao-Bang Zeng Committee Chair Ignazio Carbone Committee Member Jeffrey Thorne Committee Member Philip Awadalla Committee Member Keywords
- quantitative genetics
- gene expression
- eQTL
- epistasis
Date of Defense 2008-03-19 Availability unrestricted Abstract ABSTRACT
AYLOR, DAVID LAWRENCE. Not Just Another Trait: Methods for the Genetic Analysis of Gene Expression. (Under the direction of Zhao-Bang Zeng.)
Gene expression refers to the process by which DNA is transcribed to mRNA. It is now possible to measure genome-wide transcript abundance in many genetically distinct individuals. Genetical genomics refers to the application of quantitative genetic techniques to such data. We present two analyses of gene expression is distinct experimental populations.
We first present a method for a classical epistasis analysis that includes gene expression measurements. We propose a framework for estimating and interpreting epistasis that borrows from both classical and quantitative approaches. Regression analysis estimates the effects of gene deletions as well as interactions and significant effects are selected such that a reduced model describes each expression trait. We then show how the resulting models correspond to specific hierarchical relationships between two regulator genes and a target gene. These hierarchical relationships are the building blocks of systems diagrams and genetic pathways. Our framework can serve as a foundation for future epistasis analyses based on genomic data.
Secondly, we analyze expression quantitative trait locus mapping (eQTL) results in a segregating yeast population. We use prior information about yeast pathways to group expression measurements and ask questions about pathway regulation. We find that while many genes share quantitative trait loci, sharing is not prevalent within pathway groups. We propose a model that explains our observations and how they fit in with previous interpretations of these data.
Lastly, we present a tool for manipulating sequence data within a population. Our software enables the user to pull out important features from a multiple alignment such as variable sites, unique haplotypes, and insertions or deletions. The output is compatible with a number of existing tools for population genetic analysis.
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