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Title: Data mining and statistical techniques applied to genetic epidemiology
Author: Li, Qiao
ISNI:       0000 0004 2700 3201
Awarding Body: University of East Anglia
Current Institution: University of East Anglia
Date of Award: 2010
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Genetic epidemiology is the study of the joint action of genes and environmental factors in determining the phenotypes of diseases. The twin study is a classic and important epidemiological tool, which can help to separate the underlying effects of genes and environment on phenotypes. Twin data have been widely examined using traditional methods to genetic epidemiological research. However, they provide a rich sources information related to many complex phenotypes that has the potential to be further explored and exploited. This thesis focuses on two major genetic epidemiological approaches: familial aggregation analysis and linkage analysis, using twin data from TwinsUK Registry. Structural equation modelling (SEM) is a conventional method used in familial aggregation analysis, and is applied in this research to discover the underlying genetic and environmental influences on two complex phenotypes: coping strategies and osteoarthritis. However, SEM is a confirmatory method and relies on prior biomedical hypotheses. A new exploratory method, named MDS-C, combining multidimensional scaling and clustering method is developed in this thesis. It does not rely on using prior hypothetical models and is applied to uncover underlying genetic determinants of bone mineral density (BMD). The results suggest that the genetic influence on BMD is site-specific. Haseman-Elston (H-E) regression is a conventional linkage analysis approach using the identity by descent (IBD) information between twins to detect quantitative trait loci (QTLs) which regulate the quantitative phenotype. However, it only considers the genetic effect from individual loci. Two new approaches including a pair-wise H-E regression (PWH-E) and a feature screening approach (FSA) are proposed in this research to detect QTLs allowing gene-gene interaction. Simulation studies demonstrate that PWH-E and FSA have greater power to detect QTLs with interactions. Application to real-world BMD data results in identifying a set of potential QTLs, including 7 chromosomal loci consistent with previous genome-wide studies.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available