Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.661639
Title: Modelling dependencies in genetic-marker data and its application to haplotype analysis
Author: Schouten, M. T.
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2008
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Abstract:
The objective of this thesis is to develop new methods to reconstruct haplotypes from phase-unknown genotypes. The need for new methodologies is motivated by the increasing availability of high-resolution marker data for many species. Such markers typically exhibit Linkage Disequilibrium (LD). It is believed that reconstructed haplotypes for markers in high LD can be valuable for a variety of application areas in population genetics, including reconstructing population history and identifying genetic disease variants. The thesis begins with a critical assessment of the limitations of existing methods, and then presents a unified statistical framework that can accommodate pedigree data, unrelated individuals and tightly linked markers. The framework makes use of graphical models, where inference entails representing the relevant joint probability distribution as a graph and the using associated algorithms to facilitate computation. The graphical model formalism provides invaluable tools to facilitate model specification, visualization, and inference. Once the unified framework is developed, a broad range of simulation studies are conducted using previously published haplotype data. Important contributions include demonstrating the different ways in which the haplotype frequency distribution can impact the accuracy of both the phase assignments and haplotype frequency estimates; evaluating the effectiveness of using family data to improve accuracy for different frequency profiles; and, assessing the dangers of treating related individuals as unrelated in an association study.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.661639  DOI: Not available
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